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Abstract #1200

Harmonizer: A Deep Learning Framework for Transforming Low-resolution Clinical dMRI to Research-grade Quality

Suheyla Cetin-Karayumak1, Ryan Zurrin1, William Consagra2, Lauren J. O'Donnell1, and Yogesh Rathi1
1Harvard Medical School and Brigham and Women's Hospital, Somerville, MA, United States, 2University of South Carolina, Columbia, SC, United States

Synopsis

Keywords: Analysis/Processing, Diffusion Analysis and Visualization, Clinical diffusion MRI (dMRI), Harmonization, Neural Orientation Distribution Field (NODF), CycleGAN, White matter

Motivation: Clinical diffusion MRI data often lack the spatial and angular resolution of research-grade data, limiting structural insights, especially in multi-site studies.

Goal(s): To develop Harmonizer, a deep learning framework that enhances clinical diffusion MRI quality to research standards.

Approach: Harmonizer combines Neural Orientation Distribution Field (NODF) for upsampling with CycleGAN for domain translation, aligning clinical data with research standards. Validation included both simulated and real-world clinical scans across multiple sites..

Results: Harmonizer improved clinical diffusion MRI quality, preserving sex-based FA differences in simulations and achieving a weighted-Dice score of 0.89 in vivo, demonstrating robust harmonization across datasets.




Impact: his work enhances clinical dMRI data for multi-site neuroimaging studies, enabling high-quality, research-compatible analyses. The Harmonizer algorithm opens opportunities to study various disorders and pathologies using clinical data, supporting detailed white matter analyses and cross-site comparisons.

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Keywords