Journal of Biomedical Physics and Engineering

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Staff Members

Editorial Board

Publication Ethics

Indexing and Abstracting

Related Links

FAQ

Peer Review Process

News

A Study on Robustness of Various Deformable Image Registration Algorithms on Image Reconstruction Using 4DCT Thoracic Images

Document Type : Original Research

Authors

1 MSc, Faculty of Sciences and Modern Technologies Graduate University of Advanced Technology Haftbagh St. Kerman Iran

2 PhD, Faculty of Sciences and Modern Technologies Graduate University of Advanced Technology Haftbagh St. Kerman Iran

10.31661/jbpe.v0i0.377

Abstract

Background: Medical image interpolation is recently introduced as a helpful tool to obtain further information via initial available images taken by tomography systems. To do this, deformable image registration algorithms are mainly utilized to perform image interpolation using tomography images.
Materials and Methods: In this work, 4DCT thoracic images of five real patients provided by DIR-lab group were utilized. Four implemented registration algorithms as 1) Original Horn-Schunck, 2) Inverse consistent Horn-Schunck, 3) Original Demons and 4) Fast Demons were implemented by means of DIRART software packages. Then, the calculated vector fields are processed to reconstruct 4DCT images at any desired time using optical flow based on interpolation method. As a comparative study, the accuracy of interpolated image obtained by each strategy is measured by calculating mean square error between the interpolated image and real middle image as ground truth dataset.
Results: Final results represent the ability to accomplish image interpolation among given two-paired images. Among them, Inverse Consistent Horn-Schunck algorithm has the best performance to reconstruct interpolated image with the highest accuracy while Demons method had the worst performance.
Conclusion: Since image interpolation is affected by increasing the distance between two given available images, the performance accuracy of four different registration algorithms is investigated concerning this issue. As a result, Inverse Consistent Horn-Schunck does not essentially have the best performance especially in facing large displacements happened due to distance increment.

Keywords

References
  1. Ouksili Z, Batatia H, editors. 4D CT image reconstruction based on interpolated optical flow fields. Image Processing (ICIP), 2010 17th IEEE International Conference on; 2010: IEEE.
  2. Xing L, Thorndyke B, Schreibmann E, Yang Y, Li TF, Kim GY, et al. Overview of image-guided radiation therapy. Med Dosim. 2006;31:91-112. doi.org/10.1016/j.meddos.2005.12.004. PubMed PMID:16690451.
  3. Chen GT, Kung JH, Beaudette KP. Artifacts in computed tomography scanning of moving objects. Semin Radiat Oncol. 2004;14:19-26. doi.org/10.1053/j.semradonc.2003.10.004. PubMed PMID: 14752730.
  4. Ehrhardt J, Säring D, Handels H, editors. Optical flow based interpolation of temporal image sequences. International Society for Optics and Photonics, Medical Imaging; 2006.
  5. Goshtasby A, Turner DA, Ackerman LV. Matching of tomographic slices for interpolation. IEEE Trans Med Imaging. 1992;11:507-16. doi.org/10.1109/42.192686. PubMed PMID:18222892.
  6. Penney GP, Schnabel JA, Rueckert D, Viergever MA, Niessen WJ. Registration-based interpolation. IEEE Trans Med Imaging. 2004;23:922-6. doi.org/10.1109/TMI.2004.828352. PubMed PMID: 15250644.
  7. Schreibmann E, Chen GT, Xing L. Image interpolation in 4D CT using a BSpline deformable registration model. Int J Radiat Oncol Biol Phys. 2006;64:1537-50. doi.org/10.1016/j.ijrobp.2005.11.018. PubMed PMID: 16503382.
  8. Yang D, Lu W, Low DA, Deasy JO, Hope AJ, El Naqa I. 4D-CT motion estimation using deformable image registration and 5D respiratory motion modeling. Med Phys. 2008;35:4577-90. doi.org/10.1118/1.2977828. PubMed PMID: 18975704. PubMed PMCID: 2673589.
  9. Keall P. 4-dimensional computed tomography imaging and treatment planning. Semin Radiat Oncol. 2004;14:81-90. doi.org/10.1053/j.semradonc.2003.10.006. PubMed PMID: 14752736.
  10. Low DA, Nystrom M, Kalinin E, Parikh P, Dempsey JF, Bradley JD, et al. A method for the reconstruction of four-dimensional synchronized CT scans acquired during free breathing. Med Phys. 2003;30:1254-63. doi.org/10.1118/1.1576230. PubMed PMID: 12852551.
  11. Vedam SS, Keall PJ, Kini VR, Mostafavi H, Shukla HP, Mohan R. Acquiring a four-dimensional computed tomography dataset using an external respiratory signal. Phys Med Biol. 2003;48:45-62. doi.org/10.1088/0031-9155/48/1/304. PubMed PMID: 12564500.
  12. Brown LG. A survey of image registration techniques. ACM computing surveys (CSUR). 1992;24:325-76. doi.org/10.1145/146370.146374.
  13. Hill DL, Batchelor PG, Holden M, Hawkes DJ. Medical image registration. Physics in medicine and biology. 2001;46:R1. doi.org/10.1088/0031-9155/46/3/201.
  14. Lester H, Arridge SR. A survey of hierarchical non-linear medical image registration. Pattern recognition. 1999;32:129-49. doi.org/10.1016/S0031-3203(98)00095-8.
  15. 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.
  16. Maurer CR, Fitzpatrick JM. A review of medical image registration. Interactive image-guided neurosurgery. 1993;17.
  17. Oliveira FP, Tavares JM. Medical image registration: a review. Comput Methods Biomech Biomed Engin. 2014;17:73-93. doi.org/10.1080/10255842.2012.670855. PubMed PMID: 22435355.
  18. Pluim JP, Maintz JB, Viergever MA. Mutual-information-based registration of medical images: a survey. IEEE Trans Med Imaging. 2003;22:986-1004. doi.org/10.1109/TMI.2003.815867. PubMed PMID: 12906253.
  19. Zitova B, Flusser J. Image registration methods: a survey. Image and vision computing. 2003;21:977-1000. doi.org/10.1016/S0262-8856(03)00137-9.
  20. Pace DF, Aylward SR, Niethammer M. A locally adaptive regularization based on anisotropic diffusion for deformable image registration of sliding organs. IEEE Trans Med Imaging. 2013;32:2114-26. doi.org/10.1109/TMI.2013.2274777. PubMed PMID: 23899632. PubMed PMCID: 4112204.
  21. Papież BW, Heinrich MP, Risser L, Schnabel JA. Complex lung motion estimation via adaptive bilateral filtering of the deformation field. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013: Springer; 2013. p. 25-32.
  22. Risser L, Vialard FX, Baluwala HY, Schnabel JA. Piecewise-diffeomorphic image registration: application to the motion estimation between 3D CT lung images with sliding conditions. Med Image Anal. 2013;17:182-93. doi.org/10.1016/j.media.2012.10.001. PubMed PMID: 23177000.
  23. Yang D, Brame S, El Naqa I, Aditya A, Wu Y, Goddu SM, et al. Technical note: DIRART--A software suite for deformable image registration and adaptive radiotherapy research. Med Phys. 2011;38:67-77. doi.org/10.1118/1.3521468. PubMed PMID: 21361176. PubMed PMCID: 3017581.
  24. Shirato H, Seppenwoolde Y, Kitamura K, Onimura R, Shimizu S. Intrafractional tumor motion: lung and liver. Semin Radiat Oncol. 2004;14:10-8. doi.org/10.1053/j.semradonc.2003.10.008. PubMed PMID: 14752729.
  25. Sarrut D. Deformable registration for image-guided radiation therapy. Z Med Phys. 2006;16:285-97. doi.org/10.1078/0939-3889-00327. PubMed PMID: 17216754.
  26. Sotiras A, Davatzikos C, Paragios N. Deformable medical image registration: a survey. IEEE Trans Med Imaging. 2013;32:1153-90. doi.org/10.1109/TMI.2013.2265603. PubMed PMID: 23739795. PubMed PMCID: 3745275.
  27. Barron JL, Fleet DJ, Beauchemin SS. Performance of optical flow techniques. International journal of computer vision. 1994;12:43-77. doi.org/10.1007/BF01420984.
  28. Beauchemin SS, Barron JL. The computation of optical flow. ACM Computing Surveys (CSUR). 1995;27:433-66. doi.org/10.1145/212094.212141.
  29. Horn BK, Schunck BG, editors. Determining optical flow. 1981 Technical symposium east; 1981: International Society for Optics and Photonics.
  30. Thirion JP. Image matching as a diffusion process: an analogy with Maxwell’s demons. Med Image Anal. 1998;2:243-60. doi.org/10.1016/S1361-8415(98)80022-4. PubMed PMID: 9873902.
  31. Gu X, Pan H, Liang Y, Castillo R, Yang D, Choi D, et al. Implementation and evaluation of various demons deformable image registration algorithms on a GPU. Phys Med Biol. 2010;55:207-19. doi.org/10.1088/0031-9155/55/1/012. PubMed PMID: 20009197.
  32. Wang H, Dong L, O’Daniel J, Mohan R, Garden AS, Ang KK, et al. Validation of an accelerated ‘demons’ algorithm for deformable image registration in radiation therapy. Phys Med Biol. 2005;50:2887-905. doi.org/10.1088/0031-9155/50/12/011. PubMed PMID: 15930609.
  33. Yang D, Li H, Low DA, Deasy JO, El Naqa I. A fast inverse consistent deformable image registration method based on symmetric optical flow computation. Phys Med Biol. 2008;53:6143-65. doi.org/10.1088/0031-9155/53/21/017. PubMed PMID: 18854610. PubMed PMCID: 3915046.
  34. Chen M, Lu W, Chen Q, Ruchala KJ, Olivera GH. A simple fixed-point approach to invert a deformation field. Med Phys. 2008;35:81-8. doi.org/10.1118/1.2816107. PubMed PMID: 18293565
Journal of Biomedical Physics and Engineering
Volume 9, Issue 5
35
September and October 2019
Pages 559-568
Files
Share
How to cite
Statistics