CMAR: A Pipeline for Cross-Modal Alignment and 3-D Reconstruction of Coronary Arteries Based on Key Bifurcation Vessel Measurements

Integrating the advantages of coronary angiography (CAG) and intravascular ultrasound (IVUS) images can significantly enhance the effectiveness of diagnosing coronary artery disease. However, current techniques for aligning CAG and IVUS rely solely on the manual determination of IVUS. To address this issue, this article introduces an innovative 3-D intelligent reconstruction pipeline for coronary arteries based on CAG and IVUS. The pipeline introduces three main innovations. First, the introduction of a label-text-guided multitask network model for classification and segmentation. This model facilitates end-to-end detection of blood vessel bifurcation and vascular wall segmentation, enhancing segmentation accuracy and calculating bifurcation width information by determining the number of bifurcated blood vessel frames. Second, the proposal of an improved IVUS 3-D reconstruction algorithm that integrates dual-boundary extraction, dual-line segment patch area extraction, and the moving filter-based 3-D smoothing algorithm into the reconstruction processing flow. This method not only presents branch information in the 3-D reconstruction of blood vessels, but also highlights plaque areas, improving the visual impact of the reconstruction. Third, the introduction of a registration algorithm for key branches of blood vessels based on CAG and IVUS. This algorithm aligns CAG and IVUS using key branch information extracted from CAG, achieving intelligent alignment and reconstruction without manual intervention. Experimental results demonstrate that this algorithm effectively aligns CAG and IVUS, assisting doctors in intuitively assessing and locating vascular plaque information, thereby making a significant contribution to the auxiliary diagnosis of coronary heart disease.