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Abstract #3132

Improving Low Resolution MRI Contrast for Brain-Environment Neuroepidemiology

Jasmine D Cakmak1, Reza Azarpazhooh2, Alexander Khaw2, and Udunna Anazodo1,2
1McGill University, Montreal, QC, Canada, 2Western University, London, ON, Canada

Synopsis

Keywords: Data Processing, Segmentation, Synthetic MRIRecycling large-scale clinical data to retrospectively study associations of brain imaging variables with emerging environmental risk factors is a sustainable approach in the growing field of neuroepidemiology. Here, we evaluated the utility of a deep learning tool to increase the resolution of clinical (1.5T) T1-weighted MRI by comparing global assessment of gray matter volume (GMV) and cortical thickness (CT) to 3T research scans. Overall, the resolved 1.5T images had higher (18%) GMV and CT compared to 3T and were more biased than unresolved images. Further analysis in larger cohorts using improved segmentation approaches could validate recycling of enhanced clinical scans.

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Keywords