Abstract TP140: Automated Individualized Evaluation of Hemodynamics in Intracranial Atherosclerotic Disease With Dual- Venc 4D Flow MRI

Abstract

Introduction: Hemodynamic alterations of intracranial atherosclerotic disease (ICAD) patients may be associated with increased risk of stroke. Dual- venc 4D Flow MRI can provide time-resolved 3D hemodynamics with large velocity dynamic range, but clinical utility is limited by labor intensive analysis. We developed an automated tool to simplify analysis, rapidly extract and visualize hemodynamic parameters, bringing 4D Flow MRI closer to clinical practice. Purpose: To demonstrate clinical feasibility of a new automated tool for extracting hemodynamic parameters based on 4D Flow MRI, and evaluate intracranial hemodynamics in individual ICAD patients relative to normative reference values. Methods: Intracranial dual- venc 4D Flow MRI was acquired in 59 healthy subjects (32M, 48±15Y) and 16 ICAD patients (12M, 62±14Y). An in-house Matlab tool was used to quantify velocity and flow in each Circle of Willis vessel. Hemodynamic measures from the controls were used to derive normative parameter estimates using polynomial fit as a function of age. A hemodynamic profile for each patient included computing percent difference of peak velocity (PV) and flow rate (FR) in each vessel compared to reference values (Fig.1a-c), and showing results in a diagram (Fig.1d). Results: Fig.1e shows results for ICAD patients. 93.8% of stenotic vessels showed normal FR suggesting intracranial hemodynamic compensation. 62.5% of stenotic vessels showed abnormal PV and 75% of patients showed increased PV in at least one vessel in affected hemisphere. Among 3 patients with compensatory collateral circulation, 2 patients had more than 3 vessels with increased PV. This suggests PV is a more sensitive marker of stenosis than FR. Conclusions: We demonstrate the feasibility of an automated tool used with 4D Flow MRI to interrogate intracranial hemodynamics in ICAD patients. It has considerable promise for identifying new noninvasive biomarkers for individualized stroke risk stratification.