Data-driven characterization and correction of the orientation dependence of magnetization transfer measures using diffusion MRI
Karan, P.; Edde, M.; Gilbert, G.; Barakovic, M.; Magon, S.; Descoteaux, M.
Show abstract
PurposeTo characterize the orientation dependence of magnetization transfer (MT) measures in white matter (WM) and propose a first correction method for such measures. MethodsA characterization method was developed using the fiber orientation obtained from diffusion MRI (dMRI) with diffusion tensor imaging (DTI) and constrained spherical deconvolution (CSD). This allowed for characterization of the orientation dependence of measures in all of WM, regardless of the number of fiber orientation in a voxel. Furthermore, a first correction method was proposed from the results of characterization, aiming at removing said orientation dependence. Both methods were tested on a 20-subject dataset and effects on tractometry results were also evaluated. ResultsPrevious results for single-fiber voxels were reproduced and a novel characterization was produced in voxels of crossing fibers, which seems to follow trends consistent with single-fiber results. Unwanted effects of the orientation dependence on MT measures were highlighted, for which the correction method was able to produce improved results. ConclusionEncouraging results of corrected MT measures showed the importance of such correction, opening the door for future research on the topic.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- High-frequency longitudinal white matter diffusion- & myelin-based MRI database: reliability and variability 96%
- Prevalence of white matter pathways coming into a single diffusion MRI voxel orientation: the bottleneck issue in tractography 96%
- Towards an informed choice of diffusion MRI image contrasts for cerebellar segmentation 96%
Similar papers in this journal
- Investigating apparent differences between standard DKI and axisymmetric DKI and its consequences for biophysical parameter estimates 97%
- Axisymmetric diffusion kurtosis imaging with Rician bias correction: A simulation study 96%
- Training Data Distribution Significantly Impacts the Estimation of Tissue Microstructure with Machine Learning 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.