Longitudinal and quantitative MR plaque imaging for prediction of response to medical management in symptomatic intracranial atherosclerosis

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ABSTRACT Intracranial atherosclerotic disease (ICAD) is one of the most common causes of ischemic stroke worldwide. Despite intensive medical management, the current standard of care for secondary stroke prevention, the rate of recurrent stroke is 13% in the first year and as high as 35% in certain populations by 2 years. Currently, the initial and follow-up evaluations of these patients rely exclusively on assessments of clinical risk factors and, in some circumstances, the degree of luminal stenosis on imaging. However, this strategy may overlook subtle non-luminal changes within ICAD lesions. A tool that can directly probe atherosclerotic plaques and accurately quantify longitudinal changes of plaque features may help early identify non-responsive patients in whom an alternative therapy can be initiated. Magnetic resonance (MR) vessel wall imaging (VWI) has the potential to fulfill this role because of its compacity to directly visualize the vessel wall and characterize plaque features. However, there are several technical challenges associated with intracranial VWI: small size, tortuous course, and deep sitting warrant a high-resolution and 3D imaging approach; multiple lesion sites require a large imaging view; signals arising from neighboring blood and cerebrospinal fluid (CSF) need to be adequately suppressed; relatively long imaging time and potential image corruption by patient motion; and lack of dedicated plaque analysis tools particularly for 3D dataset. In this proposal, we will develop a reliable 3D VWI- based MR plaque imaging (MRPI) strategy for prediction of response to medical management in symptomatic ICAD. Specifically, we will first develop a whole-brain 3D VWI sequence with intracranial vessel-dedicated motion compensation and motion-adaptive imaging acceleration (Aim1-Task1) and develop an automated intracranial vessel wall (IVA) tool integrating 3D vessel wall segmentation and computational algorithms for deriving quantitative plaque features (Aim1-Task2) followed by a validation study (Aim1-Task3). The validated techniques will then be used in a cross-sectional study to determine the MRPI-derived quantitative plaque features that are associated with ICAD lesions within the infarcted territory in patients with acute symptomatic ICAD (Aim 2). We will finally conduct a longitudinal study to determine the capacity of longitudinal and quantitative MRPI to predict response to medical management in symptomatic ICAD patients (Aim 3). Successful completion of the project will validate the compacity of VWI-based MRPI to quantitatively characterize ICAD and predict response to medical management in symptomatic ICAD patients. This will open the door to future clinical trials investigating the role of MRPI in developing personalized management paradigms and assessing new therapies.