Sparse-view computed tomography (CT) becomes a major concern in the medical imaging field due to its reduced X-ray radiation dose. Recently, various convolutional neural network (CNN)-based approaches have been proposed, requiring the pairs of full and sparse-view CT images for network training. However, these paired data acquisition is impractical or difficult in clinical practice. To handle this problem, we propose the weakly-supervised learning for streak artifact reduction with unpaired sparse-view CT data. For CNN training dataset, we generate the pairs of input and target images from the given sparse-view CT data. Then, we iteratively apply the trained network to given sparse-view CT images and acquire the prior images. As the success factor of our novel framework, we estimate the original streak artifacts in the given sparse-view CT images from the prior images and subtract the estimated streak artifacts from the given sparse-view CT images. As a result, the proposed method has the best performance of lesion detection compared to the other methods.
|Title of host publication||7th International Conference on Image Formation in X-Ray Computed Tomography|
|Editors||Joseph Webster Stayman|
|Publication status||Published - 2022|
|Event||7th International Conference on Image Formation in X-Ray Computed Tomography - Virtual, Online|
Duration: 2022 Jun 12 → 2022 Jun 16
|Name||Proceedings of SPIE - The International Society for Optical Engineering|
|Conference||7th International Conference on Image Formation in X-Ray Computed Tomography|
|Period||22/6/12 → 22/6/16|
Bibliographical noteFunding Information:
This research was supported by the Bio and Medical Technology Development Program of the National Research Foundation (NRF) funded by the Ministry of Science and ICT (NRF2019R1A2C2084936 and 2020R1A4A1016619) and the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health and Welfare, Republic of Korea, the Ministry of Food and Drug Safety) (202011A03)..
© 2022 SPIE.
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials
- Condensed Matter Physics
- Computer Science Applications
- Applied Mathematics
- Electrical and Electronic Engineering