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Abstract #0526

Development and Validation of a Multimodal 3D Brain Image Registration Algorithm for Alzheimer’s disease and Related Dementias

Juan Antonio K. Chong Chie1, Scott A. Persohn1,2, Paul Salama3, and Paul R. Territo1,2
1Indiana University School of Medicine, Stark Neurosciences Research Institute, Indianapolis, IN, United States, 2Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, United States, 3Electrical and Computer Engineering, Purdue University, Indianapolis, IN, United States

Synopsis

Keywords: Data Processing, Multimodal, Alzheimer's Disease, Image Registration

Motivation: Multimodal registration of brain images enables comprehensive integration of data between imaging modalities, enhancing our understanding of brain structure, function(s), and disease coping mechanisms.

Goal(s): This study develops and validates a pipeline for multimodal image registration of MRI and PET data from ADRD subjects to a common atlas space.

Approach: This framework utilizes a multi-stage approach, combining rigid and deformable registration, to align input images to the MNI152+ atlas for comparisons of brain anatomy and function.

Results: This study generated a 3D registration framework for multimodal images, achieving a Structural Similarity Index Measure (SSIM) score of 0.9143±0.0288(n=337) across subjects with different ADRD stages.

Impact: This study presents a 3D multimodal registration framework invariant to image contrast, spatial resolutions, and anatomical and functional variations; thus, enabling direct comparisons of brain anatomy and function(s) across subjects, while minimizing inter-subject variability, permitting quantitative multimodal analysis.

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Keywords