ORIGINAL_ARTICLE
Editorial
https://jbpe.sums.ac.ir/article_43289_3799042e06ba9d694d978a4b7f2335ad.pdf
2018-03-01
1
2
A R
Mehdizadeh
1
Editor in Chief, Department of Medical Physics, School of Medicine, Shiraz University of Medical Science, Shiraz, Iran
LEAD_AUTHOR
ORIGINAL_ARTICLE
Assessment of Neutron Contamination Originating from the Presence of Wedge and Block in Photon Beam Radiotherapy
Background: One of the main causes of induction of secondary cancer in radiation therapy is neutron contamination received by patients during treatment.Objective: In the present study the impact of wedge and block on neutron contamination production is investigated. The evaluations are conducted for a 15 MV Siemens Primus linear accelerator. Methods: Simulations were performed using MCNPX Monte Carlo code. 30˚, 45˚ and 60˚ wedges and a cerrobend block with dimensions of 1.5 × 1.5 × 7 cm3 were simulated. The investigation were performed in the 10 × 10 cm2 field size at source to surface distance of 100 cm for depth of 0.5, 2, 3 and 4 cm in a water phantom. Neutron dose was calculated using F4 tally with flux to dose conversion factors and F6 tally.Results: Results showed that the presence of wedge increases the neutron contamination when the wedge factor was considered. In addition, 45˚ wedge produced the most amount of neutron contamination. If the block is in the center of the field, the cerrobend block caused less neutron contamination than the open field due to absorption of neutrons and photon attenuation. The results showed that neutron contamination is less in steeper depths. The results for two tallies showed practically equivalent results.Conclusion: Wedge causes neutron contamination hence should be considered in therapeutic protocols in which wedge is used. In terms of clinical aspects, the results of this study show that superficial tissues such as skin will tolerate more neutron contamination than the deep tissues.
https://jbpe.sums.ac.ir/article_43287_ec407b294605de714c988fa1d77b0c4b.pdf
2018-03-01
3
12
Neutron Contamination
wedge
cerrobend block
Siemens Primus linac
Monte Carlo Simulation
M T
Bahreyni Toossi
mbahreyni@yahoo.co.uk
1
Medical Physics Department, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
AUTHOR
B
Khajetash
benyamin.khajetash@gmail.com
2
Medical Physics Department, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
LEAD_AUTHOR
M
Ghorbani
mhdghorbani@gmail.com
3
Medical Physics Department, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
AUTHOR
In: World Health Organization. IARC-International Agency for Research on Cancer. [2014]. Available from: http://www.who.int/ionizing_radiation/research/iarc/en/.
1
Price P, Sikora K. Treatment of Cancer, Sixth Edition. London: CRC Press; 2014.
2
Exposito MR, Sanchez-Nieto B, Terron JA, Domingo C, Gomez F, Sanchez-Doblado F. Neutron contamination in radiotherapy: estimation of second cancers based on measurements in 1377 patients. Radiother Oncol. 2013;107:234-41. doi.org/10.1016/j.radonc.2013.03.011. PubMed PMID: 23601351.
3
Protection ICoR. ICRP Publication 60: 1990 Recommendations of the International Commission on Radiological Protection:Amsterdam: Elsevier Health Sciences; 1991.
4
In: National Council on Radiation Protection Measurements. NCRP Report 79, Neutron contamination from medical electron accelerators. [1984]. Available from: http://ncrponline.org/publications/reports/ncrp-report-79/.
5
Hashemi S, Raisali G, Taheri M, Majdabadi A, Ghafoori M. The effect of external wedge on the photoneutron dose equivalent at a high energy medical linac. Nukleonika. 2011;56:49-51.
6
Hashemi SM, Hashemi-Malayeri B, Raisali G, Shokrani P, Sharafi AA, Torkzadeh F. Measurement of photoneutron dose produced by wedge filters of a high energy linac using polycarbonate films. J Radiat Res. 2008;49:279-83. doi.org/10.1269/jrr.07066. PubMed PMID: 18460824.
7
Biltekin F, Ozyigit G, Yeginer M, Celik D, Gurkaynak M. EP-1376 investigating the effect of wedge filter on neutron contamination by using bubble detectors. Radiotherapy and Oncology. 2012;103:S522. doi.org/10.1016/S0167-8140(12)71709-7.
8
Hashemi SM, Hashemi-Malayeri B, Raisali G, Shokrani P, Sharafi AA, Jafarizadeh M. The effect of field modifier blocks on the fast photoneutron dose equivalent from two high-energy medical linear accelerators. Radiat Prot Dosimetry. 2008;128:359-62. doi.org/10.1093/rpd/ncm421. PubMed PMID: 17875628.
9
Mesbahi A. A Monte Carlo study on neutron and electron contamination of an unflattened 18-MV photon beam. Appl Radiat Isot. 2009;67:55-60. doi.org/10.1016/j.apradiso.2008.07.013. PubMed PMID: 18760613.
10
Bahreyni Toossi MT, Behmadi M, Ghorbani M, Gholamhosseinian H. A Monte Carlo study on electron and neutron contamination caused by the presence of hip prosthesis in photon mode of a 15 MV Siemens PRIMUS linac. J Appl Clin Med Phys. 2013;14:52-67. PubMed PMID: 24036859.
11
Hussien M, Ma A, Spyrou N. Monte Carlo Simulations of the Effective Neutron Dose Received by a Male Anthropomorphic Voxel Phantom outside a Medical Linac Treatment Room. University of Surrey, Guildford, Surrey. 2006.
12
Zanini A, Durisi E, Fasolo F, Ongaro C, Visca L, Nastasi U, et al. Monte Carlo simulation of the photoneutron field in linac radiotherapy treatments with different collimation systems. Phys Med Biol. 2004;49:571-82. doi.org/10.1088/0031-9155/49/4/008. PubMed PMID: 15005166.
13
Hashemi SM, Hashemi-Malayeri B, Raisali G, Shokrani P, Sharafi AA, Torkzadeh F. Measurement of photoneutron dose produced by wedge filters of a high energy linac using polycarbonate films. J Radiat Res. 2008;49:279-83. doi.org/10.1269/jrr.07066. PubMed PMID: 18460824.
14
Zabihinpoor S, Hasheminia M. Calculation of Neutron Contamination from Medical Linear Accelerator in Treatment Room. Adv Studies Theor Phys. 2011;5:421-8.
15
Pelowitz D. MCNPX user’s manual, version 2.6. 0, LA-CP-07-1473. . New Mexico: Los Alamos National Laboratory; 2008.
16
Schneider U. Modeling the risk of secondary malignancies after radiotherapy. Genes (Basel). 2011;2:1033-49. doi.org/10.3390/genes2041033. PubMed PMID: 24710304. PubMed PMCID: 3927608.
17
ORIGINAL_ARTICLE
A Monte Carlo Study on Dose Enhancement by Homogeneous and Inhomogeneous Distributions of Gold Nanoparticles in Radiotherapy with Low Energy X-rays
Background: To enhance the dose to tumor, the use of high atomic number elements has been proposed.Objective: The aim of this study is to investigate the effect of gold nanoparticle distribution on dose enhancement in tumor when the tumor is irradiated by typical monoenergetic X-ray beams by considering homogeneous and inhomogeneous distributions of gold nanoparticles (GNPs) in the tumor.Methods: MCNP-4C Monte Carlo code was utilized for the simulation of a source, a phantom containing tumor and gold nanoparticles with concentrations of 10, 30 and 70 mg Au/g tumor. A 15 cm×15 cm×15 cm cubic water phantom was irradiated with a small planar source with four monoenergetic X-ray beams of 35, 55, 75 and 95 keV energy. Furthermore, tumor depths of 2.5 cm, 4.5 cm and 6.5 cm with homogeneous and inhomogeneous distributions of nanoparticles were studied. Each concentration, photon energy, tumor depth and type of distribution was evaluated in a separate simulation.Results: Results have shown that dose enhancement factor (DEF) in tumor increases approximately linearly with the concentration of gold nanoparticles. While DEF has fluctuations with photon energy, 55 keV photons have the highest DEF values compared to other energies. While DEF has relatively the same values with tumor located at various depths, inhomogeneous distribution of GNP has shown different results compared with the homogeneous model. Dose enhancement can be expected with relatively deep seated tumors in radiotherapy with low energy X-rays. Inhomogeneous model is recommended for the purpose of dose enhancement study because it mimics the real distribution of GNPs in tumor.
https://jbpe.sums.ac.ir/article_43294_3f3d89103710da4830240882b65de5ac.pdf
2018-03-01
13
28
Gold Nanoparticle
Dose Enhancement
Homogeneous Distribution
Inhomogeneous Distribution
Monte Carlo Simulation
M
Zabihzadeh
manzabih@gmail.com
1
Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
AUTHOR
T
Moshirian
t.moshirian@gmail.com
2
Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
LEAD_AUTHOR
M
Ghorbani
mhdghorbani@gmail.com
3
Biomedical Engineering and Medical Physics Department, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
AUTHOR
C
Knaup
courtney.knaup@usoncology.com
4
Comprehensive Cancer Centers of Nevada, Las Vegas, Nevada, USA
AUTHOR
M A
Behrooz
behrooz_m@aums.ac.ir
5
Department of Medical Physics, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
AUTHOR
Hainfeld JF, Dilmanian FA, Slatkin DN, Smilowitz HM. Radiotherapy enhancement with gold nanoparticles. J Pharm Pharmacol. 2008;60:977-85. doi.org/10.1211/jpp.60.8.0005. PubMed PMID: 18644191.
1
McMahon SJ, Hyland WB, Muir MF, Coulter JA, Jain S, Butterworth KT, et al. Biological consequences of nanoscale energy deposition near irradiated heavy atom nanoparticles. Sci Rep. 2011;1:18. doi.org/10.1038/srep00018. PubMed PMID: 22355537. PubMed PMCID: 3216506.
2
Hainfeld JF, Slatkin DN, Smilowitz HM. The use of gold nanoparticles to enhance radiotherapy in mice. Phys Med Biol. 2004;49:N309-15. doi.org/10.1088/0031-9155/49/18/N03. PubMed PMID: 15509078.
3
Chithrani BD, Ghazani AA, Chan WC. Determining the size and shape dependence of gold nanoparticle uptake into mammalian cells. Nano Lett. 2006;6:662-8. doi.org/10.1021/nl052396o. PubMed PMID: 16608261.
4
Cho SH. Estimation of tumour dose enhancement due to gold nanoparticles during typical radiation treatments: a preliminary Monte Carlo study. Phys Med Biol. 2005;50:N163-73. doi.org/10.1088/0031-9155/50/15/N01. PubMed PMID: 16030374.
5
Robar JL. Generation and modelling of megavoltage photon beams for contrast-enhanced radiation therapy. Phys Med Biol. 2006;51:5487-504. doi.org/10.1088/0031-9155/51/21/007. PubMed PMID: 17047265.
6
Chithrani BD, Chan WC. Elucidating the mechanism of cellular uptake and removal of protein-coated gold nanoparticles of different sizes and shapes. Nano Lett. 2007;7:1542-50. doi.org/10.1021/nl070363y. PubMed PMID: 17465586.
7
Chithrani BD, Stewart J, Allen C, Jaffray DA. Intracellular uptake, transport, and processing of nanostructures in cancer cells. Nanomedicine. 2009;5:118-27. doi.org/10.1016/j.nano.2009.01.008. PubMed PMID: 19480047.
8
Hainfeld JF, Dilmanian FA, Zhong Z, Slatkin DN, Kalef-Ezra JA, Smilowitz HM. Gold nanoparticles enhance the radiation therapy of a murine squamous cell carcinoma. Phys Med Biol. 2010;55:3045-59. doi.org/10.1088/0031-9155/55/11/004. PubMed PMID: 20463371.
9
Zhang SX, Gao J, Buchholz TA, Wang Z, Salehpour MR, Drezek RA, et al. Quantifying tumor-selective radiation dose enhancements using gold nanoparticles: a monte carlo simulation study. Biomed Microdevices. 2009;11:925-33. doi.org/10.1007/s10544-009-9309-5. PubMed PMID: 19381816.
10
Jones BL, Krishnan S, Cho SH. Estimation of microscopic dose enhancement factor around gold nanoparticles by Monte Carlo calculations. Med Phys. 2010;37:3809-16. doi.org/10.1118/1.3455703. PubMed PMID: 20831089.
11
Lechtman E, Chattopadhyay N, Cai Z, Mashouf S, Reilly R, Pignol JP. Implications on clinical scenario of gold nanoparticle radiosensitization in regards to photon energy, nanoparticle size, concentration and location. Phys Med Biol. 2011;56:4631-47. doi.org/10.1088/0031-9155/56/15/001. PubMed PMID: 21734337.
12
Leung MK, Chow JC, Chithrani BD, Lee MJ, Oms B, Jaffray DA. Irradiation of gold nanoparticles by x-rays: Monte Carlo simulation of dose enhancements and the spatial properties of the secondary electrons production. Med Phys. 2011;38:624-31. doi.org/10.1118/1.3539623. PubMed PMID: 21452700.
13
Cai Y, Xu S, Wu J, Long Q. Coupled modelling of tumour angiogenesis, tumour growth and blood perfusion. J Theor Biol. 2011;279:90-101. doi.org/10.1016/j.jtbi.2011.02.017. PubMed PMID: 21392511.
14
Lesart AC, van der Sanden B, Hamard L, Esteve F, Stephanou A. On the importance of the submicrovascular network in a computational model of tumour growth. Microvasc Res. 2012;84:188-204. doi.org/10.1016/j.mvr.2012.06.001. PubMed PMID: 22705361.
15
Briesmeister JF. MCNPTM-A general Monte Carlo N-particle transport code. Version 4C, LA-13709-M, New Mexico: Los Alamos National Laboratory; 2000.
16
ICRU I. Tissue Substitutes in Radiation Dosimetry and Measurement. International Commission on Radiation Units and Measurements. 1989.
17
Welter M, Rieger H. Blood Vessel Network Remodeling During Tumor Growth. Modeling Tumor Vasculature: Springer; 2012. p. 335-60.
18
Ranjbar H, Shamsaei M, Ghasemi MR. Investigation of the dose enhancement factor of high intensity low mono-energetic X-ray radiation with labeled tissues by gold nanoparticles. Nukleonika. 2010;55:307-12.
19
ORIGINAL_ARTICLE
Evaluation of the Effect of Source Geometry on the Output of Miniature X-ray Tube for Electronic Brachytherapy through Simulation
Objective: The use of miniature X-ray source in electronic brachytherapy is on the rise so there is an urgent need to acquire more knowledge on X-ray spectrum production and distribution by a dose. The aim of this research was to investigate the influence of target thickness and geometry at the source of miniature X-ray tube on tube output.Method: Five sources were simulated based on problems each with a specific geometric structure and conditions using MCNPX code. Tallies proportional to the output were used to calculate the results for the influence of source geometry on output.Results: The results of this work include the size of the optimal thickness of 5 miniature sources, energy spectrum of the sources per 50 kev and also the axial and transverse dose of simulated sources were calculated based on these thicknesses. The miniature source geometric was affected on the output x-ray tube.Conclusion: The result of this study demonstrates that hemispherical-conical, hemispherical and truncated-conical miniature sources were determined as the most suitable tools.
https://jbpe.sums.ac.ir/article_43295_755c39a1fcf5ad3aed7c7deb9e850a13.pdf
2018-03-01
29
42
10.31661/jbpe.v8i1Mar.697
Monte Carlo
Electronic Brachytherapy
Target Optimization
Energy Spectrum
Miniature Source
B
Barati
hojjatbarati1391@gmail.com
1
Department of Medical Physics, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
LEAD_AUTHOR
M
Zabihzadeh
manzabih@gmail.com
2
Department of Medical Physics, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
AUTHOR
M J
Tahmasebi Birgani
tahmasebi_mj@yahoo.com
3
Department of Medical Physics, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
AUTHOR
N
Chegini
chegenin@gamil.com
4
Department of Medical Physics, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
AUTHOR
J
Fatahiasl
fatahi.j49@gmail.com
5
Department of Medical Physics, School of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
AUTHOR
I
Mirr
imanmirr@yahoo.com
6
Department of Biostatistics and Epidemiology, School of Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
AUTHOR
Cho SO, Heo SH. Super miniature X-ray tube using NANO material field emitter: Google Patents; 2012.
1
Choe KS, Liauw SL. Radiotherapeutic strategies in the management of low-risk prostate cancer. ScientificWorldJournal. 2010;10:1854-69. doi.org/10.1100/tsw.2010.179. PubMed PMID: 20852828.
2
Porter AT, Blasko JC, Grimm PD, Reddy SM, Ragde H. Brachytherapy for prostate cancer. CA Cancer J Clin. 1995;45:165-78. doi.org/10.3322/canjclin.45.3.165. PubMed PMID: 7743420.
3
Kubo HD, Glasgow GP, Pethel TD, Thomadsen BR, Williamson JF. High dose-rate brachytherapy treatment delivery: report of the AAPM Radiation Therapy Committee Task Group No. 59. Med Phys. 1998;25:375-403. doi.org/10.1118/1.598232. PubMed PMID: 9571605.
4
Gierga DP, Shefer RE. Characterization of a soft X-ray source for intravascular radiation therapy. Int J Radiat Oncol Biol Phys. 2001;49:847-56. doi.org/10.1016/S0360-3016(00)01510-8. PubMed PMID: 11172969.
5
Heoa S, Haa J, Choa S. An Optimization of Super-Miniature X-ray Target. 2011.
6
Dinsmore M, Harte KJ, Sliski AP, Smith DO, Nomikos PM, Dalterio MJ, et al. A new miniature x-ray source for interstitial radiosurgery: device description. Med Phys. 1996;23:45-52. doi.org/10.1118/1.597790. PubMed PMID: 8700032.
7
Ihsan A, Heo SH, Kim HJ, Kang CM, Cho SO. An optimal design of X-ray target for uniform X-ray emission from an electronic brachytherapy system. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms. 2011;269:1053-7. doi.org/10.1016/j.nimb.2011.03.001.
8
Rivard MJ, Davis SD, DeWerd LA, Rusch TW, Axelrod S. Calculated and measured brachytherapy dosimetry parameters in water for the Xoft Axxent X-Ray Source: an electronic brachytherapy source. Med Phys. 2006;33:4020-32. doi.org/10.1118/1.2357021. PubMed PMID: 17153382.
9
Rivard MJ, Rusch TW, Axelrod S. Radiological dependence of electronic brachytherapy simulation on input parameters. Medical Physics. 2006:11747-4502.
10
Silvern D, Rusch T, Zaider M, editors Dosimetric Benefits of an Adjustable-Energy Electronic Brachytherapy Source.Medical Physics. 2004;31:1880.
11
Hiatt JR, Davis SD, Rivard MJ. A revised dosimetric characterization of the model S700 electronic brachytherapy source containing an anode-centering plastic insert and other components not included in the 2006 model. Med Phys. 2015;42:2764-76. doi.org/10.1118/1.4919280. PubMed PMID: 26127029.
12
Rong Y, Welsh JS. New technology in high-dose-rate brachytherapy with surface applicators for non-melanoma skin cancer treatment: electronic miniature x-ray brachytherapy. Skin Cancer Overview: InTech; 2011.
13
Liu DMC. Characterization of novel electronic brachytherapy system. Montreal: McGill University; 2007.
14
Holt RW, Rivard MJ. Electronic brachytherapy: comparisons with external-beam and high-dose-rate 192Ir brachytherapy. J Am Coll Radiol. 2008;5:221-3. doi.org/10.1016/j.jacr.2007.12.001. PubMed PMID: 18312972.
15
Clausen S, Schneider F, Jahnke L, Fleckenstein J, Hesser J, Glatting G, et al. A Monte Carlo based source model for dose calculation of endovaginal TARGIT brachytherapy with INTRABEAM and a cylindrical applicator. Z Med Phys. 2012;22:197-204. doi.org/10.1016/j.zemedi.2012.06.003. PubMed PMID: 22739321.
16
Grobmyer SR, Lightsey JL, Bryant CM, Shaw C, Yeung A, Bhandare N, et al. Low-kilovoltage, single-dose intraoperative radiation therapy for breast cancer: results and impact on a multidisciplinary breast cancer program. J Am Coll Surg. 2013;216:617-23. doi.org/10.1016/j.jamcollsurg.2012.12.038. PubMed PMID: 23415885.
17
Chiu-Tsao S-T, Davis S, Pike T, DeWerd LA, Rusch TW, Burnside RR, et al. Two-dimensional dosimetry for an electronic brachytherapy source using radiochromic EBT film: Determination of TG43 parameters. Brachytherapy. 2007;6:110. doi.org/10.1016/j.brachy.2007.02.110.
18
Kelley L, Axelrod S, Dutta A. SU-DD-A2-03: Measurement of Skin Dose When Using FlexiShield® with the Axxent® Electronic Brachytherapy System. Medical Physics. 2008;35:2632-. doi.org/10.1118/1.2961358.
19
Holt RW, Thomadsen BR, Orton CG. Point/Counterpoint. Miniature x-ray tubes will ultimately displace Ir-192 as the radiation sources of choice for high dose rate brachytherapy. Med Phys. 2008;35:815-7. doi.org/10.1118/1.2836415. PubMed PMID: 18404918.
20
Ballester-Sanchez R, Pons-Llanas O, Candela-Juan C, Celada-Alvarez FJ, de Unamuno-Bustos B, Llavador-Ros M, et al. Efficacy and safety of electronic brachytherapy for superficial and nodular basal cell carcinoma. J Contemp Brachytherapy. 2015;7:231-8. doi.org/10.5114/jcb.2015.52140. PubMed PMID: 26207112. PubMed PMCID: 4499517.
21
Beatty J, Biggs PJ, Gall K, Okunieff P, Pardo FS, Harte KJ, et al. A new miniature x-ray device for interstitial radiosurgery: dosimetry. Med Phys. 1996;23:53-62. doi.org/10.1118/1.597791. PubMed PMID: 8700033.
22
Eaton DJ, Duck S. Dosimetry measurements with an intra-operative x-ray device. Phys Med Biol. 2010;55:N359-69. doi.org/10.1088/0031-9155/55/12/N02. PubMed PMID: 20505225.
23
Hendricks JS, McKinney GW, Fensin ML, James MR, Johns RC, Durkee JW, et al. MCNPX 2.6. 0 Extensions. Los Alamos National Laboratory. 2008.
24
Ay MR, Shahriari M, Sarkar S, Adib M, Zaidi H. Monte carlo simulation of x-ray spectra in diagnostic radiology and mammography using MCNP4C. Phys Med Biol. 2004;49:4897-917. doi.org/10.1088/0031-9155/49/21/004. PubMed PMID: 15584526.
25
McKinney G, Durkee J, Waters L, Pelowitz D, James M, Hendricks J. Review of Monte Carlo all-particle transport codes and overview of recent MCNPX features. PoS. 2006;088.
26
Braga MR, Penna R, Vasconcelos DC, Pereira C, Guerra BT, Silva C, editors. Nuclear densimeter of soil simulated in MCNP-4C code. International Nuclear Atlantic Conference: Rio de Janeiro, RJ, Brazil; 2009.
27
Ihsan A, Heo SH, Cho SO. Optimization of X-ray target parameters for a high-brightness microfocus X-ray tube. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms. 2007;264:371-7. doi.org/10.1016/j.nimb.2007.09.023.
28
Grant EJ, Posada CM, Castano CH, Lee HK, editors. Electron field emission Particle in Cell (PIC) coupled with MCNPX simulation of a CNT-based flat-panel-X-ray source. Medical Imaging 2011: Physics of Medical Imaging. 2011;7961:796108.
29
McConn RJ, Gesh CJ, Pagh RT, Rucker RA, Williams III R. Compendium of material composition data for radiation transport modeling. WA (US): Pacific Northwest National Laboratory (PNNL), Richland; 2011.
30
Hughes III HG. Summary of DBCN Options in MCNP6. Los Alamos National Laboratory (LANL); 2013.
31
Pelowitz D, Durkee J, Elson J, Fensin M, James M, Johns R, et al. MCNPX 2.7. 0 Extensions, LA-UR-11-02295. New Mexico: Los Alamos National Laboratory; 2011.
32
Nasseri MM. Determination of tungsten target parameters for transmission X-ray tube: A simulation study using Geant4. Nuclear Engineering and Technology. 2016;48:795-8. doi.org/10.1016/j.net.2016.01.006.
33
Seibert JA. X-ray imaging physics for nuclear medicine technologists. Part 1: Basic principles of x-ray production. J Nucl Med Technol. 2004;32:139-47. PubMed PMID: 15347692.
34
Mordechai S. Applications of Monte Carlo method in science and engineering. InTech, Rijeka. 2011:6.
35
Ganguly A, Karim R. Essential physics for radiology and imaging. New Delhi: Academic Publishers; 2016.
36
Zoubair M, El Bardouni T, Allaoui O, Boulaich Y, El Bakkari B, El Younoussi C, et al. Computing Efficiency Improvement in Monte Carlo Simulation of a 12 MV Photon Beam Medical LINAC. World Journal of Nuclear Science and Technology. 2013;3:14. doi.org/10.4236/wjnst.2013.31003.
37
Ihsan A, Heo SH, Cho SO. A microfocus X-ray tube based on a microstructured X-ray target. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms. 2009;267:3566-73. doi.org/10.1016/j.nimb.2009.08.012.
38
Sukowski F, Uhlmann N. Monte Carlo Simulations in NDT. Applications of Monte Carlo Method in Science and Engineering: InTech; 2011.
39
Wang R, Pei L, Huang Z. Study on Calculation of Detector Flux with Monte Carlo Methods. Journal of Nuclear Science and Technology. 2000;37:436-40.
40
Davis SD. Air-kerma strength determination of a miniature x-ray source for brachytherapy applications. 2009.
41
Malabre-O’Sullivan N. Low energy photon mimic of the tritium beta decay energy spectrum. 2013.
42
Williams T. Axial Energy Distribution in Disc-Shaped Tantalum and Aluminium Bremsstrahlung Conversion Targets. Acta Physica Polonica-Series A General Physics. 2009;115:1180. doi.org/10.12693/APhysPolA.115.1180.
43
Sofiienko A, Jarvis C, Ådne V. Electron range evaluation and X-ray conversion optimization in tungsten transmission-type targets with the aid of wide electron beam Monte Carlo simulations
44
ORIGINAL_ARTICLE
Monte Carlo Simulation of Electron Beams produced by LIAC Intraoperative Radiation Therapy Accelerator
Background: One of the main problems of dedicated IORT accelerators is to determine dosimetric characteristics of the electron beams. Monte Carlo simulation of IORT accelerator head and produced beam will be useful to improve the accuracy of beam dosimetry.Materials and Methods: Liac accelerator head was modeled using the BEAMnrcMonte Carlo simulation system. Phase-space files were generated at the bottom of the applicators. These phase-space files were used as an input source in DOSXYZnrc and BEAMDP codes for dose calculation and analysis of the characteristic of the electron beams in all applicators and energies.Results: The results of Monte Carlo calculations are in very close agreement with the measurements. There is a decrease in the peak of the initial spectrum when electrons come from the end of accelerator wave guide to the end of applicator. By decreasing the applicator diameter, the mean energy of electron beam decreased. Using applicators and increasing their size, X-ray contamination will increase. The percentage of X-ray contamination increases by applicator diameter. This is related to the increase of the mean energy of electron beams.Conclusion: Application of PMMA collimator leads to, although well below accepted level, the production of bremsstrahlung. The results of this study showed that special design of LIAC head accompanying by PMMA collimator system cause to produce an electron beam with an individual dosimetric characteristic making it a useful tool for intraoperative radiotherapy purposes.
https://jbpe.sums.ac.ir/article_43296_afd60a1228ee34403cdf3efdcb9812c0.pdf
2018-03-01
43
52
Monte Carlo Simulation
IORT
Photon Contamination
Dosimetry
LIAC
M
Robatjazi
robatjazi1361@gmail.com
1
Medical Physics and Radiological Sciences Department, Sabzevar University of Medical Sciences, Sabzevar, Iran
AUTHOR
K
Tanha
tanha.kaveh@gmail.com
2
Persian Gulf Nuclear Medicine Research Center, Bushehr University of Medical Sciences, Bushehr, Iran
AUTHOR
S R
Mahdavi
srmahdavi@hotmail.com
3
Medical Physics Department, Iran University of Medical Sciences, Tehran, Iran
LEAD_AUTHOR
H R
Baghani
4
Physics Department, School of Sciences, Hakim Sabzevari University, Sabzevar, Iran
AUTHOR
H R
Mirzaei
5
Radiation Therapy Department, Shahid Beheshti University of Medical Sciences, Tehran, Iran
AUTHOR
M
Mousavi
mmousavi@razi.tums.ac.ir
6
Medical Physics and Radiological Sciences Department, Sabzevar University of Medical Sciences, Sabzevar, Iran
AUTHOR
N
Nafissi
nafissi_n@yahoo.com
7
Surgery Department, Iran University of Medical Sciences, Tehran, Iran
AUTHOR
E
Akbari
akbari@gmail.com
8
Oncological Surgery Department, Shahid Beheshti University of Medical Sciences, Tehran, Iranv
AUTHOR
Baghani HR, Aghamiri SM, Mahdavi SR, Akbari ME, Mirzaei HR. Comparing the dosimetric characteristics of the electron beam from dedicated intraoperative and conventional radiotherapy accelerators. J Appl Clin Med Phys. 2015;16:5017. doi: 10.1120/jacmp.v16i2.5017. PubMed PMID: 26103175.
1
Laitano RF, Guerra AS, Pimpinella M, Caporali C, Petrucci A. Charge collection efficiency in ionization chambers exposed to electron beams with high dose per pulse. Phys Med Biol. 2006;51:6419-36. doi.org/10.1088/0031-9155/51/24/009. PubMed PMID: 17148826.
2
Di Martino F, Giannelli M, Traino AC, Lazzeri M. Ion recombination correction for very high dose-per-pulse high-energy electron beams. Med Phys. 2005;32:2204-10. doi.org/10.1118/1.1940167. PubMed PMID: 16121574.
3
Righi S, Karaj E, Felici G, Di Martino F. Dosimetric characteristics of electron beams produced by two mobile accelerators, Novac7 and Liac, for intraoperative radiation therapy through Monte Carlo simulation. J Appl Clin Med Phys. 2013;14:3678. PubMed PMID: 23318376.
4
Iaccarino G, Strigari L, D’Andrea M, Bellesi L, Felici G, Ciccotelli A, et al. Monte Carlo simulation of electron beams generated by a 12 MeV dedicated mobile IORT accelerator. Phys Med Biol. 2011;56:4579-96. doi.org/10.1088/0031-9155/56/14/022. PubMed PMID: 21725139.
5
Rogers DW, Faddegon BA, Ding GX, Ma CM, We J, Mackie TR. BEAM: a Monte Carlo code to simulate radiotherapy treatment units. Med Phys. 1995;22:503-24. doi.org/10.1118/1.597552. PubMed PMID: 7643786.
6
Rogers D, Ma C, Walters B, Ding G, Sheikh-Bagheri D, Zhang G. BEAMnrc Users Manual National Research Council of Canada. NRCC Report PIRS-0509 (A) revK. 2002.
7
Bielajew AF, Rogers D. PRESTA: the parameter reduced electron-step transport algorithm for electron Monte Carlo transport. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms. 1986;18:165-81. doi.org/10.1016/S0168-583X(86)80027-1.
8
Walters B, Kawrakow I, Rogers D. DOSXYZnrc users manual. NRC Report PIRS. 2005;794.
9
Low DA, Harms WB, Mutic S, Purdy JA. A technique for the quantitative evaluation of dose distributions. Med Phys. 1998;25:656-61. doi.org/10.1118/1.598248. PubMed PMID: 9608475.
10
Low DA, Dempsey JF. Evaluation of the gamma dose distribution comparison method. Med Phys. 2003;30:2455-64. doi.org/10.1118/1.1598711. PubMed PMID: 14528967.
11
Ma C, Rogers D. BEAMDP users manual. NRC Report PIRS-0509 (D). 1995.
12
Bjork P, Nilsson P, Knoos T. Dosimetry characteristics of degraded electron beams investigated by Monte Carlo calculations in a setup for intraoperative radiation therapy. Phys Med Biol. 2002;47:239-56. doi.org/10.1088/0031-9155/47/2/305. PubMed PMID: 11837615.
13
Mihailescu D, Pimpinella M, Guerra A, Laitano R. Comparison of measured and Monte Carlo calculated dose distributions for the NOVAC7® linear accelerator. Romanian Journal of Physics. 2006;51:729.
14
Bjork P, Knoos T, Nilsson P. Influence of initial electron beam characteristics on monte carlo calculated absorbed dose distributions for linear accelerator electron beams. Phys Med Biol. 2002;47:4019-41. doi.org/10.1088/0031-9155/47/22/308. PubMed PMID: 12476980.
15
Bjork P, Knoos T, Nilsson P, Larsson K. Design and dosimetry characteristics of a soft-docking system for intraoperative radiation therapy. Int J Radiat Oncol Biol Phys. 2000;47:527-33. doi.org/10.1016/S0360-3016(00)00456-9. PubMed PMID: 10802382.
16
Pimpinella M, Mihailescu D, Guerra AS, Laitano RF. Dosimetric characteristics of electron beams produced by a mobile accelerator for IORT. Phys Med Biol. 2007;52:6197-214. doi.org/10.1088/0031-9155/52/20/008. PubMed PMID: 17921580.
17
Beddar AS, Biggs PJ, Chang S, Ezzell GA, Faddegon BA, Hensley FW, et al. Intraoperative radiation therapy using mobile electron linear accelerators: Report of AAPM Radiation Therapy Committee Task Group No. 72. Medical physics. 2006;33:1476-89. doi.org/10.1118/1.2194447.
18
Robatjazi M, Mahdavi SR, Takavr A, Baghani HR. Application of Gafchromic EBT2 film for intraoperative radiation therapy quality assurance. Phys Med. 2015;31:314-9. doi.org/10.1016/j.ejmp.2015.01.020. PubMed PMID: 25703011.
19
ORIGINAL_ARTICLE
New Pseudo-CT Generation Approach from Magnetic Resonance Imaging using a Local Texture Descriptor
Background: One of the challenges of PET/MRI combined systems is to derive an attenuation map to correct the PET image. For that, the pseudo-CT image could be used to correct the attenuation. Until now, most existing scientific researches construct this pseudo-CT image using the registration techniques. However, these techniques suffer from the local minima of the non-rigid deformation energy function which leads to unsatisfactory results.Objective: We propose in this paper a new approach for the generation of a pseudo-CT image from an MR image.Materials and Methods: This approach is based on a dense stereo matching concept, for that, we encode each pixel according to a shape related coordinates method, and we apply a local texture descriptor to put into correspondence pixels between MRI patient and MRI atlas images. The proposed approach was tested on a real MRI data, and in order to show the effectiveness of the proposed local descriptor, it has been compared to three other local descriptors: SIFT, SURF and DAISY. Also it was compared to registration method.Results: The calculation of structural similarity (SSIM) index and DICE coefficients, between the pseudo-CT image and the corresponding real CT image show that the proposed stereo matching approach outperforms a registration one.Conclusion: The use of dense matching with atlas promises good results in the creation of pseudo-CT. The proposed approach can be recommended as an alternative to registration techniques.
https://jbpe.sums.ac.ir/article_43297_3527cd2fce7265810f6be93fa29be3c2.pdf
2018-03-01
53
64
Pseudo-CT
Attenuation Correction
Stereo Matching
Local Texture Descriptor for Matching
PET/MRI
H
Chaibi
chaibih@yahoo.fr
1
Lab. LITIO, University of Oran 1 Ahmed Ben Bella- Algeria.
LEAD_AUTHOR
R
Nourine
hassene.chaibi@univ-saida.dz
2
Lab. LITIO, University of Oran 1 Ahmed Ben Bella- Algeria.
AUTHOR
Hofmann M, Steinke F, Scheel V, Charpiat G, Farquhar J, Aschoff P, et al. MRI-based attenuation correction for PET/MRI: a novel approach combining pattern recognition and atlas registration. J Nucl Med. 2008;49:1875-83. doi.org/10.2967/jnumed.107.049353. PubMed PMID: 18927326.
1
Rousseau F, Habas PA, Studholme C. A supervised patch-based approach for human brain labeling. IEEE Trans Med Imaging. 2011;30:1852-62. doi.org/10.1109/TMI.2011.2156806. PubMed PMID: 21606021. PubMed PMCID: 3318921.
2
Ay MR, Akbarzadeh A, Ahmadian A, Zaidi H. Classification of bones from MR images in torso PET-MR imaging using a statistical shape model. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 2014;734:196-200. doi.org/10.1016/j.nima.2013.09.007.
3
Keereman V, Fierens Y, Broux T, De Deene Y, Lonneux M, Vandenberghe S. MRI-based attenuation correction for PET/MRI using ultrashort echo time sequences. J Nucl Med. 2010;51:812-8. doi.org/10.2967/jnumed.109.065425. PubMed PMID: 20439508.
4
Ribeiro AS, Kops ER, Herzog H, Almeida P. Skull segmentation of UTE MR images by probabilistic neural network for attenuation correction in PET/MR. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 2013;702:114-6. doi.org/10.1016/j.nima.2012.09.005.
5
Mollet P, Keereman V, Clementel E, Vandenberghe S. Simultaneous MR-compatible emission and transmission imaging for PET using time-of-flight information. IEEE Trans Med Imaging. 2012;31:1734-42. doi.org/10.1109/TMI.2012.2198831. PubMed PMID: 22948340.
6
Mehranian A, Zaidi H. Joint Estimation of Activity and Attenuation in Whole-Body TOF PET/MRI Using Constrained Gaussian Mixture Models. IEEE Trans Med Imaging. 2015;34:1808-21. doi.org/10.1109/TMI.2015.2409157. PubMed PMID: 25769148.
7
Ribeiro AS, Kops ER, Herzog H, Almeida P. Hybrid approach for attenuation correction in PET/MR scanners. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 2014;734:166-70. doi.org/10.1016/j.nima.2013.09.034.
8
Kops ER, Herzog H, editors. Alternative methods for attenuation correction for PET images in MR-PET scanners. 2007 IEEE Nuclear Science Symposium Conference Record. IEEE: 2008.
9
Kops ER, Herzog H, editors. Template based attenuation correction for PET in MR-PET scanners. 2008 IEEE Nuclear Science Symposium Conference Record; 2008: IEEE.
10
Schreibmann E, Nye JA, Schuster DM, Martin DR, Votaw J, Fox T. MR-based attenuation correction for hybrid PET-MR brain imaging systems using deformable image registration. Med Phys. 2010;37:2101-9. doi.org/10.1118/1.3377774. PubMed PMID: 20527543.
11
Arabi H, Zaidi H. Magnetic resonance imaging-guided attenuation correction in whole-body PET/MRI using a sorted atlas approach. Med Image Anal. 2016;31:1-15. doi.org/10.1016/j.media.2016.02.002. PubMed PMID: 26948109.
12
Hirsch M, Hofmann M, Mantlik F, Pichler BJ, Schölkopf B, Habeck M, editors. A blind deconvolution approach for pseudo CT prediction from MR image pairs. 2012 19th IEEE International Conference on Image Processing; 2012: IEEE.
13
Torrado-Carvajal A, Herraiz JL, Alcain E, Montemayor AS, Garcia-Canamaque L, Hernandez-Tamames JA, et al. Fast Patch-Based Pseudo-CT Synthesis from T1-Weighted MR Images for PET/MR Attenuation Correction in Brain Studies. J Nucl Med. 2016;57:136-43. doi.org/10.2967/jnumed.115.156299. PubMed PMID: 26493204.
14
Burgos N, Cardoso MJ, Thielemans K, Modat M, Pedemonte S, Dickson J, et al. Attenuation correction synthesis for hybrid PET-MR scanners: application to brain studies. IEEE Trans Med Imaging. 2014;33:2332-41. doi.org/10.1109/TMI.2014.2340135. PubMed PMID: 25055381.
15
Mérida I, Costes N, Heckemann RA, Drzezga A, Förster S, Hammers A, editors. Evaluation of several multi-atlas methods for PSEUDO-CT generation in brain MRI-PET attenuation correction. 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI); 2015: IEEE.
16
Gui Y, Su A, Du J. Point-pattern matching method using SURF and Shape Context. Optik-International Journal for Light and Electron Optics. 2013;124:1869-73. doi.org/10.1016/j.ijleo.2012.05.037.
17
Toews M, Wells WM, Efficient and robust model-to-image alignment using 3D scale-invariant features. Med Image Anal. 2013;17:271-82. doi.org/10.1016/j.media.2012.11.002. PubMed PMID: 23265799. PubMed PMCID: 3606671.
18
Vinay A, Hebbar D, Shekhar VS, Murthy KB, Natarajan S. Two Novel Detector-Descriptor Based Approaches for Face Recognition Using SIFT and SURF. Procedia Computer Science. 2015;70:185-97. doi.org/10.1016/j.procs.2015.10.070.
19
Miao Q, Wang G, Shi C, Lin X, Ruan Z. A new framework for on-line object tracking based on SURF. Pattern Recognition Letters. 2011;32:1564-71. doi.org/10.1016/j.patrec.2011.05.017.
20
Zigh E, Belbachir MF. Soft computing strategy for stereo matching of multi spectral urban very high resolution IKONOS images. Applied soft computing. 2012;12:2156-67. doi.org/10.1016/j.asoc.2012.02.014.
21
Juntu J, Sijbers J, De Backer S, Rajan J, Van Dyck D. Machine learning study of several classifiers trained with texture analysis features to differentiate benign from malignant soft-tissue tumors in T1-MRI images. J Magn Reson Imaging. 2010;31:680-9. doi.org/10.1002/jmri.22095. PubMed PMID: 20187212.
22
Mayerhoefer ME, Breitenseher MJ, Kramer J, Aigner N, Hofmann S, Materka A. Texture analysis for tissue discrimination on T1-weighted MR images of the knee joint in a multicenter study: Transferability of texture features and comparison of feature selection methods and classifiers. J Magn Reson Imaging. 2005;22:674-80. doi.org/10.1002/jmri.20429. PubMed PMID: 16215966.
23
Chaibi H, Nourine R. Skull Segmentation of MR images based on texture features for attenuation correction in PET/MR. 2nd International Conference on Signal, Image, Vision and their Applications (SIVA’13): Guelma, Algeria; 2013.
24
Lowe DG. Distinctive image features from scale-invariant keypoints. International journal of computer vision. 2004;60:91-110. doi.org/10.1023/B:VISI.0000029664.99615.94.
25
Valenzuela REG, Schwartz WR, Pedrini H, editors. Dimensionality reduction through PCA over SIFT and SURF descriptors. Cybernetic Intelligent Systems (CIS), 2012 IEEE 11th International Conference on; 2012.
26
Tola E, Lepetit V, Fua P, editors. A fast local descriptor for dense matching. Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on: 2008.
27
In: The Retrospective Image Registration Evaluation Project. The Retrospective Image Registration Evaluation Project, Version 2.0. [cited April 2013]; Available from: http://www.insight-journal.org/rire/.
28
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process. 2004;13:600-12. doi.org/10.1109/TIP.2003.819861. PubMed PMID: 15376593.
29
Balan AG, Traina AJ, Ribeiro MX, Marques PM, Traina C, Jr. Smart histogram analysis applied to the skull-stripping problem in T1-weighted MRI. Comput Biol Med. 2012;42:509-22. doi.org/10.1016/j.compbiomed.2012.01.004. PubMed PMID: 22336779.
30
Poynton CB, Chen KT, Chonde DB, Izquierdo-Garcia D, Gollub RL, Gerstner ER, et al. Probabilistic atlas-based segmentation of combined T1-weighted and DUTE MRI for calculation of head attenuation maps in integrated PET/MRI scanners. Am J Nucl Med Mol Imaging. 2014;4:160-71. PubMed PMID: 24753982. PubMed PMCID: 3992209.
31
In: SPM. Statistical Parametric Mapping. Available from: http://www.fil.ion.ucl.ac.uk/spm/.
32
In: Elasti. A toolbox for rigid and nonrigid registration of images. Available from: http://elastix.isi.uu.nl/.
33
ORIGINAL_ARTICLE
Comparison and Evaluation of Different Treatment Plans with IFRT Field and 6 and 18 MV Energies in Hodgkin’s Lymphoma Involvement Neck and Mediastinum
Background: Radiotherapy with large mantle field is an effective technique in increasing the risk of secondary cancers among HL (Hodgkin Lymphoma) patients; therefore, it is essential to choose an effective treatment field including the least medical conditions in radiotherapy.Objective: The present study aimed to plan separate fields for neck and mediastinum using various energies, to compare dose distribution with MLC and to block field formation.Materials and Methods: In this study, 3D conformal treatments, Siemens Oncor accelerator equipped with multi-leaf collimator (MLC) were performed to create anterior-posterior fields. CT-scan data of 18 female patients with neck and mediastinal involvement was imported in TIGRT treatment planning system, and then treatment plans were introduced.Results and Conclusion: Using treatment plan 1, photon 6 MV in neck weighting 1 from interior, 0.5 from posterior, photon 18MV in mediastinum weighting 1 from interior and 0.5 from posterior, it was shown that regarding the common treatment plan used with photon 6 MV, mean dose delivered to breast, lung, esophagus and larynx reduced 6, 7, 41 and 10 percent, respectively and uniformity index improved by 10 percent. Using block compared to MLC in all treatment plans offered improved average dose in all organs under study. To protect breast and lung while using MLC and block in the first treatment plan seemed to be more appropriate; however, using blocks in comparison to MLC increased delivered mean dose in all organs under study. Using separate fields with Pb blocks, though, showed smaller increase.
https://jbpe.sums.ac.ir/article_43298_19c2e77e6bc39a9ab152ecfe2d3c3ce3.pdf
2018-03-01
65
72
10.31661/jbpe.v8i1Mar.759
Treatment planning
Radiotherapy
Hodgkin
MLC
3D Conformal
M B
Tavakoli
tavakoli@med.mui.ac.ir
1
Professor, Department of Medical Physics and Engineering, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
AUTHOR
M
Maleki
m.maleki777@yahoo.com
2
M.Sc., Department of Medical Physics and Engineering, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
LEAD_AUTHOR
A
Akhavan
ali52akhavan@yahoo.com
3
Assistant Professor, Department of Radiation-Oncology, Isfahan University of Medical Sciences, Isfahan, Iran
AUTHOR
T
Hadisinia
sahebehs@yahoo.com
4
Ph.D. Student, Department of Medical Physics and Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
AUTHOR
I
Abedi
iraj_abedi@yahoo.com
5
Ph.D. Student, Department of Medical Physics and Engineering, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
AUTHOR
A
Amouheidari
amouheidari@isfahanmiladhospital.ir
6
Oncologist, Department of Radiation Oncology, Isfahan Milad Hospital, Isfahan, Iran
AUTHOR
Villasboas JC, Ansell SM. Recent advances in the management of Hodgkin lymphoma. F1000Res. 2016;5. doi.org/10.12688/f1000research.8301.1. PubMed PMID: 27158471. PubMed PMCID: 4850875.
1
Steven H, Swerdlow EC, Harris NL, Jaffe ES, Pireli S, Stein H, et al. WHO classification of tumours of haematopoietic and lymphoid tissues. International agency for research on cancer. Lyon. 2008:274–88.
2
Norval EJ, Raubenheimer EJ. Second malignancies in Hodgkin’s disease: A review of the literature and report of a case with a secondary Lennert’s lymphoma. J Oral Maxillofac Pathol. 2014;18:S90-5. doi.org/10.4103/0973-029X.141332. PubMed PMID: 25364188. PubMed PMCID: 4211247.
3
Greenfield DM, Wright J, Brown JE, Hancock BW, Davies HA, O’Toole L, et al. High incidence of late effects found in Hodgkin’s lymphoma survivors, following recall for breast cancer screening. Br J Cancer. 2006;94:469-72. doi.org/10.1038/sj.bjc.6602974. PubMed PMID: 16465193. PubMed PMCID: 2361189.
4
Glicksman AS, Pajak TF, Gottlieb A, Nissen N, Stutzman L, Cooper MR. Second malignant neoplasms in patients successfully treated for Hodgkin’s disease: a Cancer and Leukemia Group B study. Cancer Treat Rep. 1982;66:1035-44. PubMed PMID: 6951632.
5
Koletsky AJ, Bertino JR, Farber LR, Prosnitz LR, Kapp DS, Fischer D, et al. Second neoplasms in patients with Hodgkin’s disease following combined modality therapy--the Yale experience. J Clin Oncol. 1986;4:311-7. doi.org/10.1200/JCO.1986.4.3.311. PubMed PMID: 3950674.
6
Arseneau JC, Canellos GP, Johnson R, DeVita VT, Jr. Risk of new cancers in patients with Hodgkin’s disease. Cancer. 1977;40:1912-6. doi.org/10.1002/1097-0142(197710)40:4+3.0.CO;2-D. PubMed PMID: 907993.
7
Poston GJ, Beauchamp D, Ruers T. Textbook of surgical oncology: CRC Press; 2007.
8
Koh ES, Tran TH, Heydarian M, Sachs RK, Tsang RW, Brenner DJ, et al. A comparison of mantle versus involved-field radiotherapy for Hodgkin’s lymphoma: reduction in normal tissue dose and second cancer risk. Radiat Oncol. 2007;2:13. doi.org/10.1186/1748-717X-2-13. PubMed PMID: 17362522. PubMed PMCID: 1847517.
9
Brady LW, Perez CA, Wazer DE. Perez & Brady’s principles and practice of radiation oncology: Lippincott Williams & Wilkins; 2013.
10
Halpering E, Perez C, Brady L. Principles and Practice of radiation Oncology. Philadelphia: Lippincott Williams & Wilkins; 2008.
11
Bonadonna G, Bonfante V, Viviani S, Di Russo A, Villani F, Valagussa P. ABVD plus subtotal nodal versus involved-field radiotherapy in early-stage Hodgkin’s disease: long-term results. J Clin Oncol. 2004;22:2835-41. doi.org/10.1200/JCO.2004.12.170. PubMed PMID: 15199092.
12
Specht L, Ng AK. Background and Rationale for Radiotherapy in Early-Stage Hodgkin Lymphoma. Radiotherapy for Hodgkin Lymphoma: Springer; 2011. p. 7-20.
13
Yan G, Liu C, Simon TA, Peng LC, Fox C, Li JG. On the sensitivity of patient-specific IMRT QA to MLC positioning errors. J Appl Clin Med Phys. 2009;10:2915. doi.org/10.1120/jacmp.v10i1.2915. PubMed PMID: 19223841.
14
Topolnjak R, van der Heide UA. An analytical approach for optimizing the leaf design of a multi-leaf collimator in a linear accelerator. Phys Med Biol. 2008;53:3007-21. doi.org/10.1088/0031-9155/53/11/017. PubMed PMID: 18490812.
15
Cheng CW, Das IJ, Steinberg T. Role of multileaf collimator in replacing shielding blocks in radiation therapy. Int J Cancer. 2001;96:385-95. doi.org/10.1002/ijc.1038. PubMed PMID: 11745510.
16
Tajiri M, Sunaoka M, Fukumura A, Endo M. A new radiation shielding block material for radiation therapy. Med Phys. 2004;31:3022-3. doi.org/10.1118/1.1809767. PubMed PMID: 15587655.
17
Khan, F.M. and J.P. Gibbons, Khan’s the physics of radiation therapy. Philadelphia, PA: Lippincott Williams & Wilkins; 2014.
18
Hoskin P, Díez P, Williams M, Lucraft H, Bayne M. Recommendations for the use of radiotherapy in nodal lymphoma. Clinical Oncology. 2013;25:49-58. doi.org/10.1016/j.clon.2012.07.011.
19
De Sanctis V, Bolzan C, D’Arienzo M, Bracci S, Fanelli A, Cox MC, et al. Intensity modulated radiotherapy in early stage Hodgkin lymphoma patients: is it better than three dimensional conformal radiotherapy? Radiat Oncol. 2012;7:129. doi.org/10.1186/1748-717X-7-129. PubMed PMID: 22857015. PubMed PMCID: 3484070.
20
Cella L, Liuzzi R, Magliulo M, Conson M, Camera L, Salvatore M, et al. Radiotherapy of large target volumes in Hodgkin’s lymphoma: normal tissue sparing capability of forward IMRT versus conventional techniques. Radiat Oncol. 2010;5:33. doi.org/10.1186/1748-717X-5-33. PubMed PMID: 20459790. PubMed PMCID: 2881006.
21
Schill S, Kampfer S, Hansmeier B, Nieder C, Geinitz H, editors. Sparing of critical organs in radiotherapy of mediastinal lymphoma. September 7-12, 2009. Munich, Germany: World Congress on Medical Physics and Biomedical Engineering; 2009.
22
Kumar PP, Good RR, Jones EO, Somers JE, McAnulty BE, McCaul GF, et al. Extended-field isocentric irradiation for Hodgkin’s disease. J Natl Med Assoc. 1987;79:969-80. PubMed PMID: 3312619. PubMed PMCID: 2625591.
23
Voong KR, McSpadden K, Pinnix CC, Shihadeh F, Reed V, Salehpour MR, et al. Dosimetric advantages of a “butterfly” technique for intensity-modulated radiation therapy for young female patients with mediastinal Hodgkin’s lymphoma. Radiat Oncol. 2014;9:94. doi.org/10.1186/1748-717X-9-94. PubMed PMID: 24735767. PubMed PMCID: 4013438.
24
Feuvret L, Noel G, Mazeron JJ, Bey P. Conformity index: a review. Int J Radiat Oncol Biol Phys. 2006;64:333-42. doi.org/10.1016/j.ijrobp.2005.09.028. PubMed PMID: 16414369.
25
Tavakolli MB, Maleki M, Akhavan A, Amooheidary A, Abedi E, Hadisinia T. Comparing different treatment plans in radiotherapy of Hodgkin’s disease involving neck and Mediastinum, using “Parallel- opposite fields” method. Journal of Isfahan Medical School. 2017;35:381-6. [in Persian]
26
ORIGINAL_ARTICLE
Determining Changes in Electromyography Indices when Measuring Maximum Acceptable Weight of Lift in Iranian Male Students
Background: In spite of the increasing degree of automation in industry, manual material handling (MMH) is still performed in many occupational settings. The aim of the current study was to determine the maximum acceptable weight of lift using psychophysical and electromyography indices.Methods: This experimental study was conducted among 15 male students recruited from Tehran University of Medical Sciences. Each participant performed 18 different lifting tasks which involved three lifting frequencies, three lifting heights and two box sizes. Each set of experiments was conducted during the 20 min work period using free-style lifting technique and subjective as well as objective assessment methodologies. SPSS version 18 software was used for descriptive and analytical analyses by Friedman, Wilcoxon and Spearman correlation techniques.Results: The results demonstrated that muscle activity increased with increasing frequency, height of lift and box size (P<0.05). Meanwhile, MAWLs obtained in this study are lower than those in Snook table (P<0.05). In this study, the level of muscle activity in percent MVC in relation to the erector spine muscles in L3 and T9 regions as well as left and right abdominal external oblique muscles were at 38.89%, 27.78%, 11.11% and 5.55% in terms of muscle activity is more than 70% MVC, respectively. The results of Wilcoxon test revealed that for both small and large boxes under all conditions, significant differences were detected between the beginning and end of the test values for MPF of erector spine in L3 and T9 regions, and left and right abdominal external oblique muscles (P<0.05). The results of Spearman correlation test showed that there was a significant relation between the MAWL, RMS and MPF of the muscles in all test conditions (P<0.05).Conclusion: Based on the results of this study, it was concluded if muscle activity is more than 70% of MVC, the values of Snook tables should be revisited. Furthermore, the biomechanical perspective should receive special attention in determining the standards for MMHÂ
https://jbpe.sums.ac.ir/article_43299_c9c1beb3da2759e5f0576198e4d415a0.pdf
2018-03-01
73
86
10.31661/jbpe.v8i1Mar.443
MMH
MAWL
EMG
Psychophysical Methodology
A
Salehi Sahl Abadi
asalehi529@gmail.com
1
Department of Occupational Health, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran
LEAD_AUTHOR
A
Mazloumi
2
Department of Occupational Health, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
AUTHOR
G
Nasl Saraji
3
Department of Occupational Health, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
AUTHOR
H
Zeraati
4
Department of Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
AUTHOR
M R
Hadian
5
Department of Postgraduate Studies, Faculty of Rehabilitation, Tehran University of Medical Sciences, Int. Campus (TUMS-IC), Tehran, Iran
AUTHOR
A H
Jafari
h_jafari@tums.ac.ir
6
Medical Physics and Biomedical Engineering Department, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
AUTHOR
Yoon H, Smith JL. Psychophysical and physiological study of one-handed and two-handed combined tasks. International Journal of Industrial Ergonomics. 1999;24:49-60. doi.org/10.1016/S0169-8141(98)00087-0.
1
Ayoub M, Selan J, Jiang B. Manual Materials Handling in Handbook of Human Factors Engineering Ed. Salvendy Gavriel, Chischester. New York, John Wiley; 1987.
2
Snook SH, Ciriello VM. The design of manual handling tasks: revised tables of maximum acceptable weights and forces. Ergonomics. 1991;34:1197-213. doi.org/10.1080/00140139108964855. PubMed PMID: 1743178.
3
Snook SH. The design of manual handling tasks. Ergonomics. 1978;21:963-85. doi.org/10.1080/00140137808931804. PubMed PMID: 729559.
4
Snook SH. Psychophysical considerations in permissible loads. Ergonomics. 1985;28:327-30. doi.org/10.1080/00140138508963140. PubMed PMID: 3158515.
5
Legg SJ, Myles WS. Maximum acceptable repetitive lifting workloads for an 8-hour work-day using psychophysical and subjective rating methods. Ergonomics. 1981;24:907-16. doi.org/10.1080/00140138108924913. PubMed PMID: 7338215.
6
Wu SP. Maximum acceptable weight of lift by Chinese experienced male manual handlers. Appl Ergon. 1997;28:237-44. doi.org/10.1016/S0003-6870(96)00080-4. PubMed PMID: 9414362.
7
Ciriello VM, Snook SH. A study of size, distance, height, and frequency effects on manual handling tasks. Hum Factors. 1983;25:473-83. PubMed PMID: 6667937.
8
Ciriello VM, Snook SH, Hashemi L, Cotnam J. Distributions of manual materials handling task parameters. International Journal of Industrial Ergonomics. 1999;24:379-88. doi.org/10.1016/S0169-8141(99)00005-0.
9
Karwowski W, Yates JW. Reliability of the psychophysical approach to manual lifting of liquids by females. Ergonomics. 1986;29:237-48. doi.org/10.1080/00140138608968262. PubMed PMID: 3956474.
10
Snook SH, Vaillancourt DR, Ciriello VM, Webster BS. Psychophysical studies of repetitive wrist flexion and extension. Ergonomics. 1995;38:1488-507. doi.org/10.1080/00140139508925204. PubMed PMID: 7635136.
11
Stellman JM. Encyclopaedia of occupational health and safety: International Labour Organization; 1998.
12
Jonsson B. Kinesiology: with special reference to electromyographic kinesiology. Electroencephalogr Clin Neurophysiol Suppl. 1978;(34):417-28. PubMed PMID: 285846.
13
Rohmert W. Problems in determining rest allowances Part 1: use of modern methods to evaluate stress and strain in static muscular work. Appl Ergon. 1973;4:91-5. doi.org/10.1016/0003-6870(73)90082-3. PubMed PMID: 15677120.
14
Panjabi MM. The stabilizing system of the spine. Part II. Neutral zone and instability hypothesis. J Spinal Disord. 1992;5:390-6; discussion 7. doi.org/10.1097/00002517-199212000-00002. PubMed PMID: 1490035.
15
Hodges PW, Moseley GL. Pain and motor control of the lumbopelvic region: effect and possible mechanisms. J Electromyogr Kinesiol. 2003;13:361-70. doi.org/10.1016/S1050-6411(03)00042-7. PubMed PMID: 12832166.
16
van Dieen JH, Selen LP, Cholewicki J. Trunk muscle activation in low-back pain patients, an analysis of the literature. J Electromyogr Kinesiol. 2003;13:333-51. doi.org/10.1016/S1050-6411(03)00041-5. PubMed PMID: 12832164.
17
Cifrek M, Medved V, Tonkovic S, Ostojic S. Surface EMG based muscle fatigue evaluation in biomechanics. Clin Biomech (Bristol, Avon). 2009;24:327-40. doi.org/10.1016/j.clinbiomech.2009.01.010. PubMed PMID: 19285766.
18
de Looze M, Bosch T, van Dieen J. Manifestations of shoulder fatigue in prolonged activities involving low-force contractions. Ergonomics. 2009;52:428-37. doi.org/10.1080/00140130802707709. PubMed PMID: 19401894.
19
Hostens I, Ramon H. Assessment of muscle fatigue in low level monotonous task performance during car driving. J Electromyogr Kinesiol. 2005;15:266-74. doi.org/10.1016/j.jelekin.2004.08.002. PubMed PMID: 15763673.
20
Lin MI, Liang HW, Lin KH, Hwang YH. Electromyographical assessment on muscular fatigue--an elaboration upon repetitive typing activity. J Electromyogr Kinesiol. 2004;14:661-9. doi.org/10.1016/j.jelekin.2004.03.004. PubMed PMID: 15491841.
21
Tucker K, Falla D, Graven-Nielsen T, Farina D. Electromyographic mapping of the erector spinae muscle with varying load and during sustained contraction. J Electromyogr Kinesiol. 2009;19:373-9. doi.org/10.1016/j.jelekin.2007.10.003. PubMed PMID: 18061480.
22
Kumar S. The effect of sustained spinal load on intra-abdominal pressure and EMG characteristics of trunk muscles. Ergonomics. 1997;40:1312-34. doi.org/10.1080/001401397187397. PubMed PMID: 9416014.
23
Arjmand N, Shirazi-Adl A. Model and in vivo studies on human trunk load partitioning and stability in isometric forward flexions. J Biomech. 2006;39:510-21. doi.org/10.1016/j.jbiomech.2004.11.030. PubMed PMID: 16389091.
24
Abdoli EM, Agnew MJ, Stevenson JM. An on-body personal lift augmentation device (PLAD) reduces EMG amplitude of erector spinae during lifting tasks. Clin Biomech (Bristol, Avon). 2006;21:456-65. doi.org/10.1016/j.clinbiomech.2005.12.021. PubMed PMID: 16494978.
25
Chan S. The use of EMG for load prediction during manual lifting. 2007.
26
ORIGINAL_ARTICLE
Diagnosis of Tempromandibular Disorders Using Local Binary Patterns
Background: Temporomandibular joint disorder (TMD) might be manifested as structural changes in bone through modification, adaptation or direct destruction. We propose to use Local Binary Pattern (LBP) characteristics and histogram-oriented gradients on the recorded images as a diagnostic tool in TMD assessment.Material and Methods: CBCT images of 66 patients (132 joints) with TMD and 66 normal cases (132 joints) were collected and 2 coronal cut prepared from each condyle, although images were limited to head of mandibular condyle. In order to extract features of images, first we use LBP and then histogram of oriented gradients. To reduce dimensionality, the linear algebra Singular Value Decomposition (SVD) is applied to the feature vectors matrix of all images. For evaluation, we used K nearest neighbor (K-NN), Support Vector Machine, Naïve Bayesian and Random Forest classifiers. We used Receiver Operating Characteristic (ROC) to evaluate the hypothesis.Results: K nearest neighbor classifier achieves a very good accuracy (0.9242), moreover, it has desirable sensitivity (0.9470) and specificity (0.9015) results, when other classifiers have lower accuracy, sensitivity and specificity.Conclusion: We proposed a fully automatic approach to detect TMD using image processing techniques based on local binary patterns and feature extraction. K-NN has been the best classifier for our experiments in detecting patients from healthy individuals, by 92.42% accuracy, 94.70% sensitivity and 90.15% specificity. The proposed method can help automatically diagnose TMD at its initial stages.
https://jbpe.sums.ac.ir/article_43300_470c338f188ec34e57403a52ad827214.pdf
2018-03-01
87
96
Temporomandibular Joint Disorder
Cone-Beam Computed Tomography
Local Binary Pattern
Histogram of Oriented Gradients
K Nearest Neighbor
A A
Haghnegahdar
ahagh@sums.ac.ir
1
Department of Oral & Maxillofacial Radiology, school of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
S
Kolahi
shirin.kolahi@gmail.com
2
Department of Oral & Maxillofacial Radiology, school of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran
LEAD_AUTHOR
L
Khojastepour
3
Department of Oral & Maxillofacial Radiology, school of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
F
Tajeripour
4
Department of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
AUTHOR
Alkhader M, Ohbayashi N, Tetsumura A, Nakamura S, Okochi K, Momin MA, et al. Diagnostic performance of magnetic resonance imaging for detecting osseous abnormalities of the temporomandibular joint and its correlation with cone beam computed tomography. Dentomaxillofac Radiol. 2010;39:270-6. doi.org/10.1259/dmfr/25151578. PubMed PMID: 20587650. PubMed PMCID: 3520245.
1
Okeson JP, de Kanter RJ. Temporomandibular disorders in the medical practice. Journal of family practice. 1996;43:347-57.
2
White SC, Pharoah MJ. Oral radiology: principles and interpretation. Amsterdam: Elsevier Health Sciences; 2014.
3
Zain-Alabdeen EH, Alsadhan RI. A comparative study of accuracy of detection of surface osseous changes in the temporomandibular joint using multidetector CT and cone beam CT. Dentomaxillofac Radiol. 2012;41:185-91. doi.org/10.1259/dmfr/24985971. PubMed PMID: 22378752. PubMed PMCID: 3520284.
4
Tsiklakis K, Syriopoulos K, Stamatakis HC. Radiographic examination of the temporomandibular joint using cone beam computed tomography. Dentomaxillofac Radiol. 2004;33:196-201. doi.org/10.1259/dmfr/27403192. PubMed PMID: 15371321.
5
Hussain AM, Packota G, Major PW, Flores-Mir C. Role of different imaging modalities in assessment of temporomandibular joint erosions and osteophytes: a systematic review. Dentomaxillofac Radiol. 2008;37:63-71. doi.org/10.1259/dmfr/16932758. PubMed PMID: 18239033.
6
Sindeaux R, Figueiredo PT, de Melo NS, Guimaraes AT, Lazarte L, Pereira FB, et al. Fractal dimension and mandibular cortical width in normal and osteoporotic men and women. Maturitas. 2014;77:142-8. doi.org/10.1016/j.maturitas.2013.10.011. PubMed PMID: 24289895.
7
Gaalaas L, Henn L, Gaillard PR, Ahmad M, Islam MS. Analysis of trabecular bone using site-specific fractal values calculated from cone beam CT images. Oral Radiology. 2014;30:179-85. doi.org/10.1007/s11282-013-0163-z.
8
Ghodsi M, Hassani H, Sanei S, Hicks Y. The use of noise information for detection of temporomandibular disorder. Biomedical Signal Processing and Control. 2009;4:79-85. doi.org/10.1016/j.bspc.2008.10.001.
9
Thevenot J, Chen J, Finnilä M, Nieminen M, Lehenkari P, Saarakkala S, et al. Local binary patterns to evaluate trabecular bone structure from micro-CT data: application to studies of human osteoarthritis. European Conference on Computer Vision: Springer; 2014.
10
Mäenpää T, Pietikäinen M. Texture analysis with local binary patterns. Handbook of Pattern Recognition and Computer Vision. 2005;3:197-216. doi.org/10.1142/9789812775320_0011.
11
Ojala T, Pietikainen M, Harwood D. Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. 9-13 Oct. 1994. Jerusalem: Pattern Recognition, 1994. Vol. 1-Conference A: Computer Vision & Image Processing., Proceedings of the 12th IAPR International Conference on; 1994.
12
Ojala T, Pietikäinen M, Harwood D. A comparative study of texture measures with classification based on featured distributions. Pattern recognition. 1996;29:51-9. doi.org/10.1016/0031-3203(95)00067-4.
13
Tajeripour F, Kabir E, Sheikhi A. Fabric defect detection using modified local binary patterns. EURASIP Journal on Advances in Signal Processing. 2008;2008:60. doi.org/10.1155/2008/783898.
14
Ojala T, Pietikainen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on pattern analysis and machine intelligence. 2002;24:971-87. doi.org/10.1109/TPAMI.2002.1017623.
15
Lowe DG. Distinctive image features from scale-invariant keypoints. International journal of computer vision. 2004;60:91-110. doi.org/10.1023/B:VISI.0000029664.99615.94.
16
Dalal N, Triggs B, editors . Histograms of oriented gradients for human detection. 20-25 June 2005. San Diego: Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on; 2005.
17
Sadek RA. SVD based image processing applications: state of the art, contributions and research challenges. arXiv preprint arXiv:1211.7102. 2012;3:26–34.
18
Altman NS. An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician. 1992;46:175-85.
19
Smola AJ, Schölkopf B. A tutorial on support vector regression. Statistics and computing. 2004;14:199-222. doi.org/10.1023/B:STCO.0000035301.49549.88.
20
Rish I. An empirical study of the naive Bayes classifier. IJCAI 2001 workshop on empirical methods in artificial intelligence; 2001: IBM New York. New York: IJCAI 2001 workshop on empirical methods in artificial intelligence; 2001.
21
Horning N. Introduction to decision trees and random forests. American Museum of Natural History’s. 2013.
22
Powers DM. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. 2011.
23
Bradley AP. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern recognition. 1997;30:1145-59. doi.org/10.1016/S0031-3203(96)00142-2.
24
Mishra AK, Kim D, Andayana I, editors. Development of three dimensional binary patterns for local bone structure analysis. Bioinformatics and Biomedicine Workshops (BIBMW), 2011 IEEE International Conference on; 2011: IEEE.
25
ORIGINAL_ARTICLE
A Quantitative Investigation on the Effect of Edge Enhancement for Improving Visual Acuity at Different Levels of Contrast
Background: The major limitation in human vision is refractive error. Auxiliary equipment and methods for these people are not always available. In addition, limited range of accommodation in adult people when switching from a far point to a near point is not simply possible. In this paper, we are looking for solutions to use the facilities of digital image processing and displaying to improve visual acuity when using digital display devices. We quantitatively investigate the effect of edge enhancement on improving the visual acuity at different levels of contrast. We can improve visual acuity for people such as emmetropia, myopia and hyperopia when they utilize display devices.Materials and Methods: According to the objective of this research, 24 visual acuity optical charts were designed using MATLAB software, based on logMAR standard. The charts have different levels of contrast with enhanced edges of optotypes at two brightness levels: 0 and 255. The proposed patterns were tested on 20 human subjects. The obtained results for each chart were analyzed in SPSS software.Results: The results show that at all contrast levels, edge enhancement improves visual acuity. The degree of improvement where the edges have brightness level of 0 is higher than where the edges have brightness level of 255. Conclusion: Based on the results, enhancing the edges of optotypes in the background image improves visual acuity by about 16.1% on logMAR scale.
https://jbpe.sums.ac.ir/article_43301_d962e233aba35aef30a538f798a797a4.pdf
2018-03-01
97
106
Optical Aberrations
Pre-compensation
Visual Acuity
Contrast Sensitivity
Edge Enhancement
S
Nabavi
soheil.nabavi@yahoo.com
1
Biomedical Engineering Dept., Faculty of Advanced Medical Technology, Isfahan University of Medical Sciences, Isfahan, Iran
AUTHOR
A
Mehri Dehnavi
mehri@med.mui.ac.ir
2
Biomedical Engineering Dept., Faculty of Advanced Medical Technology, Isfahan University of Medical Sciences, Isfahan, Iran
LEAD_AUTHOR
A
Vard
alivard@gmail.com
3
Biomedical Engineering Dept., Faculty of Advanced Medical Technology, Isfahan University of Medical Sciences, Isfahan, Iran
AUTHOR
S
Mohammad Pour
soheilmpour@gmail.com
4
Iranian Scientific Association of Optometry, Tehran, Iran
AUTHOR
Cervino A, Hosking SL, Montes-Mico R, Bates K. Clinical ocular wavefront analyzers. J Refract Surg. 2007;23:603-16. PubMed PMID: 17598581.
1
Lombardo M, Lombardo G. Wave aberration of human eyes and new descriptors of image optical quality and visual performance. J Cataract Refract Surg. 2010;36:313-31. doi.org/10.1016/j.jcrs.2009.09.026. PubMed PMID: 20152616.
2
Golovinskiy A, Matusik W, Pfister H, Rusinkiewicz S, Funkhouser T. A statistical model for synthesis of detailed facial geometry. ACM Transactions on Graphics (TOG) 2006;25:1025–34.
3
Peli E, Woods RL. Image enhancement for impaired vision: the challenge of evaluation. Int J Artif Intell Tools. 2009;18:415-38. doi.org/10.1142/S0218213009000214. PubMed PMID: 20161188. PubMed PMCID: 2727758.
4
Peli E. Limitations of image enhancement for the visually impaired. Optom Vis Sci. 1992;69:15-24. doi.org/10.1097/00006324-199201000-00003. PubMed PMID: 1371332.
5
Templin K, Didyk P, Ritschel T, Eisemann E, Myszkowski K, Seidel H-P, editors. Apparent resolution enhancement for animations. April 28 - 30, 2011. New York: Proceedings of the 27th Spring Conference on Computer Graphics; 2011.
6
Stengel M, Eisemann M, Wenger S, Hell B, Magnor M. Optimizing apparent display resolution enhancement for arbitrary videos. IEEE Trans Image Process. 2013;22:3604-13. doi.org/10.1109/TIP.2013.2265885. PubMed PMID: 23744682.
7
Pamplona VF, Oliveira MM, Aliaga DG, Raskar R. Tailored displays to compensate for visual aberrations. ACM Transactions on Graphics. 2012;31:1–12.
8
Huang F-C, Lanman D, Barsky BA, Raskar R. Correcting for optical aberrations using multilayer displays. ACM Transactions on Graphics (TOG). 2012;31:185. doi.org/10.1145/2366145.2366204.
9
Masia B, Wetzstein G, Aliaga C, Raskar R, Gutierrez D. Display adaptive 3D content remapping. Computers & Graphics. 2013;37:983-96. doi.org/10.1016/j.cag.2013.06.004.
10
Alonso Jr M, Barreto A, Cremades JG. Image pre-compensation to facilitate computer access for users with refractive errors. ACM SIGACCESS Accessibility and Computing; 2004: ACM. ACM SIGACCESS Accessibility and Computing. 2004;77-78:126–32.
11
Alonso J, Barreto A, Cremades JG, Jacko JA, Adjouadi M. Image pre-compensation to facilitate computer access for users with refractive errors. Behaviour & Information Technology. 2005;24:161-73. doi.org/10.1080/01449290412331327456.
12
Montalto C, Garcia-Dorado I, Aliaga D, Oliveira MM, Meng F. A total variation approach for customizing imagery to improve visual acuity. ACM Transactions on Graphics (TOG). 2015;34:28. doi.org/10.1145/2717307.
13
Bailey IL, Lovie JE. New design principles for visual acuity letter charts. Am J Optom Physiol Opt. 1976;53:740-5. doi.org/10.1097/00006324-197611000-00006. PubMed PMID: 998716.
14
Carlson NB, Kurtz D, Hines C. Clinical procedures for ocular examination. New York: McGraw-Hill; 2004.
15
Virgili G, Acosta R. Reading aids for adults with low vision. Cochrane Database Syst Rev. 2006;(4):CD003303. doi.org/10.1002/14651858.cd003303.pub2. PubMed PMID: 17054166.
16
Rangayyan RM. Biomedical image analysis. Florida: CRC press; 2004.
17
Staff Z. The lighting handbook. Austria: Zumtobel; 2004.
18
ORIGINAL_ARTICLE
Automatic Prostate Cancer Segmentation Using Kinetic Analysis in Dynamic Contrast-Enhanced MRI
Background: Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) provides functional information on the microcirculation in tissues by analyzing the enhancement kinetics which can be used as biomarkers for prostate lesions detection and characterization.Objective: The purpose of this study is to investigate spatiotemporal patterns of tumors by extracting semi-quantitative as well as wavelet-based features, both extracted from pixel-based time-signal intensity curves to segment prostate lesions on prostate DCE-MRI. Methods: Quantitative dynamic contrast-enhanced MRI data were acquired on 22 patients. Optimal features selected by forward selection are used for the segmentation of prostate lesions by applying fuzzy c-means (FCM) clustering. The images were reviewed by an expert radiologist and manual segmentation performed as the ground truth. Results: Empirical results indicate that fuzzy c-mean classifier can achieve better results in terms of sensitivity, speciïcity when semi-quantitative features were considered versus wavelet kinetic features for lesion segmentation (Sensitivity of 87.58% and 75.62%, respectively) and (Specificity of 89.85% and 68.89 %, respectively).Conclusion: The proposed segmentation algorithm in this work can potentially be implemented for automatic prostate lesion detection in a computer aided diagnosis scheme and combined with morphologic features to increase diagnostic credibility
https://jbpe.sums.ac.ir/article_43302_14e1b8781fb4b75151203d2f18aeaa18.pdf
2018-03-01
107
116
DCE-MRI
Prostate Cancer
Semi-quantitative Feature
Wavelet Kinetic Feature
Segmentation
S
Navaei Lavasani
1
Department of Biomedical Engineering and Medical Physics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
AUTHOR
A
Mostaar
2
Department of Biomedical Engineering and Medical Physics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
LEAD_AUTHOR
M
Ashtiyani
m.ash.80@gmail.com
3
Department of Biomedical Engineering and Medical Physics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
AUTHOR
Stamey TA, Caldwell M, McNeal JE, Nolley R, Hemenez M, Downs J. The prostate specific antigen era in the United States is over for prostate cancer: what happened in the last 20 years? J Urol. 2004;172:1297-301. doi.org/10.1097/01.ju.0000139993.51181.5d. PubMed PMID: 15371827.
1
Artan Y, Langer DL, Haider MA, van der Kwast TH, Evans AJ, Wernick MN, et al. Prostate cancer segmentation with multispectral MRI using cost-sensitive conditional random fields. 28 June-1 July 2009 . Boston: Biomedical Imaging: From Nano to Macro, 2009 ISBI’09 IEEE International Symposium on; 2009.
2
Verma S, Turkbey B, Muradyan N, Rajesh A, Cornud F, Haider MA, et al. Overview of dynamic contrast-enhanced MRI in prostate cancer diagnosis and management. AJR Am J Roentgenol. 2012;198:1277-88. doi.org/10.2214/AJR.12.8510. PubMed PMID: 22623539.
3
Mostaar A, Ashtiyani M, Lavasany SN, Rexhepi AH, Kongoli R, Dey A, et al. AAn Improved Ant Colony Algorithm Optimization for Automated MRI Segmentation Using Probabilistic Atlas. Int J Innov Res Sci Eng. 2015;3:399, 406.
4
Turkbey B, Bernardo M, Merino MJ, Wood BJ, Pinto PA, Choyke PL. MRI of localized prostate cancer: coming of age in the PSA era. Diagn Interv Radiol. 2012;18:34-45. PubMed PMID: 21922459.
5
Puech P, Betrouni N, Makni N, Dewalle AS, Villers A, Lemaitre L. Computer-assisted diagnosis of prostate cancer using DCE-MRI data: design, implementation and preliminary results. Int J Comput Assist Radiol Surg. 2009;4:1-10. doi.org/10.1007/s11548-008-0261-2. PubMed PMID: 20033597.
6
van Dorsten FA, van der Graaf M, Engelbrecht MR, van Leenders GJ, Verhofstad A, Rijpkema M, et al. Combined quantitative dynamic contrast-enhanced MR imaging and 1H MR spectroscopic imaging of human prostate cancer. Journal of Magnetic Resonance Imaging. 2004;20:279-87. doi.org/10.1002/jmri.20113.
7
Futterer JJ, Heijmink SW, Scheenen TW, Veltman J, Huisman HJ, Vos P, et al. Prostate cancer localization with dynamic contrast-enhanced MR imaging and proton MR spectroscopic imaging 1. Radiology. 2006;241:449-58. doi.org/10.1148/radiol.2412051866.
8
Yankeelov TE, Gore JC. Dynamic Contrast Enhanced Magnetic Resonance Imaging in Oncology: Theory, Data Acquisition, Analysis, and Examples. Curr Med Imaging Rev. 2009;3:91-107. doi.org/10.2174/157340507780619179. PubMed PMID: 19829742. PubMed PMCID: 2760951.
9
Birgani PM, Ashtiyani M, editors. Wireless Real-time Brain Mapping. 27-30 Nov. 2006. Guilin: Communication Technology, 2006 ICCT’06 International Conference on; 206.
10
Engelbrecht MR, Huisman HJ, Laheij RJ, Jager GJ, van Leenders GJ, Hulsbergen-Van De Kaa CA, et al. Discrimination of prostate cancer from normal peripheral zone and central gland tissue by using dynamic contrast-enhanced MR imaging. Radiology. 2003;229:248-54. doi.org/10.1148/radiol.2291020200. PubMed PMID: 12944607.
11
Artan Y, Haider MA, Langer DL, van der Kwast TH, Evans AJ, Yang Y, et al. Prostate cancer localization with multispectral MRI using cost-sensitive support vector machines and conditional random fields. IEEE Trans Image Process. 2010;19:2444-55. doi.org/10.1109/TIP.2010.2048612. PubMed PMID: 20716496.
12
Liu X, Langer DL, Haider MA, Yang Y, Wernick MN, Yetik IS. Prostate cancer segmentation with simultaneous estimation of Markov random field parameters and class. IEEE Trans Med Imaging. 2009;28:906-15. doi.org/10.1109/TMI.2009.2012888. PubMed PMID: 19164079.
13
Guo Y, Ruan S, Walker P, Feng Y, editors . Prostate cancer segmentation from multiparametric MRI based on fuzzy Bayesian model. 29 April-2 May 2014. Beijing: Biomedical Imaging (ISBI), 2014 IEEE 11th International Symposium on; 2014.
14
Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging. 2013;26:1045-57. doi.org/10.1007/s10278-013-9622-7. PubMed PMID: 23884657. PubMed PMCID: 3824915.
15
Lu W, Yao J, Lu C, Prindiville S, Chow C, editors. DCE-MRI segmentation and motion correction based on active contour model and forward mapping. 19-20 June 2006. Las Vegas: Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2006 SNPD 2006 Seventh ACIS International Conference on; 2006.
16
Maintz JA, Viergever MA. A survey of medical image registration. Medical image analysis. 1998;2:1-36. doi.org/10.1016/S1361-8415(01)80026-8.
17
Chen CJ, Chang RF, Moon WK, Chen DR, Wu HK. 2-D ultrasound strain images for breast cancer diagnosis using nonrigid subregion registration. Ultrasound Med Biol. 2006;32:837-46. doi.org/10.1016/j.ultrasmedbio.2006.02.1406. PubMed PMID: 16785006.
18
Schneider CA, Rasband WS, Eliceiri KW. NIH Image to ImageJ: 25 years of image analysis. Nat Methods. 2012;9:671-5. doi.org/10.1038/nmeth.2089. PubMed PMID: 22930834.
19
Padhani AR. Dynamic contrast-enhanced MRI studies in human tumours. Br J Radiol. 1999;72:427-31. doi.org/10.1259/bjr.72.857.10505003. PubMed PMID: 10505003.
20
Alonzi R, Padhani AR, Allen C. Dynamic contrast enhanced MRI in prostate cancer. Eur J Radiol. 2007;63:335-50. doi.org/10.1016/j.ejrad.2007.06.028. PubMed PMID: 17689907.
21
Navaei-Lavasani S, Fathi-Kazerooni A, Saligheh-Rad H, Gity M. Discrimination of Benign and Malignant Suspicious Breast Tumors Based on Semi-Quantitative DCE-MRI Parameters Employing Support Vector Machine. Frontiers in Biomedical Technologies. 2015;2:87-92.
22
Fotouhi A, Eqlimi E, Makkiabadi B, editors. Evaluation of adaptive parafac alogorithms for tracking of simulated moving brain sources. 25-29 Aug. 2015. Milan: Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE; 2015.
23
Eqlimi E. Resting State Functional Connectivity Analysis Based on Mutual Information Graphs For MS Patients. 2013.
24
Kuhl CK, Mielcareck P, Klaschik S, Leutner C, Wardelmann E, Gieseke J, et al. Dynamic breast MR imaging: are signal intensity time course data useful for differential diagnosis of enhancing lesions? Radiology. 1999;211:101-10. doi.org/10.1148/radiology.211.1.r99ap38101. PubMed PMID: 10189459.
25
Isebaert S, De Keyzer F, Haustermans K, Lerut E, Roskams T, Roebben I, et al. Evaluation of semi-quantitative dynamic contrast-enhanced MRI parameters for prostate cancer in correlation to whole-mount histopathology. Eur J Radiol. 2012;81:e217-22. doi.org/10.1016/j.ejrad.2011.01.107. PubMed PMID: 21349667.
26
Navaei Lavasani S, Fathi Kazerooni A, Saligheh Rad H, Gity M. Discrimination of Benign and Malignant Suspicious Breast Tumors Based on Semi-Quantitative DCE-MRI Parameters Employing Support Vector Machine. Frontiers in Biomedical Technologies. 2015;2:87-92.
27
Unser M, Aldroubi A. A review of wavelets in biomedical applications. Proceedings of the IEEE. 1996;84:626-38. doi.org/10.1109/5.488704.
28
Mallat S. A wavelet tour of signal processing: Academic press. Cambridge: Academic press; 1999.
29
Poularikas AD. Transforms and applications handbook. Florida: CRC press; 2010.
30
Struzik ZR, Siebes A, editors. The Haar wavelet transform in the time series similarity paradigm. European Conference on Principles of Data Mining and Knowledge Discovery: Springer; 1999.
31
Birgani PM, Ashtiyani M, Asadi S, editors. MRI segmentation using fuzzy c-means clustering algorithm basis neural network. 7-11 April 2008. Damascus: Information and Communication Technologies: From Theory to Applications, 2008. ICTTA 2008. 3rd International Conference on; 2008.
32
Ashtiyani M, Asadi S, Birgani PM, editors . ICA-based EEG classification using fuzzy c-mean algorithm. 7-11 April 2008. Damascus. Information and Communication Technologies: From Theory to Applications, 2008 ICTTA 2008 3rd International Conference on; 2008.
33
Ashtiyani M, Behbahani S, Asadi S, Birgani PM, editors . Transmitting encrypted data by wavelet transform and neural network. 15-18 Dec. 2007. Giza: Signal Processing and Information Technology, 2007 IEEE International Symposium on; 2007.
34
Mansoory MS, Ashtiyani M, Sarabadani H. Automatic crack detection in eggshell based on SUSAN Edge Detector using Fuzzy Thresholding. Modern Applied Science. 2011;5:117. doi.org/10.5539/mas.v5n6p117.
35
Bezdek JC, Ehrlich R, Full W. FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences. 1984;10:191-203. doi.org/10.1016/0098-3004(84)90020-7.
36
Kohavi R, John GH. Wrappers for feature subset selection. Artificial intelligence. 1997;97:273-324. doi.org/10.1016/S0004-3702(97)00043-X.
37
Jamshidi O, Pilevar AH. Automatic segmentation of medical images using fuzzy c-means and the genetic algorithm. Journal of Computational Medicine. 2013;2013.
38
Udupa JK, Leblanc VR, Zhuge Y, Imielinska C, Schmidt H, Currie LM, et al. A framework for evaluating image segmentation algorithms. Comput Med Imaging Graph. 2006;30:75-87. doi.org/10.1016/j.compmedimag.2005.12.001. PubMed PMID: 16584976.
39
Medved M, Karczmar G, Yang C, Dignam J, Gajewski TF, Kindler H, et al. Semiquantitative analysis of dynamic contrast enhanced MRI in cancer patients: Variability and changes in tumor tissue over time. J Magn Reson Imaging. 2004;20:122-8. doi.org/10.1002/jmri.20061. PubMed PMID: 15221817.
40
Jackson A, Reinsberg S, Sohaib S, Charles-Edwards E, Jhavar S, Christmas T, et al. Dynamic contrast-enhanced MRI for prostate cancer localization. The British journal of radiology. 2014.
41
ORIGINAL_ARTICLE
A New Algorithm for Skin Lesion Border Detection in Dermoscopy Images
Background: With advances in medical imaging systems, digital dermoscopy has become one of the major imaging modalities in the analysis of skin lesions. Thus, automated segmentation or border detection has a great impact on the subsequent steps of skin cancer computer-aided diagnosis using demoscopy images. Since dermoscopy images suffer from artifacts such as shading and hair, there is a need for automated and robust artifact attenuation removal and lesion border detection.Methods: A method for segmentation of dermoscopy images is proposed based on active contour. To this end, at first, a simple method for hair pixels is restored and a new scheme for shading detection is proposed. Then, particle swarm optimization (PSO) algorithm is applied to select the best coefficients for converting RGB to gray level. The obtained gray level image is then used as input for multi Otsu method which provides initial contour for border detection using active contour. Finally, Chan and Vese active contour is used for final lesion border detection.Results: The method is tested on a total of 145 dermoscopic images: 79 cases with benign lesion and 75 cases with melanoma lesion. Mean accuracy, sensitivity and specificity were obtained 94%, 78.5% and 99%, respectively.Conclusion: Results reveal that the proposed method segments the lesion from dermoscopy images accurately.
https://jbpe.sums.ac.ir/article_43303_2723e72752795b124db22defa15dbaee.pdf
2018-03-01
117
126
Dermoscopy
Melanoma
Segmentation
Active Contour
E
Meskini
ms_helfroush@sutech.ac.ir
1
Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
AUTHOR
M S
Helfroush
2
Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
LEAD_AUTHOR
K
Kazemi
kazemi@sutech.ac.ir
3
Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
AUTHOR
M
Sepaskhah
4
Molecular Dermatology Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
Jemal A, Siegel R, Ward E, Murray T, Xu J, Smigal C, et al. Cancer statistics, 2006. CA Cancer J Clin. 2006;56:106–30. doi: 10.3322/canjclin.56.2.106.
1
In: American Cancer Society. What are the key statistics about melanoma skin cancer? Available from: http://www.cancer.org/cancer/skincancer-melanoma/detailedguide/melanoma-skin-cancer-key-statistics.
2
Celebi ME, Kingravi HA, Iyatomi H, Aslandogan YA, Stoecker WV, Moss RH, et al. Border detection in dermoscopy images using statistical region merging. Skin Res Technol. 2008;14:347-53. doi.org/10.1111/j.1600-0846.2008.00301.x. PubMed PMID: 19159382. PubMed PMCID: 3160669.
3
Emre Celebi M, Wen Q, Hwang S, Iyatomi H, Schaefer G. Lesion border detection in dermoscopy images using ensembles of thresholding methods. Skin Res Technol. 2013;19:e252-8. doi.org/10.1111/j.1600-0846.2012.00636.x. PubMed PMID: 22676490.
4
Zhou H, Schaefer G, Celebi ME, Lin F, Liu T. Gradient vector flow with mean shift for skin lesion segmentation. Comput Med Imaging Graph. 2011;35:121-7. doi.org/10.1016/j.compmedimag.2010.08.002. PubMed PMID: 20832242.
5
Xie F, Bovik AC. Automatic segmentation of dermoscopy images using self-generating neural networks seeded by genetic algorithm. Pattern Recognition. 2013;46:1012-9. doi.org/10.1016/j.patcog.2012.08.012.
6
Eysenbach G, Paessler J, Diepgen T. COM6 /485: Dermis.net - from a Dermatology Internet Atlas to a Dermatology Internet Portal Site. Journal of Medical Internet Research. 1999;1(Suppl 1):e19. doi:10.2196/jmir.1.suppl1.e19.
7
Osher S, Sethian JA. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. Journal of computational physics. 1988;79:12-49. doi.org/10.1016/0021-9991(88)90002-2.
8
Mumford D, Shah J. Optimal approximations by piecewise smooth functions and associated variational problems. Communications on pure and applied mathematics. 1989;42:577-685. doi.org/10.1002/cpa.3160420503.
9
Chan TF, Vese LA. Active contours without edges. IEEE Trans Image Process. 2001;10:266-77. doi.org/10.1109/83.902291. PubMed PMID: 18249617.
10
Toossi MT, Pourreza HR, Zare H, Sigari MH, Layegh P, Azimi A. An effective hair removal algorithm for dermoscopy images. Skin Res Technol. 2013;19:230-5. doi.org/10.1111/srt.12015. PubMed PMID: 23560826.
11
Otsu N. A threshold selection method from gray-level histograms. Automatica. 1975;11:23-7.
12
Cavalcanti PG, Scharcanski J, Lopes CB. Shading attenuation in human skin color images. Advances in Visual Computing: Springer; 2010. p. 190-8.
13
Kennedy J, editor. TThe particle swarm: social adaptation of knowledge. 13-16 April 1997. Indianapolis: Evolutionary Computation, 1997, IEEE International Conference on; 1997.
14
Lei W, Huichuan D. Application of Otsu’method in multi-threshold image segmentation. Computer Engineering and Design. 2008;29:2844-5.
15
Soille P. Morphological image analysis: principles and applications. Berlin: Springer Science & Business Media; 2013.
16
Weickert J. Anisotropic diffusion in image processing. Stuttgart: Teubner Stuttgart; 1998.
17
Saez A, Serrano C, Acha B. Model-based classification methods of global patterns in dermoscopic images. IEEE Trans Med Imaging. 2014;33:1137-47. doi.org/10.1109/TMI.2014.2305769. PubMed PMID: 24770918.
18
ORIGINAL_ARTICLE
Use of Magnetic Resonance Imaging in Food Quality Control: A Review
Modern challenges of food science require a new understanding of the determinants of food quality and safety. Application of advanced imaging modalities such as magnetic resonance imaging (MRI) has seen impressive successes and fast growth over the past decade. Since MRI does not have any harmful ionizing radiation, it can be considered as a magnificent tool for the quality control of food products. MRI allows the structure of foods to be imaged noninvasively and nondestructively. Magnetic resonance images can present information about several processes and material properties in foods. This review will provide an overview of the most prominent applications of MRI in food research.
https://jbpe.sums.ac.ir/article_43293_69b8c4a7acae6f76ac4daa7c0ed004ec.pdf
2018-03-01
127
132
Food Quality
Food Technology
Food Analysis
Food Industry
Magnetic Resonance Imaging
Hamed
Ebrahimnejad
hsimple11@gmail.com
1
DDS, MSc, Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Kerman University of Medical Sciences, Kerman, Iran
LEAD_AUTHOR
Hadi
Ebrahimnejad
hsimple11@yahoo.com
2
DVM, Ph.D., Assistant Professor, Department of Food Hygiene and Public Health, Faculty of Veterinary Medicine, Shahid Bahonar University of Kerman, Kerman, Iran
AUTHOR
A
Salajegheh
3
MSc, Department of Radiology, School of Paramedical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
H
Barghi
4
DDS, MSc, Assistant Professor, Department of Pediatric Dentistry, Faculty of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
Grunert KG. Food quality and safety: consumer perception and demand. European Review of Agricultural Economics. 2005;32:369-91. doi.org/10.1093/eurrag/jbi011.
1
Xiong Z, Sun DW, Pu H, Gao W, Dai Q. Applications of Emerging Imaging Techniques for Meat Quality and Safety Detection and Evaluation: A Review. Crit Rev Food Sci Nutr. 2015:0. PubMed PMID: 25975703.
2
Bushong SC. Magnetic resonance imaging: physical and biological principles. 3rd ed. United States of America: Mosby; 2003.
3
Van As H, van Duynhoven J. MRI of plants and foods. J Magn Reson. 2013;229:25-34. doi.org/10.1016/j.jmr.2012.12.019. PubMed PMID: 23369439.
4
Collewet G, Bogner P, Allen P, Busk H, Dobrowolski A, Olsen E, et al. Determination of the lean meat percentage of pig carcasses using magnetic resonance imaging. Meat Sci. 2005;70:563-72. doi.org/10.1016/j.meatsci.2005.02.005. PubMed PMID: 22063881.
5
Kremer PV, Forster M, Scholz AM. Use of magnetic resonance imaging to predict the body composition of pigs in vivo. Animal. 2013;7:879-84. doi.org/10.1017/S1751731112002340. PubMed PMID: 23228200.
6
Mohrmann M, Roehe R, Susenbeth A, Baulain U, Knap PW, Looft H, et al. Association between body composition of growing pigs determined by magnetic resonance imaging, deuterium dilution technique, and chemical analysis. Meat Sci. 2006;72:518-31. doi.org/10.1016/j.meatsci.2005.08.020. PubMed PMID: 22061736.
7
Monziols M, Collewet G, Bonneau M, Mariette F, Davenel A, Kouba M. Quantification of muscle, subcutaneous fat and intermuscular fat in pig carcasses and cuts by magnetic resonance imaging. Meat Sci. 2006;72:146-54. doi.org/10.1016/j.meatsci.2005.06.018. PubMed PMID: 22061385.
8
Dransfield E. The taste of fat. Meat Sci. 2008;80:37-42. doi.org/10.1016/j.meatsci.2008.05.030. PubMed PMID: 22063168.
9
Collewett G, Toussaint C, Davenel A, Akoka S, Médale F, Fauconneau B, et al. Magnetic resonance imaging as a tool to quantify the adiposity distribution in fish. Special Publication-Royal Society of Chemistry. 2001;262:252-8. doi.org/10.1039/9781847551252-00252.
10
Davenel A, Bazin C, Quellec S, Challois S, Gispert M, Mercat M, et al. High throughput determination of intramuscular fat content by magnetic resonance imaging. Journees de la recherche porcine en France. 2012;44:53-4.
11
Clerjon S, Bonny JM. Diffusion-weighted NMR micro-imaging of lipids: Application to food products. 2011.
12
Clerjon S, Kondjoyan A, Bonny JM, Portanguen S, Chevarin C, Thomas A, et al. Oil uptake by beef during pan frying: impact on fatty acid composition. Meat Sci. 2012;91:79-87. doi.org/10.1016/j.meatsci.2011.12.009. PubMed PMID: 22265369.
13
Horigane A, Motoi H, Irie K, Yoshida M. Observation of the structure, moisture distribution, and oil distribution in the coating of tempura by NMR micro imaging. Journal of food science. 2003;68:2034-9. doi.org/10.1111/j.1365-2621.2003.tb07014.x.
14
Brix O, Apablaza P, Baker A, Taxt T, Grüner R. Chemical shift based MR imaging and gas chromatography for quantification and localization of fat in Atlantic mackerel. Journal of experimental marine biology and ecology. 2009;376:68-75. doi.org/10.1016/j.jembe.2009.06.006.
15
Wu J-L, Zhang J-L, Du X-X, Shen Y-J, Lao X, Zhang M-L, et al. Evaluation of the distribution of adipose tissues in fish using magnetic resonance imaging (MRI). Aquaculture. 2015;448:112-22. doi.org/10.1016/j.aquaculture.2015.06.002.
16
Ballerini L, Hogberg A, Borgefors G, Bylund A-C, Lindgard A, Lundstrom K, et al. A segmentation technique to determine fat content in NMR images of beef meat. IEEE transactions on Nuclear Science. 2002;49:195-9. doi.org/10.1109/TNS.2002.998751.
17
Toussaint C, Fauconneau B, Médale F, Collewet G, Akoka S, Haffray P, et al. Description of the heterogeneity of lipid distribution in the flesh of brown trout (Salmo trutta) by MR imaging. Aquaculture. 2005;243:255-67. doi.org/10.1016/j.aquaculture.2004.09.029.
18
Aaslyng MD, Bejerholm C, Ertbjerg P, Bertram HC, Andersen HJ. Cooking loss and juiciness of pork in relation to raw meat quality and cooking procedure. Food quality and preference. 2003;14:277-88. doi.org/10.1016/S0950-3293(02)00086-1.
19
Bouhrara M, Clerjon S, Damez JL, Chevarin C, Portanguen S, Kondjoyan A, et al. Dynamic MRI and thermal simulation to interpret deformation and water transfer in meat during heating. J Agric Food Chem. 2011;59:1229-35. doi.org/10.1021/jf103384d. PubMed PMID: 21265572.
20
Bouhrara M, Lehallier B, Clerjon S, Damez JL, Bonny JM. Mapping of muscle deformation during heating: in situ dynamic MRI and nonlinear registration. Magn Reson Imaging. 2012;30:422-30. doi.org/10.1016/j.mri.2011.10.002. PubMed PMID: 22133287.
21
Hansen CL, van der Berg F, Ringgaard S, Stodkilde-Jorgensen H, Karlsson AH. Diffusion of NaCl in meat studied by (1)H and (23)Na magnetic resonance imaging. Meat Sci. 2008;80:851-6. doi.org/10.1016/j.meatsci.2008.04.003. PubMed PMID: 22063607.
22
Ruiz-Cabrera MA, Gou P, Foucat L, Renou JP, Daudin JD. Water transfer analysis in pork meat supported by NMR imaging. Meat Sci. 2004;67:169-78. doi.org/10.1016/j.meatsci.2003.10.005. PubMed PMID: 22061130.
23
Veliyulin E, Egelandsdal B, Marica F, Balcom BJ. Quantitative 23Na magnetic resonance imaging of model foods. J Agric Food Chem. 2009;57:4091-5. doi.org/10.1021/jf9000605. PubMed PMID: 21314196.
24
Vestergaard C, Risum J, Adler-Nissen J. Quantification of salt concentrations in cured pork by computed tomography. Meat Sci. 2004;68:107-13. doi.org/10.1016/j.meatsci.2004.02.011. PubMed PMID: 22062013.
25
Vestergaard C, Risum J, Adler-Nissen J. (23)Na-MRI quantification of sodium and water mobility in pork during brine curing. Meat Sci. 2005;69:663-72. doi.org/10.1016/j.meatsci.2004.11.001. PubMed PMID: 22063144.
26
Aursand IG, Erikson U, Veliyulin E. Water properties and salt uptake in Atlantic salmon fillets as affected by ante-mortem stress, rigor mortis, and brine salting: a low-field 1 H NMR and 1 H/23 Na MRI study. Food chemistry. 2010;120:482-9. doi.org/10.1016/j.foodchem.2009.10.041.
27
Aursand IG, Veliyulin E, Bocker U, Ofstad R, Rustad T, Erikson U. Water and salt distribution in Atlantic salmon (Salmo salar) studied by low-field 1H NMR, 1H and 23Na MRI and light microscopy: effects of raw material quality and brine salting. J Agric Food Chem. 2009;57:46-54. doi.org/10.1021/jf802158u. PubMed PMID: 19090754.
28
Erikson U, Veliyulin E, Singstad T, Aursand M. Salting and Desalting of Fresh and Frozen-thawed Cod (Gadus morhua) Fillets: A Comparative Study Using 23Na NMR, 23Na MRI, Low-field 1H NMR, and Physicochemical Analytical Methods. Journal of food science. 2004;69:FEP107-FEP14. doi.org/10.1111/j.1365-2621.2004.tb13362.x.
29
Veliyulin E, Aursand IG. (1)H and (23)Na MRI studies of Atlantic salmon (Salmo salar) and Atlantic cod (Gadus morhua) fillet pieces salted in different brine concentrations. J Sci Food Agric. 2007;87(14):2676-83. doi.org/10.1002/jsfa.3030. PubMed PMID: 20836176.
30
Bonny J, Laurent W, Renou J. Characterisation of meat structure by NMR imaging at high field. Special publication-royal society of chemistry. 2001;262:17-21. doi.org/10.1039/9781847551252-00017.
31
Laurent W, Bonny J, Renou J. Muscle characterisation by NMR imaging and spectroscopic techniques. Food chemistry. 2000;69:419-26. doi.org/10.1016/S0308-8146(00)00051-0.
32
Pérez-Palacios T, Antequera T, Durán ML, Caro A, Rodríguez PG, Palacios R. MRI-based analysis of feeding background effect on fresh Iberian ham. Food chemistry. 2011;126:1366-72. doi.org/10.1016/j.foodchem.2010.11.101.
33
Bonny JM, Renou JP. Water diffusion features as indicators of muscle structure ex vivo. Magn Reson Imaging. 2002;20:395-400. doi.org/10.1016/S0730-725X(02)00515-5. PubMed PMID: 12206864.
34
amez J, Clerjon S, Labas R, Danon J, Peyrin F, Bonny J, editors. Microstructure characterization of meat by quantitative MRI. 12-17 August 2012. Montreal: 58th International Congress of Meat Science and Technology; 2012.
35
Shaarani SM, Nott KP, Hall LD. Combination of NMR and MRI quantitation of moisture and structure changes for convection cooking of fresh chicken meat. Meat Sci. 2006;72:398-403. doi.org/10.1016/j.meatsci.2005.07.017. PubMed PMID: 22061723.
36
Van Duynhoven J, Goudappel GW, Weglarz W. Noninvasive assessment of moisture migration in food products by MRI. In: Codd S, Seymour JD et al., eds. Magnetic resonance microscopy. Weinheim: Wiley-VCH; 2009. pp. 331–351.
37
Gonzalez J, McCarthy K, McCarthy M. Textural and structural changes in lasagna after cooking. Journal of texture studies. 2000;31:93-108. doi.org/10.1111/j.1745-4603.2000.tb00286.x.
38
Thybo AK, Szczypiński PM, Karlsson AH, Dønstrup S, Stødkilde-Jørgensen HS, Andersen HJ. Prediction of sensory texture quality attributes of cooked potatoes by NMR-imaging (MRI) of raw potatoes in combination with different image analysis methods. Journal of Food Engineering. 2004;61:91-100. doi.org/10.1016/S0260-8774(03)00190-0.
39
Lai H-M, Hwang S-C. Water status of cooked white salted noodles evaluated by MRI. Food research international. 2004;37:957-66. doi.org/10.1016/j.foodres.2004.06.008.
40
Hills BP, Remigereau B. NMR studies of changes in subcellular water compartmentation in parenchyma apple tissue during drying and freezing. International Journal of Food Science & Technology. 1997;32:51-61. doi.org/10.1046/j.1365-2621.1997.00381.x.
41
Hindmarsh J, Buckley C, Russell A, Chen X, Gladden L, Wilson D, et al. Imaging droplet freezing using MRI. Chemical engineering science. 2004;59:2113-22. doi.org/10.1016/j.ces.2003.12.031.
42
Mahdjoub R, Chouvenc P, Seurin MJ, Andrieu J, Briguet A. Sucrose solution freezing studied by magnetic resonance imaging. Carbohydr Res. 2006;341:492-8. doi.org/10.1016/j.carres.2006.01.005. PubMed PMID: 16430876.
43
Chaland B, Mariette F, Marchal P, De Certaines J. 1H nuclear magnetic resonance relaxometric characterization of fat and water states in soft and hard cheese. J Dairy Res. 2000;67:609-18. doi.org/10.1017/S0022029900004398. PubMed PMID: 11131073.
44
Mahdjoub R, Molegnana J, Seurin MJ, Briguet A. High resolution magnetic resonance imaging evaluation of cheese. Journal of food science. 2003;68:1982-4. doi.org/10.1111/j.1365-2621.2003.tb07005.x.
45
Hwang S-S, Cheng Y-C, Chang C, Lur H-S, Lin T-T. Magnetic resonance imaging and analyses of tempering processes in rice kernels. Journal of Cereal Science. 2009;50:36-42. doi.org/10.1016/j.jcs.2008.10.012.
46
Cornillon P, Salim LC. Characterization of water mobility and distribution in low- and intermediate-moisture food systems. Magn Reson Imaging. 2000;18:335-41. doi.org/10.1016/S0730-725X(99)00139-3. PubMed PMID: 10745143.
47
Mariette F. Investigations of food colloids by NMR and MRI. Current Opinion in Colloid & Interface Science. 2009;14:203-11. doi.org/10.1016/j.cocis.2008.10.006.
48
Miquel ME, Hall LD. Measurement by MRI of storage changes in commercial chocolate confectionery products. Food research international. 2002;35:993-8. doi.org/10.1016/S0963-9969(02)00160-6.
49
JHA SN, MATSUOKA T. Non-Destructive Techniques for Quality Evaluation of Intact Fruits and Vegetables. Food Science and Technology Research. 2000;6:248-51. doi.org/10.3136/fstr.6.248.
50
Milczarek RR, McCarthy MJ. Low-field MR Sensors for Fruit Inspection. In: Codd SL, Seymour JD, eds. Magnetic Resonance Microscopy. Weinheim, Wiley-VCH; 2009. p. 289-299.
51
Van As H. Intact plant MRI for the study of cell water relations, membrane permeability, cell-to-cell and long distance water transport. J Exp Bot. 2007;58:743-56. doi.org/10.1093/jxb/erl157. PubMed PMID: 17175554.
52
Hills B, Clark C. Quality assessment of horticultural products by NMR. Annual Reports on NMR spectroscopy. 2003;50:75-120. doi.org/10.1016/S0066-4103(03)50002-3.
53
Clark CJ, MacFall JS. Quantitative magnetic resonance imaging of ‘Fuyu’ persimmon fruit during development and ripening. Magn Reson Imaging. 2003;21:679-85. doi.org/10.1016/S0730-725X(03)00082-1. PubMed PMID: 12915200.
54
Musse M, Quellec S, Cambert M, Devaux M-F, Lahaye M, Mariette F. Monitoring the postharvest ripening of tomato fruit using quantitative MRI and NMR relaxometry. Postharvest Biology and Technology. 2009;53:22-35. doi.org/10.1016/j.postharvbio.2009.02.004.
55
Joyce DC, Hockings PD, Mazucco RA, Shorter AJ. 1H-Nuclear magnetic resonance imaging of ripening’Kensington Pride’mango fruit. Functional plant biology. 2002;29:873-9. doi.org/10.1071/PP01150.
56
Shaarani SM, Cardenas-Blanco A, Amin MG, Soon NG, Hall LD. Monitoring development and ripeness of oil palm fruit (Elaeis guneensis) by MRI and bulk NMR. International Journal of Agriculture and Biology (Pakistan). 2010;12:101–105.
57
Khoshroo A, Keyhani A, Zoroofi R, Yaghoobi G, editors. NNondestructive inspection of pomegranate maturity using magnetic resonance imaging and neural networks. April; 2011. France: CIGR Section VI International Symposium on Towards a Sustainable Food Chain Food Process, Bioprocess Food Qual Manag Nantes; 2011.
58
Galed G, Fernandez-Valle ME, Martinez A, Heras A. Application of MRI to monitor the process of ripening and decay in citrus treated with chitosan solutions. Magn Reson Imaging. 2004;22:127-37. doi.org/10.1016/j.mri.2003.05.006. PubMed PMID: 14972402.
59
Barreiro P, Ortiz C, Ruiz-Altisent M, Ruiz-Cabello J, Fernandez-Valle ME, Recasens I, et al. Mealiness assessment in apples and peaches using MRI techniques. Magn Reson Imaging. 2000;18:1175-81. doi.org/10.1016/S0730-725X(00)00179-X. PubMed PMID: 11118773.
60
Otero L, Préstamo G. Effects of pressure processing on strawberry studied by nuclear magnetic resonance. Innovative Food Science & Emerging Technologies. 2009;10:434-40. doi.org/10.1016/j.ifset.2009.04.004.
61
Haishi T, Koizumi H, Arai T, Koizumi M, Kano H. Rapid Detection of Infestation of Apple Fruits by the Peach Fruit Moth, Carposina sasakii Matsumura, Larvae Using a 0.2-T Dedicated Magnetic Resonance Imaging Apparatus. Appl Magn Reson. 2011;41:1-18. doi.org/10.1007/s00723-011-0222-8. PubMed PMID: 21957330. PubMed PMCID: 3162149.
62
Lammertyn J, Dresselaers T, Van Hecke P, Jancsok P, Wevers M, Nicolai BM. MRI and x-ray CT study of spatial distribution of core breakdown in ‘Conference’ pears. Magn Reson Imaging. 2003;21:805-15. doi.org/10.1016/S0730-725X(03)00105-X. PubMed PMID: 14559346.
63
Létal J, Jirak D, Šuderlová L, Hájek M. MRI ‘texture’analysis of MR images of apples during ripening and storage. LWT-Food Science and Technology. 2003;36:719-27. doi.org/10.1016/S0023-6438(03)00099-9.
64
Burdon J, Clark C. Effect of postharvest water loss on ‘Hayward’kiwifruit water status. Postharvest Biology and Technology. 2001;22:215-25. doi.org/10.1016/S0925-5214(01)00095-3.
65
ORIGINAL_ARTICLE
Protective Effects of IMOD and Cimetidine against Radiation-induced Cellular Damage
Radiation damage is to a large extent caused by overproduction of reactive oxygen species (ROS). Radioprotectors are agents or substances that reduce the effects of radiation in healthy normal tissues while maintaining the sensitivity to radiation damage in tumor cells.Radioprotectors are agents or substances that reduce the effects of radiation in healthy normal tissues while maintaining the sensitivity to radiation damage in tumor cellsCimetidine was found more effective when used in vivo; this effect might be due to the augmentation of the presence of Sulphur atom in the compound which is important for their scavenging activity.Recently, a new herbal-based medicine with immunomodulatory capacities, Setarud (IMOD), was introduced as an additional therapy in various inflammatory diseases and HIV infection. IMOD is a mixture of herbal extracts enriched with selenium. Selenium confers protection by inducing or activating cellular free-radical scavenging systems and by enhancing peroxide breakdown. This article suggests that nontoxic amount of IMOD and cimetidine have radioprotective properties and could reduce cytotoxic effects of radiation.
https://jbpe.sums.ac.ir/article_43290_9ec0b2edf0eadb4576dd6f4245f1e9bb.pdf
2018-03-01
133
140
Radioprotection
Cimetidine
IMOD
Immunomodulator
Free Radical
S
Rahgoshai
siroos_rahgosha@yahoo.com
1
Department of Medical radiation Science, School of Paramedicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
AUTHOR
M
Mohammadi
mmohammadi@yahoo.com
2
Department of Medical radiation Science, School of Paramedicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran
LEAD_AUTHOR
S
Refahi
3
Assistant Professor of Medical Physics, Department of Medical Physics, Faculty of Medicine, Ardabil University of Medical Sciences, Ardabil, Iran
AUTHOR
M
Oladghaffari
gaffarim21@gmail.com
4
Cellular & Molecular Biology Research Center, Medical Physics Department, Faculty of medicine, Babol University of Medical Sciences, Babol, Iran
AUTHOR
S M R
Aghamiri
smr-aghamiri@sbu.ac.ir
5
Department of Radiation Medicine, Shahid Beheshti University of Medical Sciences,Tehran, Iran
AUTHOR
Shukla SK, Gupta ML. Approach towards development of a radioprotector using herbal source against lethal irradiation. Int Res J Plant Sci. 2010;1:118-25.
1
Islamian JP, Mohammadi M, Baradaran B. Inhibition of human esophageal squamous cell carcinomas by targeted silencing of tumor enhancer genes: an overview. Cancer Biol Med. 2014;11:78-85. PubMed PMID: 25009749. PubMed PMCID: 4069799.
2
Weitzel DH, Tovmasyan A, Ashcraft KA, Rajic Z, Weitner T, Liu C, et al. Radioprotection of the brain white matter by Mn(III) n-Butoxyethylpyridylporphyrin-based superoxide dismutase mimic MnTnBuOE-2-PyP5+. Mol Cancer Ther. 2015;14:70-9. doi.org/10.1158/1535-7163.MCT-14-0343. PubMed PMID: 25319393. PubMed PMCID: 4397941.
3
Raafat Y, Eman N, El Omama S, Nadia F, Maha G. Evaluation of Anti-Oxidant Status and Radioprotective Activity of a Novel Anti-Cancer Drug in Mice. Journal of Cancer Therapy. 2011;2011.
4
Said U, Azab K, Soliman A. Cardio protective role of garlic (Allium Sativum) against oxidative stress induced by gamma radiation exposure. Isotope and Radiation Research. 2004;36:465-79.
5
Manea A, Fortuno A, Martin-Ventura JL. Oxidative stress in cardiovascular pathologies: genetics, cellular, and molecular mechanisms and future antioxidant therapies. Oxid Med Cell Longev. 2012;2012:373450. doi.org/10.1155/2012/373450. PubMed PMID: 23346282. PubMed PMCID: 3549365.
6
Liu W, Chen Q, Wu S, Xia X, Wu A, Cui F, et al. Radioprotector WR-2721 and mitigating peptidoglycan synergistically promote mouse survival through the amelioration of intestinal and bone marrow damage. J Radiat Res. 2015;56:278-86. doi.org/10.1093/jrr/rru100. PubMed PMID: 25617317. PubMed PMCID: 4380048.
7
Naruka K, Bhartiya HC. Protection of bone marrow of Swiss albino mouse against whole body gamma irradiation by WR-2721. Indian J Exp Biol. 1992;30:535-7. PubMed PMID: 1324228.
8
Momm F, Bechtold C, Fischer K, Tsekos A, Henke M. Alteration of radiation-induced hematotoxicity by amifostine (ethyol). Strahlenther Onkol. 1999;175:2-5. doi.org/10.1007/BF03215919. PubMed PMID: 10584132.
9
Soref CM, Hacker TA, Fahl WE. A new orally active, aminothiol radioprotector-free of nausea and hypotension side effects at its highest radioprotective doses. Int J Radiat Oncol Biol Phys. 2012;82:e701-7. doi.org/10.1016/j.ijrobp.2011.11.038. PubMed PMID: 22330992.
10
San Segundo CG, Manuel FAC. Radioprotectors. Revista de Oncología. 2002;4:277-83.
11
Xu P, Zhang WB, Cai XH, Lu DD, He XY, Qiu PY, et al. Flavonoids of Rosa roxburghii Tratt act as radioprotectors. Asian Pac J Cancer Prev. 2014;15:8171-5. doi.org/10.7314/APJCP.2014.15.19.8171. PubMed PMID: 25339001.
12
Kma L. Plant extracts and plant-derived compounds: promising players in a countermeasure strategy against radiological exposure. Asian Pac J Cancer Prev. 2014;15:2405-25. doi.org/10.7314/APJCP.2014.15.6.2405. PubMed PMID: 24761841.
13
Brizel DM, Wasserman TH, Henke M, Strnad V, Rudat V, Monnier A, et al. Phase III randomized trial of amifostine as a radioprotector in head and neck cancer. J Clin Oncol. 2000;18:3339-45. PubMed PMID: 11013273.
14
Yamini K, Gopal V. Natural radioprotective agents against ionizing radiation–an overview. International Journal of PharmTech Research. 2010;2:1421-6.
15
Baliga MS, Rao S. Radioprotective potential of mint: a brief review. J Cancer Res Ther. 2010;6:255-62. doi.org/10.4103/0973-1482.73336. PubMed PMID: 21119249.
16
G CJ. Radioprotective Potential of Plants and Herbs against the Effects of Ionizing Radiation. J Clin Biochem Nutr. 2007;40:74-81. doi.org/10.3164/jcbn.40.74. PubMed PMID: 18188408. PubMed PMCID: 2127223.
17
Chaterjee A, Pakrashi S. Annona squamosa in the treatise of Indian medicinal plants Publication and Information Directorate. New Delhi. 1995;130.
18
Bhattacharya S, Subramanian M, Roychowdhury S, Bauri AK, Kamat JP, Chattopadhyay S, et al. Radioprotective property of the ethanolic extract of Piper betel Leaf. J Radiat Res. 2005;46:165-71. doi.org/10.1269/jrr.46.165. PubMed PMID: 15988134.
19
Bhattacharjee S. Reactive oxygen species and oxidative burst: roles in stress, senescence and signal. Curr Sci. 2005;89:1113-21.
20
Zangeneh M, Mozdarani H, Mahmoudzadeh A, Aghamiri MR. Effects of famotidine and vitamin C on low dose radiation-induced micronuclei in mice bone marrow cells. Journal of Paramedical Sciences. 2015;5(4).
21
Xu P, Jiang EJ, Wen SY, Lu DD. Amentoflavone acts as a radioprotector for irradiated v79 cells by regulating reactive oxygen species (ROS), cell cycle and mitochondrial mass. Asian Pac J Cancer Prev. 2014;15:7521-6. doi.org/10.7314/APJCP.2014.15.18.7521. PubMed PMID: 25292022.
22
Mahdavi M, Mozdarani H. Protective effects of famotidine and vitamin C against radiation induced cellular damage in mouse spermatogenesis process. Iran J Radiat Res. 2011;8:223-30.
23
Mozdarani H, Salimi M, Froughizadeh M. Effect of cimetidine and famotidine on survival of lethally gamma irradiated mice. Iran J Radiat Res. 2008;5:187-94.
24
Du XX, Zhou YJ, Xu YH. Effects of histamine H2-receptor antagonists on hemopoietic reconstruction in bone marrow. Sheng Li Xue Bao. 1989;41:597-601. PubMed PMID: 2576333.
25
Hast R, Bernell P, Hansson M. Cimetidine as an immune response modifier. Med Oncol Tumor Pharmacother. 1989;6:111-3. PubMed PMID: 2657245.
26
Marshall ME, Conley D, Hollingsworth P, Brown S, Thompson JS. Effects of coumarin (1,2-benzopyrone) on lymphocyte, natural killer cell, and monocyte functions in vitro. J Biol Response Mod. 1989;8:70-85. PubMed PMID: 2921611.
27
Mozdarani H, Gharbali A. Radioprotective effects of cimetidine in mouse bone marrow cells exposed to gamma-rays as assayed by the micronucleus test. Int J Radiat Biol. 1993;64:189-94. doi.org/10.1080/09553009314551291. PubMed PMID: 8103543.
28
Mozdarani H, Khoshbin-Khoshnazar AR. In vivo protection by cimetidine against fast neutron-induced micronuclei in mouse bone marrow cells. Cancer Lett. 1998;124:65-71. doi.org/10.1016/S0304-3835(97)00451-5. PubMed PMID: 9500193.
29
Mozdarani H, J Vessal N. Cimetidine can modify the effects of whole body’y-irradiation on ly mphohem atopoietic system. Medical Journal of the Islamic Republic of Iran (MJIRI). 1993;7:95-9.
30
Lapenna D, De Gioia S, Mezzetti A, Grossi L, Festi D, Marzio L, et al. H2-receptor antagonists are scavengers of oxygen radicals. Eur J Clin Invest. 1994;24:476-81. doi.org/10.1111/j.1365-2362.1994.tb02378.x. PubMed PMID: 7957505.
31
Ardestani SK, Janlow MM, Tavakoli AKZ. Effect of cimetidine and ranitidine on lipid profile and lipid peroxidation in γ-irradiated mice. Acta Medica Iranica. 2004;42:198-204.
32
SeyedAlinaghi S, Paydary K, Emamzadeh-Fard S, Mohraz M. Treatment with IMODTM as a novel immune modulator in HIV positive patients. Journal of AIDS & Clinical Research. 2012;2012.
33
Mirzaee S, Drewniak A, Sarrami-Forooshani R, Kaptein TM, Gharibdoost F, Geijtenbeek TB. Herbal medicine IMOD suppresses LPS-induced production of proinflammatory cytokines in human dendritic cells. Front Pharmacol. 2015;6:64. doi.org/10.3389/fphar.2015.00064. PubMed PMID: 25870561. PubMed PMCID: 4375992.
34
Farhoudi M, Najafi-Nesheli M, Hashemilar M, Mahmoodpoor A, Sharifipour E, Baradaran B, et al. Effect of IMOD on the inflammatory process after acute ischemic stroke: a randomized clinical trial. Daru. 2013;21:26. doi.org/10.1186/2008-2231-21-26. PubMed PMID: 23514014. PubMed PMCID: 3620936.
35
Mohraz M, Sedaghat A, SeyedAlinaghi S, Asheri H, Mohammaddoust S, Gharibdoost F, et al. Post marketing surveillance on safety and efficacy of IMOD in Iranian patients with HIV/AIDS. Infect Disord Drug Targets. 2013;13:71-4. doi.org/10.2174/18715265112129990031. PubMed PMID: 23713668.
36
Arastoo M, Khorshid HRK, Radmanesh R, Gharibdoust F. Combination of IMOD™ and Arbidol to increase their immunomodulatory effects as a novel medicine to prevent and cure influenza and some other infectious diseases. Journal of Medical Hypotheses and Ideas. 2014;8:53-6. doi.org/10.1016/j.jmhi.2014.02.001.
37
Novitsky YA, Madani H, Gharibdoust F, Farhadi M, Farzamfar B, Mohraz M. Use of a combination of ethanolic rosa sp., urtica dioica and tanacetum vulgare extracts, further compromising selenium and urea and having been exposed to a pulsed electromagnetic field, for the preparation of a medicament for immunostimulation and/or treatment of hiv infections. Google Patents. 2009.
38
Schnittman SM, Greenhouse JJ, Psallidopoulos MC, Baseler M, Salzman NP, Fauci AS, et al. Increasing viral burden in CD4+ T cells from patients with human immunodeficiency virus (HIV) infection reflects rapidly progressive immunosuppression and clinical disease. Ann Intern Med. 1990;113:438-43. doi.org/10.7326/0003-4819-113-6-438. PubMed PMID: 1974752.
39
Ren F, Chen X, Hesketh J, Gan F, Huang K. Selenium promotes T-cell response to TCR-stimulation and ConA, but not PHA in primary porcine splenocytes. PLoS One. 2012;7:e35375. doi.org/10.1371/journal.pone.0035375. PubMed PMID: 22530011. PubMed PMCID: 3328446.
40
Rayman MP. The importance of selenium to human health. Lancet. 2000;356:233-41. doi.org/10.1016/S0140-6736(00)02490-9. PubMed PMID: 10963212.
41
Cicek E, Yildiz M, Delibas N, Bahceli S. The effects of 201Tl myocardial perfusion scintigraphy studies on oxidative damage in patients. West Indian Med J. 2009;58:50-3. PubMed PMID: 19565998.
42
Cicek E, Yildiz M, Delibas N, Bahceli S. The effects of thyroid scintigraphy studies on oxidative damage in patients. Acta Physiol Hung. 2006;93:131-6. doi.org/10.1556/APhysiol.93.2006.2-3.3. PubMed PMID: 17063624.
43
Ozturk P, Arican O, Belge Kurutas E, Karakas T, Kabakci B. Oxidative stress in patients with scalp seborrheic dermatitis. Acta Dermatovenerol Croat. 2013;21:80-5. PubMed PMID: 24001414.
44
Razzaghdoust A, Mozdarani H, Mofid B, Aghamiri SM, Heidari AH. Reduction in radiation-induced lymphocytopenia by famotidine in patients undergoing radiotherapy for prostate cancer. Prostate. 2014;74:41-7. doi.org/10.1002/pros.22725. PubMed PMID: 24019126.
45
Ghorbani M, Mozdarani H. In vitro radioprotective effects of histamine H2 receptor antagonists against gamma-rays induced chromosomal aberrations in human lymphocytes. Iran J Radiat Res. 2003;1:99-104.
46
Ching TL, Haenen GR, Bast A. Cimetidine and other H2 receptor antagonists as powerful hydroxyl radical scavengers. Chem Biol Interact. 1993;86:119-27. doi.org/10.1016/0009-2797(93)90116-G. PubMed PMID: 8095439.
47
Mozdarani H. Radioprotective properties of histamine H2 receptor antagonists: present and future prospects. J Radiat Res. 2003;44:145-9. doi.org/10.1269/jrr.44.145. PubMed PMID: 13678344.
48
Xian L, Lou M, Wu X, Yu B, Frassica F, Wan M, et al. Pretreatment with antioxidants prevent bone injury by improving bone marrow microenvironment for stem cells. Stem Cell Discovery. 2012;2:100.
49
SShajiei A, Saadati M, Khalil Bahmani M, Doroudian M. The Novel Study of IMOD TM against HIV-1, P24production. Int J Mol Clin Microbiol. 2011;1:60–4.
50
Nair CK, Parida DK, Nomura T. Radioprotectors in radiotherapy. J Radiat Res. 2001;42:21-37. doi.org/10.1269/jrr.42.21. PubMed PMID: 11393887.
51
Nair CK, Salvi V, Kagiya TV, Rajagopalan R. Relevance of radioprotectors in radiotherapy: studies with tocopherol monoglucoside. J Environ Pathol Toxicol Oncol. 2004;23:153-60. doi.org/10.1615/jenvpathtoxoncol.v23.i2.80. PubMed PMID: 15163294.
52
Weiss JF, Hoover RL, Kumar KS. Selenium pretreatment enhances the radioprotective effect and reduces the lethal toxicity of WR-2721. Free Radic Res Commun. 1987;3:33-8. doi.org/10.3109/10715768709069767. PubMed PMID: 2854528.
53
Patchen ML, MacVittie TJ, Weiss JF. Combined modality radioprotection: the use of glucan and selenium with WR-2721. Int J Radiat Oncol Biol Phys. 1990;18:1069-75. doi.org/10.1016/0360-3016(90)90442-M. PubMed PMID: 2161407.
54
Badiello R, Fielden EM. Pulse radiolysis of selenium-containing radioprotectors. I. Selenourea. Int J Radiat Biol Relat Stud Phys Chem Med. 1970;17:1-14. doi.org/10.1080/09553007014550011. PubMed PMID: 5309092.
55
Borek C, Ong A, Mason H, Donahue L, Biaglow JE. Selenium and vitamin E inhibit radiogenic and chemically induced transformation in vitro via different mechanisms. Proc Natl Acad Sci U S A. 1986;83:1490-4. doi.org/10.1073/pnas.83.5.1490. PubMed PMID: 3456598. PubMed PMCID: 323102.
56
Schüller P, Püttmann S, Micke O, Senner V, Schäfer U, Willich N. Selenium-a novel radiosensitizer? Increased radiation sensitivity in C6 rat glioma cells incubated with different concentrations of selenite. Trace Elements & Electrolytes. 2005;22. doi.org/10.5414/tep22201.
57
Micke O, Schomburg L, Buentzel J, Kisters K, Muecke R. Selenium in oncology: from chemistry to clinics. Molecules. 2009;14:3975-88. doi.org/10.3390/molecules14103975. PubMed PMID: 19924043.
58
Micke O, Schomburg L, Buentzel J. Selenium in oncology: from chemistry to clinics. Alternative Medicine Review. 2010;15:90-1.
59
Eroglu C, Unal D, Cetin A, Orhan O, Sivgin S, Ozturk A. Effect of serum selenium levels on radiotherapy-related toxicity in patients undergoing radiotherapy for head and neck cancer. Anticancer Res. 2012;32:3587-90. PubMed PMID: 22843950.
60
Kupper FC, Kloareg B, Guern J, Potin P. Oligoguluronates elicit an oxidative burst in the brown algal kelp Laminaria digitata. Plant Physiol. 2001;125:278-91. doi.org/10.1104/pp.125.1.278. PubMed PMID: 11154336. PubMed PMCID: 61009.
61
Joksic G, Ilic N, Spasojevic-Tisma V. Radioprotective properties of nutraceutical Gonebazol: In vivo study. Archive of Oncology. 2006;14:15. doi.org/10.2298/AOO0602015J.
62
Ikushima T, Mortazavi SJ. Radioadaptive response: its variability in cultured human lymphocytes. Biological Effects of Low Dose Radiation. Amsterdam: Elsevier. p. 2000:81-6.
63
Petrovic S, Leskovac A, Joksic G. Positive correlation between micronuclei and necrosis of lymphocytes in medical personnel occupationally exposed to ionizing radiation. Part of Oncology. 2005;13:65. doi.org/10.2298/aoo0502065p.
64
Shahidi M, Mozdarani H. Potent radioprotective effect of therapeutic doses of ranitidine and famotidine against gamma-rays induced micronuclei in vivo. Iran J Radiat Res. 2003;1:29-35.
65
Mozdarani H, Nasirian B, Haeri SA. In vivo gamma-rays induced initial DNA damage and the effect of famotidine in mouse leukocytes as assayed by the alkaline comet assay. J Radiat Res. 2007;48:129-34. doi.org/10.1269/jrr.06055. PubMed PMID: 17299251.
66
ORIGINAL_ARTICLE
“Triple M” Effect: A Proposed Mechanism to Explain Increased Dental Amalgam Microleakage after Exposure to Radiofrequency Electromagnetic Radiation
A large body of evidence now indicates that the amount of mercury released from dental amalgam fillings can be significantly accelerated by exposure to radiofrequency electromagnetic fields (RF-EMFs) such as common mobile phones and magnetic resonance imaging (MRI). Studies performed on the increased microleakage of dental amalgam restorations after exposure to RF-EMFs have further supported these findings. Although the accelerated microleakage induced by RF-EMFs is clinically significant, the entire mechanisms of this phenomenon are not clearly understood. In this paper, we introduce “Triple M” effect, a new evidence-based theory which can explain the accelerated microleakage of dental amalgam fillings after exposure to different sources of electromagnetic radiation. Based on this theory, there are saliva-filled tiny spaces between amalgam and the tooth. Exposure of the oral cavity to RF-EMFs increases the energy of these small amounts of saliva. Due to the small mass of saliva in these tiny spaces, a small amount of energy will be required for heating. Moreover, reflection of the radiofrequency radiation on the inner walls of the tiny spaces causes interference which in turn produces some “hot spots” in these spaces. Finally, formation of gas bubbles in response to increased temperature and very rapid expansion of these bubbles will accelerate the microleakage of amalgam. Experiments that confirm the validity of this theory are discussed.
https://jbpe.sums.ac.ir/article_43288_b3ea96e1e5b46e531ef1cb239c3cfff4.pdf
2018-03-01
141
146
Microleakage
Dental amalgam
Electromagnetic Fields
Triple M Effect
Gh
Mortazavi
mrtazaviqaz@gmail.com
1
Dentist, Ionizing and Non-ionizing Radiation Protection Research Center (INIRPRC), Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
S A R
Mortazavi
alirmortazavi@yahoo.com
2
Student Research Committee, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
A R
Mehdizadeh
3
Medical Physics Department, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
LEAD_AUTHOR
Mortazavi SM, Neghab M, Anoosheh SM, Bahaeddini N, Mortazavi G, Neghab P, et al. High-field MRI and mercury release from dental amalgam fillings. Int J Occup Environ Med. 2014;5:101-5. PubMed PMID: 24748001.
1
Mortazavi SM, Daiee E, Yazdi A, Khiabani K, Kavousi A, Vazirinejad R, et al. Mercury release from dental amalgam restorations after magnetic resonance imaging and following mobile phone use. Pak J Biol Sci. 2008;11:1142-6. doi.org/10.3923/pjbs.2008.1142.1146. PubMed PMID: 18819554.
2
WHO. Promoting the phase down approach of dental amalgam in developing countries. Geneva: World Health Organization; 2014.
3
Yilmaz S, Misirlioglu M. The effect of 3 T MRI on microleakage of amalgam restorations. Dentomaxillofac Radiol. 2013;42:20130072. doi.org/10.1259/dmfr.20130072. PubMed PMID: 23674614. PubMed PMCID: 3756742.
4
Shahidi SH, Bronoosh P, Alavi AA, Zamiri B, Sadeghi AR, Bagheri MH, et al. Effect of magnetic resonance imaging on microleakage of amalgam restorations: an in vitro study. Dentomaxillofac Radiol. 2009;38:470-4. doi.org/10.1259/dmfr/30077669. PubMed PMID: 19767518.
5
Kursun S, Öztas B, Atas H, Tastekin M. Effects of X-rays and magnetic resonance imaging on mercury release from dental amalgam into artificial saliva. Oral Radiology. 2014;30:142-6. doi.org/10.1007/s11282-013-0154-0.
6
Mortazavi G, Mortazavi SM. Increased mercury release from dental amalgam restorations after exposure to electromagnetic fields as a potential hazard for hypersensitive people and pregnant women. Rev Environ Health. 2015;30:287-92. doi.org/10.1515/reveh-2015-0017. PubMed PMID: 26544100.
7
Mortazavi SM, Motamedifar M, Namdari G, Taheri M, Mortazavi AR, Shokrpour N. Non-linear adaptive phenomena which decrease the risk of infection after pre-exposure to radiofrequency radiation. Dose Response. 2014;12:233-45. doi.org/10.2203/dose-response.12-055.Mortazavi. PubMed PMID: 24910582. PubMed PMCID: 4036396.
8
Mortazavi SM, Taeb S, Dehghan N. Alterations of visual reaction time and short term memory in military radar personnel. Iran J Public Health. 2013;42:428-35. PubMed PMID: 23785684. PubMed PMCID: 3684731.
9
Mortazavi SM, Rouintan MS, Taeb S, Dehghan N, Ghaffarpanah AA, Sadeghi Z, et al. Human short-term exposure to electromagnetic fields emitted by mobile phones decreases computer-assisted visual reaction time. Acta Neurol Belg. 2012;112:171-5. doi.org/10.1007/s13760-012-0044-y. PubMed PMID: 22426673.
10
Mortazavi S, Mosleh-Shirazi M, Tavassoli A, Taheri M, Mehdizadeh A, Namazi S, et al. Increased Radioresistance to Lethal Doses of Gamma Rays in Mice and Rats after Exposure to Microwave Radiation Emitted by a GSM Mobile Phone Simulator. Dose Response. 2013;11:281-92. doi.org/10.2203/dose-response.12-010.Mortazavi. PubMed PMID: 23930107. PubMed PMCID: 3682203.
11
Mortazavi S, Mosleh-Shirazi M, Tavassoli A, Taheri M, Bagheri Z, Ghalandari R, et al. A comparative study on the increased radioresistance to lethal doses of gamma rays after exposure to microwave radiation and oral intake of flaxseed oil. Iranian Journal of Radiation Research. 2011;9:9-14.
12
Mortavazi S, Habib A, Ganj-Karami A, Samimi-Doost R, Pour-Abedi A, Babaie A. Alterations in TSH and Thyroid Hormones following Mobile Phone Use. Oman Med J. 2009;24:274-8. doi.org/10.5001/omj.2009.56. PubMed PMID: 22216380. PubMed PMCID: 3243874.
13
Mortazavi SM, Daiee E, Yazdi A, Khiabani K, Kavousi A, Vazirinejad R, et al. Mercury release from dental amalgam restorations after magnetic resonance imaging and following mobile phone use. Pak J Biol Sci. 2008;11:1142-6. doi.org/10.3923/pjbs.2008.1142.1146. PubMed PMID: 18819554.
14
Mortazavi SM, Ahmadi J, Shariati M. Prevalence of subjective poor health symptoms associated with exposure to electromagnetic fields among university students. Bioelectromagnetics. 2007;28:326-30. doi.org/10.1002/bem.20305. PubMed PMID: 17330851.
15
Mortazavi S, Motamedifar M, Namdari G, Taheri M, Mortazavi A. Counterbalancing immunosuppression-induced infections during long-term stay of humans in space. Journal of Medical Hypotheses and Ideas. 2013;7:8-10.
16
Mortazavi S. Safety issue of mobile phone base stations. J Biomed Phys Eng. 2013;3:1-2.
17
Mortazavi S, Parsanezhad M, Kazempour M, Ghahramani P, Mortazavi A, Davari M. Male reproductive health under threat: Short term exposure to radiofrequency radiations emitted by common mobile jammers. J Hum Reprod Sci. 2013;6:124-8. doi.org/10.4103/0974-1208.117178. PubMed PMID: 24082653. PubMed PMCID: 3778601.
18
Rafati A, Rahimi S, Talebi A, Soleimani A, Haghani M, Mortazavi SM. Exposure to Radiofrequency Radiation Emitted from Common Mobile Phone Jammers Alters the Pattern of Muscle Contractions: an Animal Model Study. J Biomed Phys Eng. 2015;5:133-42. PubMed PMID: 26396969. PubMed PMCID: 4576874.
19
Shekoohi Shooli F, Mortazavi SA, Jarideh S, Nematollahii S, Yousefi F, Haghani M, et al. Short-Term Exposure to Electromagnetic Fields Generated by Mobile Phone Jammers Decreases the Fasting Blood Sugar in Adult Male Rats. J Biomed Phys Eng. 2016;6:27-32. PubMed PMID: 27026952. PubMed PMCID: 4795326.
20
Mortazavi SMJ, Tavassoli A, Ranjbari F, Moammaiee P. Effects of laptop computers’ electromagnetic field on sperm quality. Journal of Reproduction & Infertility. 2010;11(4).
21
Mortazavi SM, Vazife-Doost S, Yaghooti M, Mehdizadeh S, Rajaie-Far A. Occupational exposure of dentists to electromagnetic fields produced by magnetostrictive cavitrons alters the serum cortisol level. J Nat Sci Biol Med. 2012;3:60-4. doi.org/10.4103/0976-9668.95958. PubMed PMID: 22690053. PubMed PMCID: 3361780.
22
Mortazavi G, Haghani M, Rastegarian N, Zarei S, Mortazavi SMJ. Increased Release of Mercury from Dental Amalgam Fillings due to Maternal Exposure to Electromagnetic Fields as a Possible Mechanism for the High Rates of Autism in the Offspring: Introducing a Hypothesis. Journal of Biomedical Physics & Engineering. 2016;6(1):41-46. PMCID: PMC4795328.
23
Mahmoudi R, Mortazavi S, Safari S, Nikseresht M, Mozdarani H, Jafari M, et al. Effects of microwave electromagnetic radiations emitted from common Wi-Fi routers on rats’ sperm count and motility. Int J Radiat Res. 2015;13:363-8.
24
Haghnegahdar A, Khosrovpanah H, Andisheh-Tadbir A, Mortazavi G, Saeedi Moghadam M, Mortazavi S, et al. Design and fabrication of helmholtz coils to study the effects of pulsed electromagnetic fields on the healing process in periodontitis: preliminary animal results. J Biomed Phys Eng. 2014;4:83-90. PubMed PMID: 25505775. PubMed PMCID: 4258865.
25
Paknahad M, Shahidi S, Mortazavi SMJ, Mortazavi G, Moghadam MS, Nazhvani AD. The Effect of Pulsed Electromagnetic Fields on Microleakage of Amalgam Restorations: An in Vitro Study. Shiraz E-Medical Journal. 2016;17(2).
26
Vanishree HS, Shanthala BM, Bobby W. The comparative evaluation of fracture resistance and microleakage in bonded amalgam, amalgam, and composite resins in primary molars. Indian J Dent Res. 2015;26:446-50. doi.org/10.4103/0970-9290.172019. PubMed PMID: 26672412.
27
Kappe C, Dallinger D, Murphree S. Practical Microwave Synthesis for Organic Chemists 2009. Germany, Wiley-VCH: Weinheim; 2009.
28
Regier M, Schubert H, Knoerzer K. TThe microwave processing of foods. Toronto: Elsevier; 2005.
29
ORIGINAL_ARTICLE
A New Stethoscope Design with Unique Characteristics and Development in Medical Device
As regards the significant role of stethoscopes in the diagnosis of congenital and adventitious heart diseases and prevention of irreparable complications of these diseases, the quality of hearing sound of these stethoscopes by a physician has a significant impact on the disease diagnosis. This device plays an important role in the early diagnosis of congenital heart and respiratory diseases and provides this feasibility since birth. Also, the importance of this device performance in the diagnosis of heart, cardiovascular and respiratory diseases at different age periods is not a secret. This new invented device, in comparison to a variety of available stethoscopes in the field of diagnosis, is capable of hearing the sound of a very high quality and cancelling the noise of sound that sometimes leads to wrong diagnosis or misdiagnosis. This new invented stethoscope is approved by cardiologists, lung and Infectious disease specialists as well as being registered under No. 78382 in Patent Islamic Republic of Iran.
https://jbpe.sums.ac.ir/article_43291_1a0d418631c8814456185f67daec04b2.pdf
2018-03-01
147
150
Stethoscope
Cardiovascular diseases
Hearing
Invention
Noise
M
Ghahremanifar
dr_karim56@yahoo.com
1
Faculty of Medicine, Yasuj University of Medical Sciences. Yasuj, Iran
AUTHOR
M
Haghani
2
Ionizing and Non-Ionizing Radiation Protection Research Center (INIRPRC), Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
A
Ghadimi Moghadam
alimoghadam1997@yahoo.com
3
Faculty of Medicine, Szeged University, Szeged, Hungary
AUTHOR
A K
Ghadimi Moghadam
4
Pediatric Infectious Ward, Yasuj University of Medical Sciences, Yasuj, Iran
LEAD_AUTHOR
Jay V. The legacy of Laennec. Arch Pathol Lab Med. 2000;124:1420-1. PubMed PMID: 11035568.
1
Roguin A. Rene Theophile Hyacinthe Laennec (1781-1826): the man behind the stethoscope. Clin Med Res. 2006;4:230-5. doi.org/10.3121/cmr.4.3.230. PubMed PMID: 17048358. PubMed PMCID: 1570491.
2
Leng S, San Tan R, Chai KTC, Wang C, Ghista D, Zhong L. The electronic stethoscope. Biomedical engineering online. 2015;14:66. doi.org/10.1186/s12938-015-0056-y.
3
Chizner MA. Cardiac auscultation: rediscovering the lost art. Curr Probl Cardiol. 2008;33:326-408. doi.org/10.1016/j.cpcardiol.2008.03.003. PubMed PMID: 18513577.
4
ORIGINAL_ARTICLE
Cancers of the Brain and CNS: Global Patterns and Trends in Incidence
Miranda-Filho et al. in their recently published paper entitled “Cancers of the brain and CNS: global patterns and trends in incidence†provided a global status report of the geographic and temporal variations in the incidence of brain and CNS cancers in different countries across continents worldwide. While the authors confirm the role of genetic risk factors and ionizing radiation exposures, they claimed that no firm conclusion could be drawn about the role of exposure to non-ionizing radiation. The paper authored by Miranda-Filho et al. not only addresses a challenging issue, it can be considered as a good contribution in the field of brain and CNS cancers. However, our correspondence addresses a basic shortcoming of this paper about the role of electromagnetic fields and cancers and provides evidence showing that exposure to radiofrequency electromagnetic fields (RF-EMFs), at least at high levels and long durations, can increases the risk of cancer.
https://jbpe.sums.ac.ir/article_43292_97d89badf8f29f3b3c6d192a6bc276f4.pdf
2018-03-01
151
152
Mobile Phones
cancer
Radiofrequency (RF)
Electromagnetic Fields (EMFs)
S M J
Mortazavi
mortazavismj@gmail.com
1
Fox Chase Cancer Center, Philadelphia, PA 19111, USA Email: s.m.javad.mortazavi@fccc.edu
AUTHOR
S A R
Mortazavi
alirmortazavi@yahoo.com
2
Student of Research Committee, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
AUTHOR
M
Paknahad
paknahadmaryam@yahoo.com
3
Assistant Professor of Oral and Maxillofacial Radiology, School of Dentistry, Shiraz University of Medical Sciences, Shiraz, Iran
LEAD_AUTHOR
Miranda-Filho A, Pineros M, Soerjomataram I, Deltour I, Bray F. Cancers of the brain and CNS: global patterns and trends in incidence. Neuro Oncol. 2017;19:270-80. Doi:10.1093/neuonc/now166. PubMed PMID: 27571887.
1
Wyde M, Cesta M, Blystone C, Elmore S, Foster P, Hooth M, et al. Report of Partial findings from the National Toxicology Program Carcinogenesis Studies of Cell Phone Radiofrequency Radiation in Hsd: Sprague Dawley® SD rats (Whole Body Exposure). bioRxiv. 2016:055699. doi: 10.1101/055699.
2
Bortkiewicz A, Gadzicka E, Szymczak W. Mobile phone use and risk for intracranial tumors and salivary gland tumors - A meta-analysis. Int J Occup Med Environ Health. 2017;30:27-43. doi.org/10.13075/ijomeh.1896.00802. PubMed PMID: 28220905.
3
Wang Y, Guo X. Meta-analysis of association between mobile phone use and glioma risk. J Cancer Res Ther. 2016;12:C298-C300. doi.org/10.4103/0973-1482.200759. PubMed PMID: 28230042.
4
Yakymenko I, Sidorik E, Kyrylenko S, Chekhun V. Long-term exposure to microwave radiation provokes cancer growth: evidences from radars and mobile communication systems. Exp Oncol. 2011;33:62-70. PubMed PMID: 21716201.
5
Carlberg M, Koppel T, Ahonen M, Hardell L. Case-control study on occupational exposure to extremely low-frequency electromagnetic fields and glioma risk. Am J Ind Med. 2017;60:494-503. doi.org/10.1002/ajim.22707. PubMed PMID: 28394434.
6
de Vocht F. Inferring the 1985-2014 impact of mobile phone use on selected brain cancer subtypes using Bayesian structural time series and synthetic controls. Environ Int. 2016;97:100-7. doi.org/10.1016/j.envint.2016.10.019. PubMed PMID: 27835750.
7
Carlberg M, Hedendahl L, Ahonen M, Koppel T, Hardell L. Increasing incidence of thyroid cancer in the Nordic countries with main focus on Swedish data. BMC Cancer. 2016;16:426. doi.org/10.1186/s12885-016-2429-4. PubMed PMID: 27388603. PubMed PMCID: 4937579.
8