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Head motion is inevitable in the acquisition of diffusion-weighted images,especially for certain motion-prone subjects and for data gathering of advanceddiffusion models with prolonged scan times. Deficient accuracy of motioncorrection cause deterioration in the quality of diffusion modelreconstruction, thus affecting the derived measures. This results in eitherloss of data, or introducing bias in outcomes from data of different motionlevels, or both. Hence minimizing motion effects and reutilizingmotion-contaminated data becomes vital to quantitative studies. We havepreviously developed a 3-dimensional hierarchical convolution neural network(3D H-CNN) for robust diffusion kurtosis mapping from under-sampled data. Inthis study, we propose to extend this method to motion-contaminated data forrobust recovery of diffusion model-derived measures with a process of motionassessment and corrupted volume rejection. We validate the proposed pipeline intwo in-vivo datasets. Results from the first dataset of individual subjectsshow that all the diffusion tensor and kurtosis tensor-derived measures fromthe new pipeline are minimally sensitive to motion effects, and are comparableto the motion-free reference with as few as eight volumes retained from themotion-contaminated data. Results from the second dataset of a group ofchildren with attention deficit hyperactivity disorder demonstrate the abilityof our approach in ameliorating spurious group differences due to head motion.This method shows great potential for exploiting some valuable butmotion-corrupted DWI data which are likely to be discarded otherwise, andapplying to data with different motion level thus improving their utilizationand statistic power.

Robust diffusion parametric mapping of motion-corrupted data with a three-dimensional convolutional neural network