MAC-XA: Multi-view Anatomy-Correspondence Fusion for Coronary Stenosis Reporting from X-ray Angiography

2026-07-07

AI Summary

This study addresses the challenges in automatic report generation from multi-view coronary X-ray angiography, where lesion localization and stenosis grading suffer from projection-dependent inconsistencies and a lack of supervisable cross-view anatomical alignment. To overcome these limitations, the authors propose a geometry-driven multi-view fusion framework that leverages synthetically generated angiograms with controllable geometry to provide plaque-level correspondence supervision. A dedicated anatomical correspondence module explicitly aligns features from auxiliary views into the primary view’s coordinate space before feature aggregation. This approach achieves, for the first time, explicit cross-view anatomical alignment, circumventing the unsupervised nature of real clinical data. Experiments demonstrate significantly improved alignment consistency on synthetic data and successful zero-shot transfer to real angiograms, with structured stenosis reporting performance substantially outperforming both single-view and conventional multi-view methods.

Abstract

Multi-view reasoning in coronary X-ray angiography is inherently a cross-projection geometric problem, yet automated report generation in this setting remains largely unexplored. The 3D vascular topology leads to projection-dependent branch overlap and foreshortening, rendering single-view modeling fundamentally incomplete and unstable for lesion localization and stenosis grading. Although multi-view fusion appears promising, learning anatomically consistent fusion from real angiograms is impeded by a critical limitation: cross-view alignment is unobservable and cannot be explicitly supervised. Consequently, conventional fusion relies on implicit correlations rather than verified anatomical correspondence. We address this by reformulating multi-view stenosis reporting as an alignment-constrained aggregation problem. A controllable synthetic angiography generation strategy is introduced to expose geometry-derived patch-level correspondence supervision unavailable in real data. An anatomy-correspondence module learns cross-view correspondence matrices that explicitly align auxiliary features within the main-view coordinate space prior to fusion, thereby constraining evidence aggregation to anatomically consistent regions. Experiments on synthetic data and zero-shot transfer to real angiograms show that this alignment-constrained design improves correspondence consistency and structured stenosis reporting compared to single-view modeling and conventional multi-view fusion methods. The code will be publicly available upon publication.

Problem

• coronary stenosis
• multi-view fusion
• anatomical correspondence
• X-ray angiography
• cross-projection alignment

Innovation

• multi-view fusion
• anatomical correspondence
• synthetic angiography
• alignment-constrained aggregation
• coronary stenosis reporting