[CT] 6. The Current Situation of AI Image Reconstruction in CT
Author:
Affiliation:
1. Faculty of Health Sciences, Institute of Medical, Pharmaceutical and Health Sciences, Kanazawa University
Publisher
Japanese Society of Radiological Technology
Link
https://www.jstage.jst.go.jp/article/jjrt/80/2/80_2024-2321/_pdf
Reference29 articles.
1. 1） Koetzier LR, Mastrodicasa D, Szczykutowicz TP, et al. Deep learning image reconstruction for CT: technical principles and clinical prospects. Radiology 2023; 306(3): e221257.
2. 2） Nagayama Y, Sakabe D, Goto M, et al. Deep learning-based reconstruction for lower-dose pediatric CT: technical principles, image characteristics, and clinical implementations. Radiographics 2021; 41(7): 1936–1953.
3. 3） Ichikawa K, Kawashima H, Shimada M, et al. A three-dimensional cross-directional bilateral filter for edge-preserving noise reduction of low-dose computed tomography images. Comput Biol Med 2019; 111: 103353.
4. 4） Higaki T, Nakamura Y, Zhou J, et al. Deep learning reconstruction at CT: phantom study of the image characteristics. Acad Radiol 2020; 27(1): 82–87.
5. 5） Kawashima H, Ichikawa K, Takata T, et al. Performance of clinically available deep learning image reconstruction in computed tomography: a phantom study. J Med Imaging (Bellingham) 2020; 7(6): 063503.
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