MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust Quantitative Connectivity through Harmonized Preprocessing of Diffusion MRI
Creators: name: Newlin, Nancy R.
name: Schilling, Kurt
name: Koudoro, Serge
name: Chandio, Bramsh Qamar
name: Kanakaraj, Praitayini
name: Moyer, Daniel
name: Kelly, Claire E.
name: Genc, Sila
name: Chen, Jian
name: Yang, Joseph Yuan-Mou
name: Wu, Ye
name: He, Yifei
name: Zhang, Jiawei
name: Zeng, Qingrun
name: Zhang, Fan
name: Adluru, Nagesh
name: Nath, Vishwesh
name: Pathak, Sudhir
name: Schneider, Walter
name: Gade, Anurag
name: Rathi, Yogesh
name: Hendriks, Tom
name: Vilanova, Anna
name: Chamberland, Maxime
name: Pieciak, Tomasz
name: Ciupek, Dominika
name: Vega, Antonio Tristán
name: Aja-Fernández, Santiago
name: Malawski, Maciej
name: Ouedraogo, Gani
name: Machnio, Julia
name: Ewert, Christian
name: Thompson, Paul M.
name: Jahanshad, Neda
name: Garyfallidis, Eleftherios
name: Landman, Bennett A.
Publisher: name: arXiv
Year: 2024
Subjects: subject: Medical Physics (physics.med-ph)
subject: Machine Learning (cs.LG)
subject: FOS: Physical sciences
subject: FOS: Physical sciences
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences

description: White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (DW-MRI) to qualitatively identify changes in white matter microstructure and connectivity. Yet, quantitative analysis of DW-MRI data is hindered by inconsistencies stemming from varying acquisition protocols. There is a pressing need to harmonize the preprocessing of DW-MRI datasets to ensure the derivation of robust quantitative diffusion metrics across acquisitions. In the MICCAI-CDMRI 2023 QuantConn challenge, participants were provided raw data from the same individuals collected on the same scanner but with two different acquisitions and tasked with preprocessing the DW-MRI to minimize acquisition differences while retaining biological variation. Submissions are evaluated on the reproducibility and comparability of cross-acquisition bundle-wise microstructure measures, bundle shape features, and connectomics. The key innovations of the QuantConn challenge are that (1) we assess bundles and tractography in the context of harmonization for the first time, (2) we assess connectomics in the context of harmonization for the first time, and (3) we have 10x additional subjects over prior harmonization challenge, MUSHAC and 100x over SuperMUDI. We find that bundle surface area, fractional anisotropy, connectome assortativity, betweenness centrality, edge count, modularity, nodal strength, and participation coefficient measures are most biased by acquisition and that machine learning voxel-wise correction, RISH mapping, and NeSH methods effectively reduce these biases. In addition, microstructure measures AD, MD, RD, bundle length, connectome density, efficiency, and path length are least biased by these acquisition differences.
description: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2024/019