Digital subtraction angiography (DSA) in coronary imaging is fundamentally challenged by physiological motion, forcing reliance on raw angiograms cluttered with anatomical noise. Existing deep learning methods often produced images with two critical clinically unacceptable flaws: persistent boundary artifacts and a loss of native tissue grayscale fidelity that undermined diagnostic confidence. We propose a novel framework termed as CDSA-Net that for the first time explicitly decouples and jointly optimizes vascular structure preservation and realistic background restoration. CDSA-Net introduces two core innovations: (i) A hierarchical geometric prior guidance (HGPG) mechanism, embedded in our coronary structure extraction network (CSENet). It synergistically combines integrated geometric prior (IGP) with gated spatial modulation (GSM) and centerline-aware topology (CAT) loss supervision, ensuring structural continuity. (ii) An adaptive noise module (ANM) within our coronary background restoration network (CBResNet). Unlike standard restoration, ANM uniquely models the stochastic nature of clinical X-ray noise, bridging the domain gap to enable seamless background intensity estimation and the complete elimination of boundary artifacts. The final subtraction is obtained by removing the restored background from the raw angiogram. Quantitatively, it significantly outperformed state-of-the-art methods in vascular intensity correlation and perceptual quality. A 25.6% improvement in morphology assessment efficiency and a 42.9% gain in hemodynamic evaluation speed set a new benchmark for utility in interventional cardiology, while maintaining diagnostic results consistent with raw angiograms. The project code is available at https://github.com/DrThink-ai/CDSA-Net.

翻译：数字减影血管造影（DSA）在冠状动脉成像中面临生理性运动带来的根本性挑战，迫使临床从业者依赖受解剖噪声干扰的原始血管造影图像。现有深度学习方法生成的图像存在两项临床不可接受的缺陷：持续性边界伪影以及本征组织灰度保真度损失，后者直接削弱诊断可信度。我们提出名为CDSA-Net的新型框架，首次实现血管结构保持与真实背景重建的显式解耦与联合优化。该框架包含两项核心创新：(i) 嵌入冠状动脉结构提取网络（CSENet）中的层级几何先验引导（HGPG）机制，该机制通过协同融合集成几何先验（IGP）与门控空间调制（GSM）及中心线感知拓扑（CAT）损失监督，确保结构连续性；(ii) 集成于冠状动脉背景重建网络（CBResNet）的自适应噪声模块（ANM）。与传统重建方法不同，ANM独创性地对临床X射线噪声的随机特性进行建模，弥合域差异以实现无缝背景强度估计并彻底消除边界伪影。最终通过从原始血管造影图像中去除重建背景获得减影结果。定量分析表明，本方法在血管强度相关性和感知质量上显著超越现有最优技术，其形态评估效率提升25.6%，血流动力学评估速度提升42.9%，为介入心脏病学建立新的实用性基准，同时保持与原始血管造影图像一致的诊断结果。项目代码发布于 https://github.com/DrThink-ai/CDSA-Net。