Document Type: Original Research


1 PhD, Department of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Jl. Prof. Soedarto SH, Tembalang, Semarang 50275, Central Java, Indonesia

2 PhD, Department of Physics, Faculty of Mathematics and Natural Sciences, Bandung Institute of Technology, Ganesha 10, Bandung 40132, West Java, Indonesia

3 MSc, Department of Physics, Faculty of Mathematics and Natural Sciences, Bandung Institute of Technology, Ganesha 10, Bandung 40132, West Java, Indonesia

4 PhD, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan

5 PhD, Department of Applied Physics and Medical Imaging, California State University Channel Islands, Camarillo, CA 93012, USA


Background: It is necessary to have an automated noise measurement system working accurately to optimize dose in computerized tomography (CT) examinations.
Objective: This study aims to develop an algorithm to automate noise measurement that can be implemented in CT images of all body regions.
Materials and Methods: In this retrospective study, our automated noise measurement method consists of three steps as follows: the first is segmenting the image of the patient. The second is developing a standard deviation (SD) map by calculating the SD value for each pixel with a sliding window operation. The third step is estimating the noise as the smallest SD from the SD map. The proposed method was applied to the images of a homogenous phantom and a full body adult anthropomorphic phantom, and retrospectively applied to 27 abdominal images of patients.
Results: For a homogeneous phantom, the noises calculated using our proposed and previous algorithms have a linear correlation with R2 = 0.997. It is found that the noise magnitude closely follows the magnitude of the water equivalent diameter (Dw) in all body regions. The proposed algorithm is able to distinguish the noise magnitude due to variations in tube currents and different noise suppression techniques such as strong, standard, mild, and weak ones in a reconstructed image using the AIDR 3D algorithm.
Conclusion: An automated noise calculation has been proposed and successfully implemented in all body regions. It is not only accurate and easy to implement but also not influenced by the subjectivity of user.


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