Keywords:
Not applicable, Image verification, Screening, CT-Quantitative, CT, Radiation physics, Liver, Abdomen
Authors:
K. Kitazawa, T. Kitamura, A. Matuda, K. Togashi; Kyoto/JP
DOI:
10.26044/ecr2020/C-01979
References
[1] Samei E, Richard S. et al. Assessment of the dose reduction potential of a model- based iterative reconstruction algorithm using a task-based performance metrology. Med Phys. 2015;42(1):314-323.
[2] Higaki T, Nakamura Y, et al. Deep Learning Reconstruction at CT: Phantom Study of the Image Characteristics. Academic Radiology 2020;27(1):82-87.
[3] Akagi M, Nakamura Y, et al. Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT
[4] Higaki T, Nishimaru E, et al. Radiation dose reduction in CT using Deep Learning based Reconstruction (DLR): A phantom study. ECR 2018;C-1656
[5] Urikura A, Hara T, et al. Objective assessment of low-contrast computed tomography images with iterative reconstruction. Physica Medica 2016; 32(8): 992-998
[6] Ichikawa K, CTmeasure, Japanese society of CT technology, Kasumi, Minami-ku, Hiroshima, JPN, http://www.jsct-tech.org/, 2012-2014