SS-CTML: Self-Supervised Cross-Task Mutual Learning for CT Image Reconstruction
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arXiv
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| Hauptverfasser: | , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910771282182144 |
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| author | Chen, Gaofeng Zhang, Yaoduo Huang, Li Wang, Pengfei Zhang, Wenyu Zeng, Dong Ma, Jianhua He, Ji |
| author_facet | Chen, Gaofeng Zhang, Yaoduo Huang, Li Wang, Pengfei Zhang, Wenyu Zeng, Dong Ma, Jianhua He, Ji |
| contents | Supervised deep-learning (SDL) techniques with paired training datasets have been widely studied for X-ray computed tomography (CT) image reconstruction. However, due to the difficulties of obtaining paired training datasets in clinical routine, the SDL methods are still away from common uses in clinical practices. In recent years, self-supervised deep-learning (SSDL) techniques have shown great potential for the studies of CT image reconstruction. In this work, we propose a self-supervised cross-task mutual learning (SS-CTML) framework for CT image reconstruction. Specifically, a sparse-view scanned and a limited-view scanned sinogram data are first extracted from a full-view scanned sinogram data, which results in three individual reconstruction tasks, i.e., the full-view CT (FVCT) reconstruction, the sparse-view CT (SVCT) reconstruction, and limited-view CT (LVCT) reconstruction. Then, three neural networks are constructed for the three reconstruction tasks. Considering that the ultimate goals of the three tasks are all to reconstruct high-quality CT images, we therefore construct a set of cross-task mutual learning objectives for the three tasks, in which way, the three neural networks can be self-supervised optimized by learning from each other. Clinical datasets are adopted to evaluate the effectiveness of the proposed framework. Experimental results demonstrate that the SS-CTML framework can obtain promising CT image reconstruction performance in terms of both quantitative and qualitative measurements. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_01456 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SS-CTML: Self-Supervised Cross-Task Mutual Learning for CT Image Reconstruction Chen, Gaofeng Zhang, Yaoduo Huang, Li Wang, Pengfei Zhang, Wenyu Zeng, Dong Ma, Jianhua He, Ji Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Supervised deep-learning (SDL) techniques with paired training datasets have been widely studied for X-ray computed tomography (CT) image reconstruction. However, due to the difficulties of obtaining paired training datasets in clinical routine, the SDL methods are still away from common uses in clinical practices. In recent years, self-supervised deep-learning (SSDL) techniques have shown great potential for the studies of CT image reconstruction. In this work, we propose a self-supervised cross-task mutual learning (SS-CTML) framework for CT image reconstruction. Specifically, a sparse-view scanned and a limited-view scanned sinogram data are first extracted from a full-view scanned sinogram data, which results in three individual reconstruction tasks, i.e., the full-view CT (FVCT) reconstruction, the sparse-view CT (SVCT) reconstruction, and limited-view CT (LVCT) reconstruction. Then, three neural networks are constructed for the three reconstruction tasks. Considering that the ultimate goals of the three tasks are all to reconstruct high-quality CT images, we therefore construct a set of cross-task mutual learning objectives for the three tasks, in which way, the three neural networks can be self-supervised optimized by learning from each other. Clinical datasets are adopted to evaluate the effectiveness of the proposed framework. Experimental results demonstrate that the SS-CTML framework can obtain promising CT image reconstruction performance in terms of both quantitative and qualitative measurements. |
| title | SS-CTML: Self-Supervised Cross-Task Mutual Learning for CT Image Reconstruction |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2501.01456 |