Contrast-enhanced magnetic resonance imaging based calf muscle perfusion and machine learning in peripheral artery disease.

ABSTRACT: Peripheral artery disease (PAD) remains underdiagnosed and undertreated and is associated with an increased risk for adverse cardiovascular outcomes. Imaging provides an approach to identifying patients with PAD. However, the role of integrating imaging with machine learning to identify PAD patients and potentially assess disease severity remains understudied. A total of 56 participants, including 36 PAD patients with intermittent claudication and 20 matched controls, underwent contrast-enhanced magnetic resonance imaging (CE-MRI) calf muscle perfusion scanning. CE-MRI-derived dynamic muscle perfusion maps were developed to quantify alterations of the microvascular circulation in the calf muscles based on voxel contrast enhancement. These dynamic muscle perfusion maps categorized voxels as

SUBMITTER: Khagi B 

PROVIDER: S-EPMC11811054 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature