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Abstract #3736

qVision for the ELGAN-ECHO Study: An MS-qMRI Processing Pipeline Applied to Large-scale, Multi-site, and Multi-vendor Analyses.

Ryan McNaughton1, Hernan Jara1,2, Chris Pieper2, Laurie Douglass2, Rebecca Fry3, Karl Kuban2, and T. Michael O'Shea3
1Boston University, Boston, MA, United States, 2Boston University Medical Center, Boston, MA, United States, 3University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, MA, United States

Purpose: To describe an integrated, semi-automated image processing pipeline for multispectral qMRI, termed qVision. Methods: Dual-clustering and MS-qMRI python algorithms for the Tri-TSE pulse sequence are automatically calculated and harmonized across a dataset of neuroimaging data from adolescents born extremely preterm. Results: Automated processing is completed in 30 minutes per subject, resulting in high-resolution mappings of T1, T2, PD, and spatial entropy, as well as heavily R1-weighted images of white matter texture via Synthetic-MRI. Conclusion: qVision has been validated on a large-scale, multi-site, and multi-vendor dataset of neuroimaging data, capable of producing a broad spectrum of MS-qMRI outcomes.

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