Abstract #0963

A Multi-Modal Biomechanical Imaging and Analysis Framework for Co-Correlation of 7T MR Elastography, 7T DTI, and Amyloid Deposition

Em Triolo¹, Mackenzie Langan², Oleksandr Khegai², Sarah Binder³, Trey Hedden⁴, Priti Balchandani², and Mehmet Kurt^1,3

¹University of Washington, Seattle, WA, United States, ²Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York City, NY, United States, ³Icahn School of Medicine at Mount Sinai, New York City, NY, United States, ⁴Department of Neurology, Icahn School of Medicine at Mount Sinai, New York City, NY, United States

Synopsis

Keywords: Alzheimer's Disease, Alzheimer's Disease, DTI, PET

Motivation: There is currently a profound need for non-invasive early detection of mild cognitive impairment (MCI) and Alzheimer’s disease (AD).

Goal(s): The purpose of this study is to determine the relationships between imaging and cognitive testing metrics.

Approach: Subjects underwent multimodal imaging, including 7T DTI, 7T MRE, and amyloid PET, and a PACC test. These metrics were used in Shapley Regressions to determine which metrics were the best predictors of MRE or SUVR.

Results: We determined that SUVR was the best predictors of MRE metrics, and that MRE was one of the best predictors of SUVR in subjects with amyloidosis.

Impact: Using multimodal imaging at ultrahigh field (7T) we have observed preliminary relationships between amyloid deposition and biomechanical and microstructural metrics as determined by 7T MR Elastography and DTI in the human brain.

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