  Head motion is inevitable in the acquisition of diffusion-weighted images,
especially for certain motion-prone subjects and for data gathering of advanced
diffusion models with prolonged scan times. Deficient accuracy of motion
correction cause deterioration in the quality of diffusion model
reconstruction, thus affecting the derived measures. This results in either
loss of data, or introducing bias in outcomes from data of different motion
levels, or both. Hence minimizing motion effects and reutilizing
motion-contaminated data becomes vital to quantitative studies. We have
previously developed a 3-dimensional hierarchical convolution neural network
(3D H-CNN) for robust diffusion kurtosis mapping from under-sampled data. In
this study, we propose to extend this method to motion-contaminated data for
robust recovery of diffusion model-derived measures with a process of motion
assessment and corrupted volume rejection. We validate the proposed pipeline in
two in-vivo datasets. Results from the first dataset of individual subjects
show that all the diffusion tensor and kurtosis tensor-derived measures from
the new pipeline are minimally sensitive to motion effects, and are comparable
to the motion-free reference with as few as eight volumes retained from the
motion-contaminated data. Results from the second dataset of a group of
children with attention deficit hyperactivity disorder demonstrate the ability
of our approach in ameliorating spurious group differences due to head motion.
This method shows great potential for exploiting some valuable but
motion-corrupted DWI data which are likely to be discarded otherwise, and
applying to data with different motion level thus improving their utilization
and statistic power.
