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Mapping the Impact of Approximate Gradient Nonlinearity Fields Correction on Tractography.
Praitayini Kanakaraj1, Francois Rheault2,3, Leon Y Cai4
1Department of Computer Science, Vanderbilt University, Nashville, TN, USA.
Summary
Gradient nonlinearity correction in diffusion MRI tractography shows minimal population-level changes but significant subject-specific variations in microstructural measures and their variability.
Area of Science:
- Neuroimaging
- Diffusion MRI
- Computational Neuroscience
Background:
- Nonlinear gradients in diffusion-weighted MRI introduce spatial variations in diffusion tensor estimation.
- Increasing signal-to-noise ratios and ultra-strong gradients can amplify these nonlinearities, potentially biasing microstructural measures and tractography.
- Accurate tractography is crucial for understanding brain connectivity and diagnosing neurological conditions.
Purpose of the Study:
- To characterize the impact of an approximate gradient nonlinearity correction technique on diffusion MRI tractography.
- To assess the effects of this correction on white matter bundle segmentation and connectomics measures at both population and subject-specific levels.
- To evaluate the within-session variability of these measures before and after correction.
Main Methods:
- An approximate gradient nonlinearity correction technique was applied, scaling the diffusion signal based on gradient magnitude changes.
- Tractography was performed on data from 61 subjects in the MASiVar pediatric reproducibility dataset.
- Evaluated measures included white matter bundle properties (volume, FA, MD, RD, AD, eigenvector, length) and connectomics graph metrics (modularity, global efficiency, characteristic path length).
Main Results:
- The approximate correction showed little to no difference in measures at the population level.
- Significant differences were observed at the subject-specific level for both direct measures and their within-session variability.
- Within-session variability of measures was notably affected by the approximate correction on an individual basis.
Conclusions:
- Approximate correction of gradient nonlinearities may not alter population-level tractography findings.
- Subject-specific interpretations of tractography data can exhibit substantial fluctuations after approximate correction.
- Further investigation, including comparison with empirical voxel-wise correction, is warranted.
