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 Jara^1,2, 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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