Introducing QuantConn: Overcoming challenging diffusion acquisitions with harmonization

Area of Science:

• Neuroimaging
• Biomedical Engineering
• Computational Neuroscience

Background:

• White matter alterations are crucial in neurological disease progression.
• Diffusion-weighted magnetic resonance imaging (DW-MRI) is vital for studying white matter microstructure and connectivity.
• Inconsistent DW-MRI acquisition protocols hinder quantitative analysis and reproducibility across studies.

Purpose of the Study:

• To harmonize DW-MRI data preprocessing for consistent quantitative metrics.
• To enable reproducible bundle-wise microstructure, fiber bundle features, and connectomics measures.
• To address the challenge of minimizing acquisition variability while preserving biological signals.

Main Methods:

• Utilized raw DW-MRI data from the MICCAI CDMRI 2023 QuantConn challenge.
• Data involved two acquisition protocols from the same individuals on a single 4 tesla scanner.
• Focused on preprocessing strategies to harmonize data and minimize scanner/protocol differences.

Main Results:

• Established a testing framework for DW-MRI data harmonization.
• Provided baseline pre-harmonized results for the challenge.
• Demonstrated the feasibility of minimizing acquisition differences through preprocessing.

Conclusions:

• Harmonized DW-MRI preprocessing is essential for reproducible quantitative analysis in neurological research.
• The QuantConn challenge framework facilitates the development of robust harmonization techniques.
• Minimizing technical variability enhances the reliability of white matter microstructure and connectomics findings.