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Magnetic resonance imaging based modeling of microvascular perfusion in patients with peripheral artery disease.

ABSTRACT: Peripheral artery disease (PAD) is associated with an increased risk of adverse cardiovascular events, impaired lower extremity blood flow and microvascular perfusion abnormalities in the calf muscles which can be determined with contrast-enhanced magnetic resonance imaging (CE-MRI). We developed a computational model of the microvascular perfusion in the calf muscles. We included 20 patients (10 PAD, 10 controls) and utilized the geometry, mean signal intensity and arterial input functions from CE-MRI calf muscle perfusion scans. The model included the microvascular pressure (p_v), outflow filtration coefficient (OFC), transfer rate constant (k^t), porosity (φ), and the interstitial permeability (K_tissue). Parameters were fitted and the simulations were compared across PAD patients and controls. Intra-observer reproducibility of the simulated mean signal intensities was excellent (intraclass correlation coefficients >0.995). k^t and K_tissue were higher in PAD patients compared with controls (4.72 interquartile range (IQR) 3.33, 5.56 vs. 2.47 IQR 2.10, 2.85; p = 0.003; and 3.68 IQR 3.18, 4.41 vs. 1.81 IQR 1.81, 1.81; p < 0.001). Conversely, porosity (φ) was lower in PAD patients compared with controls (0.52 IQR 0.49, 0.54 vs. 0.61 IQR 0.58, 0.64; p = 0.016). Porosity (φ) was correlated with the ankle brachial index (r = 0.64, p = 0.011). The proposed computational microvascular model is robust and reproducible, and essential model parameters differ significantly between PAD patients and controls.

SUBMITTER: Gimnich OA

PROVIDER: S-EPMC7390497 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Magnetic resonance imaging based modeling of microvascular perfusion in patients with peripheral artery disease.

Gimnich Olga A OA Singh Jaykrishna J Bismuth Jean J Shah Dipan J DJ Brunner Gerd G

Journal of biomechanics 20190708

Peripheral artery disease (PAD) is associated with an increased risk of adverse cardiovascular events, impaired lower extremity blood flow and microvascular perfusion abnormalities in the calf muscles which can be determined with contrast-enhanced magnetic resonance imaging (CE-MRI). We developed a computational model of the microvascular perfusion in the calf muscles. We included 20 patients (10 PAD, 10 controls) and utilized the geometry, mean signal intensity and arterial input functions from ...[more]

PMID: 31331663

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Project description:Background Computational fluid dynamics has shown good agreement with contrast-enhanced magnetic resonance imaging measurements in cardiovascular disease applications. We have developed a biomechanical model of microvascular perfusion using contrast-enhanced magnetic resonance imaging signal intensities derived from skeletal calf muscles to study peripheral artery disease (PAD). Methods and Results The computational microvascular model was used to study skeletal calf muscle perfusion in 56 individuals (36 patients with PAD, 20 matched controls). The recruited participants underwent contrast-enhanced magnetic resonance imaging and ankle-brachial index testing at rest and after 6-minute treadmill walking. We have determined associations of microvascular model parameters including the transfer rate constant, a measure of vascular leakiness; the interstitial permeability to fluid flow which reflects the permeability of the microvasculature; porosity, a measure of the fraction of the extracellular space; the outflow filtration coefficient; and the microvascular pressure with known markers of patients with PAD. Transfer rate constant, interstitial permeability to fluid flow, and microvascular pressure were higher, whereas porosity and outflow filtration coefficient were lower in patients with PAD than those in matched controls (all P values ≤0.014). In pooled analyses of all participants, the model parameters (transfer rate constant, interstitial permeability to fluid flow, porosity, outflow filtration coefficient, microvascular pressure) were significantly associated with the resting and exercise ankle-brachial indexes, claudication onset time, and peak walking time (all P values ≤0.013). Among patients with PAD, interstitial permeability to fluid flow, and microvascular pressure were higher, while porosity and outflow filtration coefficient were lower in treadmill noncompleters compared with treadmill completers (all P values ≤0.001). Conclusions Computational microvascular model parameters differed significantly between patients with PAD and matched controls. Thus, computational microvascular modeling could be of interest in studying lower extremity ischemia.

Project description: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 hyper-, iso-, or hypo-enhanced and were generated for the anterior (AM), lateral (LM), and deep posterior (DM) muscle groups, and the soleus (SM) and gastrocnemius muscles (GM). An unsupervised block-search algorithm was developed to identify heterogeneous regions of interest based on homogeneity. Machine learning methods were utilized to classify PAD patients from controls, with subgroup analyses performed based on lower extremity function and diabetes. The hypo-enhanced and hyper-enhanced voxel percentages obtained from the dynamic muscle perfusion maps were used to train a decision tree classifier to distinguish PAD patients from controls. The two-group classifier obtained a leave-one-out cross-validation (LOOCV) F1-score of 87.6 and 76.7% with hyper-enhanced and hypo-enhanced perfusion features averaged over all muscle groups, respectively. Hypo-enhanced perfusion features, a marker of microvascular perfusion abnormalities, classified PAD patients who completed a 6-minute treadmill walking test compared to those who did not, with an LOOCV F1-score of 67.6%. Using the same method, hypo-enhanced perfusion features differentiated PAD patients with diabetes versus those without with an LOOCV F1-score of 70.3%. In conclusion, CE-MRI derived measures of skeletal calf muscle perfusion can be used with a decision tree classifier to differentiate PAD patients from matched controls. Machine learning can also identify PAD patients with lower exercise capacity and those with concomitant diabetes. Machine learning and CE-MRI derived measures of the calf microcirculation could be of interest in the study of PAD and disease severity.

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Magnetic resonance imaging based modeling of microvascular perfusion in patients with peripheral artery disease.

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Magnetic resonance imaging based modeling of microvascular perfusion in patients with peripheral artery disease.

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