像素级度量对稀疏视图计算机断层扫描重建可靠吗？
Are Pixel-Wise Metrics Reliable for Sparse-View Computed Tomography Reconstruction?
AI总结：
针对稀疏视图CT重建中像素级指标忽略解剖结构完整性的问题，提出解剖感知评估指标及模型无关的CARE框架，显著提升各器官结构完整性。
摘要：
广泛采用的稀疏视图CT重建评估指标——如结构相似性指数和峰值信噪比——优先考虑像素级保真度，但往往无法捕捉关键解剖结构的完整性，特别是容易遗漏的小或薄区域。为解决这一局限性，我们提出了一套新颖的解剖感知评估指标，旨在评估跨解剖结构（包括大器官、小器官、肠道和血管）的结构完整性。基于这些指标，我们引入了CARE，一个完整性感知重建增强框架，在训练过程中纳入结构惩罚以鼓励显著结构的解剖保留。CARE是模型无关的，可以无缝集成到解析、隐式和生成方法中。当应用于这些方法时，CARE显著提高了CT重建中的结构完整性，大器官提升高达+32%，小器官+22%，肠道+40%，血管+36%。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
