A Multi-Modality Attention Network for Coronary Artery Disease Evaluation From Routine Myocardial Perfusion Imaging and Clinical Data

Myocardial perfusion imaging (MPI) is an essential tool for diagnosing and evaluating coronary artery disease (CAD). However, the diagnosis using MPI remains laborious as it involves multi-step readouts and meticulous image processing. These challenges impact current attempts at automating image interpretation of MPI. In this paper, we propose a multi-modality attention network (MMAN) that leverages information from clinical and MPI data for CAD diagnosis. Specifically, we propose an image-correlated cross-attention (ICCA) module that fuses information from both stress and rest MPI to enhance feature representation at the image level. Furthermore, we design a clinical data-guided attention (CDGA) module that integrates clinical data with image features to improve overall feature understanding for CAD evaluation. In addition, we employ self-learning for network pre-training, which further enhances the diagnostic performance using MPI on CAD. Experiments on a myocardial perfusion imaging dataset demonstrate that the proposed method is effective for CAD evaluation using myocardial perfusion imaging and clinical data.