  Diffusion MRI offers a unique probe into neural microstructure and
connectivity in the developing brain. However, analysis of neonatal brain
imaging data is complicated by inevitable subject motion, leading to a series
of scattered slices that need to be aligned within and across
diffusion-weighted contrasts. Here, we develop a reconstruction method for
scattered slice multi-shell high angular resolution diffusion imaging (HARDI)
data, jointly estimating an uncorrupted data representation and motion
parameters at the slice or multiband excitation level. The reconstruction
relies on data-driven representation of multi-shell HARDI data using a bespoke
spherical harmonics and radial decomposition (SHARD), which avoids imposing
model assumptions, thus facilitating to compare various microstructure imaging
methods in the reconstructed output. Furthermore, the proposed framework
integrates slice-level outlier rejection, distortion correction, and slice
profile correction. We evaluate the method in the neonatal cohort of the
developing Human Connectome Project (650 scans). Validation experiments
demonstrate accurate slice-level motion correction across the age range and
across the range of motion in the population. Results in the neonatal data show
successful reconstruction even in severely motion-corrupted subjects. In
addition, we illustrate how local tissue modelling can extract advanced
microstructure features such as orientation distribution functions from the
motion-corrected reconstructions.
