发表机构
Institute of Biomedical Engineering; University of Oxford（生物医学工程研究所; 牛津大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对常规临床中仅两个窄角投影可用的情况，提出多阶段自监督NeRF框架NeCA++，通过两阶段渐进细化及血管特异性正则化，在三个数据集上优于现有方法，每例重建耗时58秒内。
AI 中文摘要
X射线冠状动脉造影是实时心脏介入手术中冠状动脉疾病临床诊断的金标准，但仅能提供本质上是三维血管的二维投影。现有的基于学习的二维到三维重建方法通常需要较宽的角覆盖或多视角，这些假设在常规临床实践中很少得到满足，因为常规实践中仅有角间隔较窄的两个投影可用。为解决这些挑战，我们提出NeCA++，一种针对临床现实采集约束量身定制的多阶段自监督神经辐射场（NeRF）框架。该框架将重建分解为两个阶段，逐步细化空间支持和表示能力。在第一阶段，重建血管系统的粗略三维表示，将后续优化限制在血管存在可能性较高的区域，称为活动区域。之后，重建被限制在该区域内，同时逐步激活更高分辨率的表示以恢复精细的血管细节。这种多阶段策略将学习聚焦于解剖学上合理的区域，减轻极端稀疏性下的梯度稀释，并在恢复精细血管分支之前稳定全局拓扑。此外，引入了两种血管特异性正则化：一种射线对齐约束以减少投影引起的模糊性，以及一种双峰密度惩罚以实现早期血管-背景分离。在三个数据集（ImageCAS、ASOCA和Synthetic RCA）和四种角度配置上的大量实验表明，该方法始终优于最先进的基线，尤其是在临床现实的窄角设置下，同时每例重建时间在58秒内完成。
英文摘要
X-ray coronary angiography is the clinical gold standard for coronary artery disease during real-time cardiac interventions, but provides only 2D projections of inherently 3D vessels. Existing learning-based 2D-to-3D reconstruction methods typically require wide angular coverage or multiple views, assumptions that are rarely satisfied in routine practice where only two projections with narrow angular separation are available. To address these challenges, we propose NeCA++, a multi-stage self-supervised neural radiance field (NeRF) framework tailored to clinically realistic acquisition constraints. The framework decomposes reconstruction into two stages that progressively refine spatial support and representation capacity. In the first stage, a coarse 3D representation of the vasculature is reconstructed, restricting the subsequent optimisation to regions with a higher likelihood of vessel presence, termed an active region. Afterward reconstruction is restricted to this region while higher-resolution representations are progressively activated to recover fine vascular details. This multi-stage strategy focuses learning on anatomically plausible regions, mitigates gradient dilution under extreme sparsity, and stabilises global topology before recovering fine vascular branches. Furthermore, two vessel-specific regularisations are introduced: a ray-aligned constraint to reduce projection-induced ambiguity, and a bimodal density penalty to enable early vessel-background separation. Extensive experiments across three datasets (ImageCAS, ASOCA, and Synthetic RCA) and four angular configurations demonstrate consistent superiority over state-of-the-art baselines, particularly under clinically realistic narrow-angle settings, while achieving reconstruction within 58 seconds per case.
Comments11 pages, 2 figures
