Are Pixel-Wise Metrics Reliable for Sparse-View Computed Tomography Reconstruction?
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915578313179136 |
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| author | Lin, Tianyu Li, Xinran Zhuang, Chuntung Chen, Qi Cai, Yuanhao Ding, Kai Yuille, Alan L. Zhou, Zongwei |
| author_facet | Lin, Tianyu Li, Xinran Zhuang, Chuntung Chen, Qi Cai, Yuanhao Ding, Kai Yuille, Alan L. Zhou, Zongwei |
| contents | Widely adopted evaluation metrics for sparse-view CT reconstruction--such as Structural Similarity Index Measure and Peak Signal-to-Noise Ratio--prioritize pixel-wise fidelity but often fail to capture the completeness of critical anatomical structures, particularly small or thin regions that are easily missed. To address this limitation, we propose a suite of novel anatomy-aware evaluation metrics designed to assess structural completeness across anatomical structures, including large organs, small organs, intestines, and vessels. Building on these metrics, we introduce CARE, a Completeness-Aware Reconstruction Enhancement framework that incorporates structural penalties during training to encourage anatomical preservation of significant structures. CARE is model-agnostic and can be seamlessly integrated into analytical, implicit, and generative methods. When applied to these methods, CARE substantially improves structural completeness in CT reconstructions, achieving up to +32% improvement for large organs, +22% for small organs, +40% for intestines, and +36% for vessels. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_02093 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Are Pixel-Wise Metrics Reliable for Sparse-View Computed Tomography Reconstruction? Lin, Tianyu Li, Xinran Zhuang, Chuntung Chen, Qi Cai, Yuanhao Ding, Kai Yuille, Alan L. Zhou, Zongwei Image and Video Processing Computer Vision and Pattern Recognition Widely adopted evaluation metrics for sparse-view CT reconstruction--such as Structural Similarity Index Measure and Peak Signal-to-Noise Ratio--prioritize pixel-wise fidelity but often fail to capture the completeness of critical anatomical structures, particularly small or thin regions that are easily missed. To address this limitation, we propose a suite of novel anatomy-aware evaluation metrics designed to assess structural completeness across anatomical structures, including large organs, small organs, intestines, and vessels. Building on these metrics, we introduce CARE, a Completeness-Aware Reconstruction Enhancement framework that incorporates structural penalties during training to encourage anatomical preservation of significant structures. CARE is model-agnostic and can be seamlessly integrated into analytical, implicit, and generative methods. When applied to these methods, CARE substantially improves structural completeness in CT reconstructions, achieving up to +32% improvement for large organs, +22% for small organs, +40% for intestines, and +36% for vessels. |
| title | Are Pixel-Wise Metrics Reliable for Sparse-View Computed Tomography Reconstruction? |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.02093 |