Innovations in MRI and AI Integration for Vascular Plaque Evaluation and Overview of Deep Learning Techniques in Peripheral Vascular Disease

Recent years have brought increasing development of artificial intelligence (AI)-based approaches aimed at optimizing clinical decision-making in vascular medicine. Models increasingly facilitate diagnostic interpretation, treatment planning, and prognosis prediction, particularly in complex cases of peripheral artery disease (PAD).

In parallel, a major effort to integrate deep learning methods and imaging modalities has led to new opportunities for using anatomical data to enhance diagnostic capabilities. Among these modalities, magnetic resonance imaging (MRI) is gaining popularity as a noninvasive, nonionizing imaging tool with excellent soft tissue contrast and plaque characterization capabilities. Unlike conventional imaging, MRI can provide information on vascular composition—eg, soft tissue, fibrosis, and calcification—without exposing PAD patients to ionizing radiation and thus is an especially desirable modality for serial monitoring and long-term follow-up of PAD.

In this review, we present an overview of the current state of AI integration with MRI in vascular imaging, with a particular focus on PAD. We also introduce several contributions from our research group, with our uniquely designed MRI protocol that incorporates ultrashort echo time and multicontrast sequences to improve vascular tissue characterization in PAD. These high-resolution datasets have served as the foundation for the development of deep learning-based classification systems, including both supervised and unsupervised models. Specifically, we describe how variational autoencoders and hybrid scoring systems have been used to phenotype PAD lesions and predict procedural factors such as guidewire crossability. Our work represents a step toward more objective, reproducible, and clinically interpretable tools that bridge high-fidelity imaging with scalable, AI-driven analysis.