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

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.

@article{lin2025_2506.02093,
  title={ Are Pixel-Wise Metrics Reliable for Sparse-View Computed Tomography Reconstruction? },
  author={ Tianyu Lin and Xinran Li and Chuntung Zhuang and Qi Chen and Yuanhao Cai and Kai Ding and Alan L. Yuille and Zongwei Zhou },
  journal={arXiv preprint arXiv:2506.02093},
  year={ 2025 }
}