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2 results

5 - Deep Learning for CT Image Reconstruction
from Part II - Deep-Learning Architecture for Various Imaging Architectures
- By Haimiao Zhang, Bin Dong, Ge Wang, Baodong Liu
Edited by Jong Chul Ye, Korea Advanced Institute of Science and Technology (KAIST), Yonina C. Eldar, Weizmann Institute of Science, Israel, Michael Unser, École Polytechnique Fédérale de Lausanne
Book:

Deep Learning for Biomedical Image Reconstruction

Published online:

15 September 2023

Print publication:

12 October 2023, pp 89-113
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Summary

In this chapter, we review largely targeted tasks in the computed tomography (CT) literature, including low-dose CT, sparse-view CT, limited angle CT, interior CT, etc. We present deep-learning-based methods which operate as image post-processing techniques or raw-to-image mapping techniques.

6 - Deep Learning in CT Reconstruction: Bringing the Measured Data to Tasks
from Part II - Deep-Learning Architecture for Various Imaging Architectures
- By Guang-Hong Chen, Chengzhu Zhang, Yinsheng Li, Yoseob Han, Jong Chul Ye
Edited by Jong Chul Ye, Korea Advanced Institute of Science and Technology (KAIST), Yonina C. Eldar, Weizmann Institute of Science, Israel, Michael Unser, École Polytechnique Fédérale de Lausanne
Book:

Deep Learning for Biomedical Image Reconstruction

Published online:

15 September 2023

Print publication:

12 October 2023, pp 114-165
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Summary

In this chapter, we show that image-domain deep-learning-only reconstruction methods have intrinsic limitations in reconstruction accuracy and generalizability to individual patients owing to the regressive nature of the method. The combination of deep learning methods with analytic reconstruction methods or statistical IR methods offers a promising opportunity to achieve personalized reconstruction with improved reconstruction accuracy and enhanced generalizability.

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2 results

5 - Deep Learning for CT Image Reconstruction

Summary

6 - Deep Learning in CT Reconstruction: Bringing the Measured Data to Tasks

Summary