Are Pixel-Wise Metrics Reliable for Computerized Tomography Reconstruction?
Tianyu Lin, Xinran Li, Chuntung Zhuang, Qi Chen, Yuanhao Cai, Kai Ding, Alan Yuille, Zongwei Zhou
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 regions. CARE is model-agnostic and can be seamlessly integrated into both analytical reconstruction methods and modern learning-based methods, such as Neural Radiance Fields and Gaussian Splatting. When applied to these methods, CARE substantially improves structural completeness in reconstructed CT scans, yielding performance gains of up to +32\% for large organs, +22\% for small organs, +40\% for intestines, and +36\% for vessels. Code has been attached as supplementary material for peer review and will be made publicly available.