A novel method for improving the accuracy of MR-derived patient-specific vascular models using X-ray angiography

MR imaging is a noninvasive imaging modality that is commonly used during clinical follow up and has been widely utilized to reconstruct realistic 3D vascular models for patient-specific analysis. In a recent work, we utilized patient-specific hemodynamic analysis of the circle of Willis to noninvasively assess stroke risk in pediatric Moyamoya disease (MMD)—a progressive steno-occlusive cerebrovascular disease that leads to recurrent stroke. The objective was to identify vascular regions with critically high wall shear rate (WSR), signifying elevated stroke risk. However, sources of error including insufficient resolution of MR images can negatively impact vascular model accuracy, especially in areas of severe pathological narrowing, and thus diminish clinical relevance of simulation results, as local hemodynamics are sensitive to vessel geometry. We have developed a novel method to improve the accuracy of MR-derived 3D vascular models utilizing 2D X-ray angiography (XA), which is considered the gold standard for clinically assessing vessel caliber. In this workflow, ″virtual angiographies″ (VA) of 3D MR-derived vascular models are conducted, producing 2D projections that are compared to corresponding XA images guiding the local adjustment of modeled vessels. This VA-comparison-adjustment loop is iterated until the two agree, as confirmed by an expert neuroradiologist. Using this method, we generated models of the circle of Willis of two patients with a history of unilateral stroke. Blood flow simulations were performed using a Navier-Stokes solver within an isogeoemtric analysis framework and WSR distributions were quantified. Results for one patient show as much as 45% underestimation of local WSR in the stenotic left anterior cerebral artery (LACA) and up to a 60% underestimation in the right anterior cerebral artery when using the initial MR-derived model compared to the XA-adjusted model, emphasizing the need for verifying improved accuracy of the adjusted model. To that end, vessel cross-sectional areas of the pre- and post-adjustment models were compared to those seen in 3D CTA images of the same patient. CTA has superior resolution and signal to noise ratio compared to MR imaging but is not commonly used in clinic due to radiation exposure concerns, especially in pediatric patients. While the vessels in the initial model had normalized root mean squared deviations (NRMSDs) ranging from 26% to 182% and 31% to 69% in two patients with respect to CTA, the adjusted vessel NRMSDs were comparatively smaller (32% to 53% and 11% to 42%). In the mildly stenotic LACA of patient 1, the NRMSDs for the pre- and post-adjusted models were 49% and 32%, respectively. These findings suggest that our XA-based adjustment method can considerably improve the accuracy of vascular models, and thus, stroke-risk prediction. An accurate individualized assessment of stroke risk would be of substantial clinical benefit because it would help guide the timing of preventative surgical interventions in pediatric MMD patients.