用频域注意力与扩散机制提升冠脉造影分割精度。

Background: Coronary artery disease (CAD) remains one of the leading causes of mortality worldwide. Precise segmentation of coronary arteries from invasive coronary angiography (ICA) is critical for effective clinical decision-making. Objective: This study aims to propose a novel deep learning model based on frequency-domain analysis to enhance the accuracy of coronary artery segmentation and stenosis detection in ICA, thereby offering robust support for the stenosis detection and treatment of CAD. Methods: We propose the Frequency-Domain Attention-Guided Diffusion Network (FAD-Net), which integrates a frequency-domain-based attention mechanism and a cascading diffusion strategy to fully exploit frequency-domain information for improved segmentation accuracy. Specifically, FAD-Net employs a Multi-Level Self-Attention (MLSA) mechanism in the frequency domain, computing the similarity between queries and keys across high- and low-frequency components in ICAs. Furthermore, a Low-Frequency Diffusion Module (LFDM) is incorporated to decompose ICAs into low- and high-frequency components via multi-level wavelet transformation. Subsequently, it refines fine-grained arterial branches and edges by reintegrating high-frequency details via inverse fusion, enabling continuous enhancement of anatomical precision. Results and Conclusions: Extensive experiments demonstrate that FAD-Net achieves a mean Dice coefficient of 0.8717 in coronary artery segmentation, outperforming existing state-of-the-art methods. In addition, it attains a true positive rate of 0.6140 and a positive predictive value of 0.6398 in stenosis detection, underscoring its clinical applicability. These findings suggest that FAD-Net holds significant potential to assist in the accurate diagnosis and treatment planning of CAD.