Are Pixel-Wise Metrics Reliable for Sparse-View Computed Tomography Reconstruction?

• URL: http://arxiv.org/abs/2506.02093v1
• Date: Mon, 02 Jun 2025 17:07:10 GMT
• Title: Are Pixel-Wise Metrics Reliable for Sparse-View Computed Tomography Reconstruction?
• Authors: Tianyu Lin, Xinran Li, Chuntung Zhuang, Qi Chen, Yuanhao Cai, Kai Ding, Alan L. Yuille, Zongwei Zhou,
• Abstract summary: We propose a suite of anatomy-aware evaluation metrics to assess structural completeness across anatomical structures.<n> CARE incorporates structural penalties during training to encourage anatomical preservation of significant structures.<n> 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.
• Score: 50.68335638232752
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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