Convolutional Neural Networks to Study Contrast-Enhanced Magnetic Resonance Imaging–Based Skeletal Calf Muscle Perfusion in Peripheral Artery Disease

Abstract

Peripheral artery disease (PAD) is associated with impaired blood flow in the lower extremities and histopathological changes of the skeletal calf muscles resulting in abnormal microvascular perfusion. We studied the use of convolutional neural networks (CNNs) to differentiate PAD patients from matched controls by utilizing perfusion pattern features from contrast-enhanced magnetic resonance imaging (CE-MRI) of the skeletal calf muscles. We acquired CE-MRI based skeletal calf muscle perfusion in 56 individuals (36 PAD patients, 20 matched controls). Microvascular perfusion imaging was performed post reactive hyperemia at the mid-calf level with a temporal resolution of 409 ms. We analyzed perfusion scans up to 2 minutes indexed from the local pre-contrast arrival time frame. Skeletal calf muscles including the anterior muscle (AM), lateral muscle (LM), deep posterior muscle group (DM), and the soleus (SM) and gastrocnemius muscles (GM) were segmented semi-automatically. Segmented muscles were represented as 3D DICOM stacks of CE-MRI perfusion scans for deep learning analysis. We tested several CNN models for the 3D CE-MRI perfusion stacks to classify PAD patients from matched controls. Two of the best performing CNNs (resNet and divNet) were selected to develop the final classification model. A peak accuracy of 75% was obtained for both resNet and divNet. Specificity was 80% and 94% for resNet and divNet, respectively. In conclusion, deep learning utilizing CNNs, and CE-MRI skeletal calf muscle perfusion can discriminate PAD patients from matched controls. Deep learning methods may be of interest for the study of PAD.