Multi-Modal Magnetic Resonance Imaging Predicts Regional Amyloid Burden in the Brain

Alathur Rangarajan, Anusha (2019) Multi-Modal Magnetic Resonance Imaging Predicts Regional Amyloid Burden in the Brain. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

Alzheimer’s disease (AD) is the most common cause of dementia and identifying early markers of this disease is important for prevention and treatment strategies. Amyloid- β (Aβ) protein deposition is one of the earliest detectable pathological changes in AD. But in-vivo detection of Aβ using positron emission tomography (PET) is hampered by high cost and limited geographical accessibility. These factors can become limiting when PET is used to screen large numbers of subjects into prevention trials when only a minority are expected to be amyloid-positive. Structural MRI is advantageous; as it is non-invasive, relatively inexpensive and more accessible. Thus it could be widely used in large studies, even when frequent or repetitive imaging is necessary. We used a machine learning, pattern recognition, approach using intensity-based features from individual and combination of MR modalities (T1 weighted, T2 weighted, T2 fluid attenuated inversion recovery [FLAIR], susceptibility weighted imaging) to predict voxel-level amyloid in the brain. The MR- Aβ relation was learned within each subject and generalized across subjects using subject–specific features (demographic, clinical, and summary MR features). When compared to other modalities, combination of T1-weighted, T2-weighted FLAIR, and SWI performed best in predicting the amyloid status as positive or negative. A combination of T2-weighted and SWI imaging performed the best in predicting change in amyloid over two timepoints. Overall, our results show feasibility of amyloid prediction by MRI and its potential use as an amyloid-screening tool.

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Details

Item Type:	University of Pittsburgh ETD
Status:	Unpublished
Creators/Authors:	Creators Email Pitt Username ORCID Alathur Rangarajan, Anusha ana92@pitt.edu ana92
ETD Committee:	Title Member Email Address Pitt Username ORCID Committee Chair Aizenstein, Howard aizensteinhj@upmc.edu aizen Committee Member Stetten, George stetten@pitt.edu stetten Committee Member Laymon, Charles cml14@pitt.edu cml14 Committee Member Kang, Kim kangkim@upmc.edu
Date:	18 June 2019
Date Type:	Publication
Defense Date:	18 March 2019
Approval Date:	18 June 2019
Submission Date:	7 March 2019
Access Restriction:	No restriction; Release the ETD for access worldwide immediately.
Number of Pages:	141
Institution:	University of Pittsburgh
Schools and Programs:	Swanson School of Engineering > Bioengineering
Degree:	PhD - Doctor of Philosophy
Thesis Type:	Doctoral Dissertation
Refereed:	Yes
Uncontrolled Keywords:	Alzheimer's disease, Amyloid prediction, machine learning, patter recogniton, multimodal MRI, PET, PiB
Date Deposited:	18 Jun 2019 19:18
Last Modified:	18 Jun 2019 19:18
URI:	http://d-scholarship.pitt.edu/id/eprint/36226

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• Multi-Modal Magnetic Resonance Imaging Predicts Regional Amyloid Burden in the Brain. (deposited 18 Jun 2019 19:18) [Currently Displayed]

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