TY - JOUR
T1 - Convolutional Neural Networks to Study Contrast-Enhanced Magnetic Resonance Imaging–Based Skeletal Calf Muscle Perfusion in Peripheral Artery Disease
AU - Khagi, Bijen
AU - Belousova, Tatiana
AU - Short, Christina M.
AU - Taylor, Addison A.
AU - Bismuth, Jean
AU - Shah, Dipan J.
AU - Brunner, Gerd
N1 - Publisher Copyright:
© 2024 Elsevier Inc.
PY - 2024/6/1
Y1 - 2024/6/1
N2 - 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.
AB - 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.
KW - classification
KW - contrast-enhanced magnetic resonance imaging
KW - convolution neural networks
KW - deep learning
KW - microvascular circulation
KW - peripheral artery disease
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U2 - 10.1016/j.amjcard.2024.03.035
DO - 10.1016/j.amjcard.2024.03.035
M3 - Article
C2 - 38580040
AN - SCOPUS:85190346412
SN - 0002-9149
VL - 220
SP - 56
EP - 66
JO - American Journal of Cardiology
JF - American Journal of Cardiology
ER -