提出新评估方法与框架，提升稀疏视角CT重建中关键结构的完整性。
Are Pixel-Wise Metrics Reliable for Sparse-View Computed Tomography Reconstruction?
- 设计解剖感知评估指标，关注器官、血管等关键结构的完整性。
- 引入CARE框架，训练时加入结构惩罚，使重建更完整，血管提升36%。
- 适用于各类重建模型，尤其适合关注解剖结构保真的医学影像任务。
稀疏视角计算机断层扫描（CT）重建中广泛使用的像素级评价指标（如SSIM、PSNR）虽注重像素精度，却难以捕捉重要解剖结构的完整性，尤其是小或细长结构易被遗漏。为此，本文提出一套新的解剖感知评价指标，用于评估大器官、小器官、肠管和血管等结构的完整性。基于这些指标，我们构建了CARE（Completeness-Aware Reconstruction Enhancement）框架，在训练中引入结构惩罚项，以增强关键解剖结构的保留。该方法不依赖特定模型，可无缝集成于解析式、隐式及生成式重建方法中。实验表明，CARE显著提升重建结构完整性：大器官最高提升32%，小器官提升22%，肠管提升40%，血管提升36%。
原文摘要 · Abstract (English)
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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