Language
English
Publication Date
6-1-2024
Journal
The American Journal of Cardiology
DOI
10.1016/j.amjcard.2024.03.035
PMID
38580040
PMCID
PMC11670141
PubMedCentral® Posted Date
6-1-2025
PubMedCentral® Full Text Version
Author MSS
Abstract
Peripheral artery disease (PAD) is associated with impaired blood flow in the lower extremities and histopathologic changes of the skeletal calf muscles, resulting in abnormal microvascular perfusion. We studied the use of convolution neural networks (CNNs) to differentiate patients with PAD from matched controls using 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 patients (36 patients with PAD and 20 matched controls). Microvascular perfusion imaging was performed after reactive hyperemia at the midcalf level, with a temporal resolution of 409 ms. We analyzed perfusion scans up to 2 minutes indexed from the local precontrast arrival time frame. Skeletal calf muscles, including the anterior muscle, lateral muscle, deep posterior muscle group, and the soleus and gastrocnemius muscles, were segmented semiautomatically. Segmented muscles were represented as 3-dimensional Digital Imaging and Communications in Medicine stacks of CE-MRI perfusion scans for deep learning (DL) analysis. We tested several CNN models for the 3-dimensional CE-MRI perfusion stacks to classify patients with PAD from matched controls. A total of 2 of the best performing CNNs (resNet and divNet) were selected to develop the final classification model. A peak accuracy of 75% was obtained for resNet and divNet. Specificity was 80% and 94% for resNet and divNet, respectively. In conclusion, DL using CNNs and CE-MRI skeletal calf muscle perfusion can discriminate patients with PAD from matched controls. DL methods may be of interest for the study of PAD.
Keywords
Humans, Peripheral Arterial Disease, Muscle, Skeletal, Male, Female, Neural Networks, Computer, Contrast Media, Aged, Middle Aged, Magnetic Resonance Imaging, Leg, Regional Blood Flow, Deep Learning, Contrast-enhanced magnetic resonance imaging, microvascular circulation, peripheral artery disease, deep learning, classification, convolutional neural networks
Published Open-Access
yes
Recommended Citation
Khagi, Bijen; Belousova, Tatiana; Short, Christina M; et al., "Convolutional Neural Networks to Study Contrast-Enhanced Magnetic Resonance Imaging-Based Skeletal Calf Muscle Perfusion in Peripheral Artery Disease" (2024). Faculty and Staff Publications. 4244.
https://digitalcommons.library.tmc.edu/baylor_docs/4244