[SPECT] 5. Application of Artificial Intelligence in Nuclear Medicine for SPECT
Author:
Affiliation:
1. Department of Radiology, Chiba University Hospital
Publisher
Japanese Society of Radiological Technology
Subject
General Medicine
Link
https://www.jstage.jst.go.jp/article/jjrt/78/10/78_2022-2096/_pdf
Reference25 articles.
1. 1） Kim K, Lee Y. Improvement of signal and noise performance using single image super-resolution based on deep learning in single photon-emission computed tomography imaging system. Nucl Eng 2021; 53(7): 2341–2347.
2. 2） Cheng Z, Wen J, Zhang J, et al. Super-resolution reconstruction for parallel-beam SPECT based on deep learning and transfer learning: a preliminary simulation study. Ann Transl Med 2022; 10(7): 396.
3. 3） Ramon AJ, Yang Y, Pretorius PH, et al. Improving diagnostic accuracy in low-dose SPECT myocardial perfusion imaging with convolutional denoising networks. IEEE Trans Med Imaging 2020; 39(9): 2893–2903.
4. 4） Song C, Yang Y, Wernick MN, et al. Low-dose cardiac-gated spect via a spatiotemporal convolutional neural network. 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) 2020; 814–817.
5. 5） Shiri I, AmirMozafari Sabet K, Arabi H, et al. Standard SPECT myocardial perfusion estimation from half-time acquisitions using deep convolutional residual neural networks. J Nucl Cardiol 2021; 28(6): 2761–2779.
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