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MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust Quantitative Connectivity through Harmonized

Nancy R Newlin1, Kurt Schilling2, Serge Koudoro3

  • 1Department of Computer Science, Vanderbilt University, Nashville, TN.

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Harmonizing diffusion-weighted MRI data preprocessing is crucial for accurate neurological disease research. Machine learning and resampling methods effectively reduce acquisition biases in white matter microstructure and connectomics analysis.

Keywords:
Diffusion MRIconnectomicsharmonizationimage processingtractographytractometry

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Area of Science:

  • Neuroimaging
  • Biomedical Engineering
  • Computational Neuroscience

Background:

  • White matter alterations are key indicators 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 cross-study comparisons.

Purpose of the Study:

  • To harmonize DW-MRI preprocessing to ensure robust quantitative diffusion metrics across different acquisitions.
  • To evaluate methods for minimizing acquisition-specific differences while preserving biological variation in DW-MRI data.
  • To assess the impact of harmonization on bundle-wise microstructure, shape features, and connectomics.

Main Methods:

  • The QuantConn challenge provided raw DW-MRI data from the same individuals with two acquisition protocols.
  • Participants were tasked with preprocessing DW-MRI data to harmonize cross-acquisition differences.
  • Harmonized data were evaluated based on the reproducibility and comparability of microstructure, shape, and connectomics measures.

Main Results:

  • Measures like bundle surface area and fractional anisotropy were most susceptible to acquisition bias.
  • Machine learning voxel-wise correction, RISH mapping, and NeSH methods effectively reduced these biases.
  • Microstructure measures (AD, MD, RD), bundle length, and connectome metrics were least biased by acquisition differences.

Conclusions:

  • A machine learning approach was most effective for harmonizing connectomic, microstructure, and macrostructure features, but requires co-registered data.
  • The NeSH resampling method also proved effective and offers a generalizable framework independent of co-registration.
  • These harmonization techniques are essential for reliable large-scale DW-MRI studies in neurological diseases.