Magnetic Resonance Imaging-Derived Microvascular Perfusion Modeling to Assess Peripheral Artery Disease

Multi-modality imaging for assessment of the microcirculation in peripheral artery disease: Bench to clinical practice

Peripheral artery disease (PAD) is a highly prevalent disorder with a high risk of mortality and amputation despite the introduction of novel medical and procedural treatments. Microvascular disease (MVD) is common among patients with PAD, and despite the established role as a predictor of amputations and mortality, MVD is not routinely assessed as part of current standard practice. Recent pre-clinical and clinical perfusion and molecular imaging studies have confirmed the important role of MVD in the pathogenesis and outcomes of PAD. The recent advancements in the imaging of the peripheral microcirculation could lead to a better understanding of the pathophysiology of PAD, and result in improved risk stratification, and our evaluation of response to therapies. In this review, we will discuss the current understanding of the anatomy and physiology of peripheral microcirculation, and the role of imaging for assessment of perfusion in PAD, and the latest advancements in molecular imaging. By highlighting the latest advancements in multi-modality imaging of the peripheral microcirculation, we aim to underscore the most promising imaging approaches and highlight potential research opportunities, with the goal of translating these approaches for improved and personalized management of PAD in the future.

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

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.

A machine learning-based approach to identify peripheral artery disease using texture features from contrast-enhanced magnetic resonance imaging

Diagnosing and assessing the risk of peripheral artery disease (PAD) has long been a focal point for medical practitioners. The impaired blood circulation in PAD patients results in altered microvascular perfusion patterns in the calf muscles which is the primary location of intermittent claudication pain. Consequently, we hypothesized that changes in perfusion and increase in connective tissue could lead to alterations in the appearance or texture patterns of the skeletal calf muscles, as visualized with non-invasive imaging techniques. We designed an automatic pipeline for textural feature extraction from contrast-enhanced magnetic resonance imaging (CE-MRI) scans and used the texture features to train machine learning models to detect the heterogeneity in the muscle pattern among PAD patients and matched controls. CE-MRIs from 36 PAD patients and 20 matched controls were used for preparing training and testing data at a 7:3 ratio with cross-validation (CV) techniques. We employed feature arrangement and selection methods to optimize the number of features. The proposed method achieved a peak accuracy of 94.11% and a mean testing accuracy of 84.85% in a 2-class classification approach (controls vs. PAD). A three-class classification approach was performed to identify a high-risk PAD sub-group which yielded an average test accuracy of 83.23% (matched controls vs. PAD without diabetes vs. PAD with diabetes). Similarly, we obtained 78.60% average accuracy among matched controls, PAD treadmill exercise completers, and PAD exercise treadmill non-completers. Machine learning and imaging-based texture features may be of interest in the study of lower extremity ischemia.