Introducing QuantConn: Overcoming Challenging Diffusion Acquisitions with Harmonization

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Nancy Newlin, Kurt Schilling, Serge Koudoro, Bramsh Qamar Chandio, Praitayini Kanakaraj, Daniel Moyer, Claire E. Kelly, Sila Genc, Joseph Yuan-Mou Yang, Ye Wu, Nagesh Adluru, Vishwesh Nath, Sudhir K. Pathak, Walter Schneider, Anurag Gade, William Consagra, Yogesh Rathi, Tom Hendriks, Anna Vilanova, Maxime Chamberland et al. (11 additional authors not shown)

Published: 01 Jan 2024, Last Modified: 07 Nov 2025CDMRI@MICCAI 2024EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: White matter alterations are increasingly implicated in neurological diseases and their progression. Diffusion-weighted magnetic resonance imaging (DW-MRI) has been included in many international-scale studies to identify alterations in white matter microstructure and connectivity. Yet, quantitative investigation of DW-MRI data is hindered by a lack of consistency due to variations in acquisition protocols, sites, and scanners. Specifically, there is a need to harmonize the preprocessing of DW-MRI datasets to ensure that compatible and reproducible quantitative metrics are derived from each site, including (1) bundle-wise microstructure measures, (2) features of white matter fiber bundles, and (3) connectomics measures. In the MICCAI CDMRI 2023 QuantConn challenge, participants are provided raw data from the same individuals taken with two different acquisition protocols on a single 4 tesla scanner in the same scanning session and asked to preprocess the data in order to minimize acquisition differences while retaining biological variation. Here, we outline the testing framework, provide baseline pre-harmonized results, and discuss the learning implications of this challenge.