Abstract
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.
Highlights
Diffusion magnetic resonance imaging offers a unique probe into brain microstructure and connectivity throughout the lifespan (Le Bihan et al, 1986)
Preprint submitted to arXiv volume-level motion correction can be regarded as a multicontrast image registration problem, in which the acquired image volumes are interpolated into the moving subject reference frame
In such groups, such as the neonatal cohort used in this work, effective motion correction needs to realign the individual slices into a self-consistent image
Summary
Diffusion magnetic resonance imaging (dMRI) offers a unique probe into brain microstructure and connectivity throughout the lifespan (Le Bihan et al, 1986). The first approaches to dMRI motion correction operated on a volume level, retrospectively seeking a rigid transformation for each image volume that describes the head motion of the subject during the scan (Rohde et al, 2004; Andersson and Sotiropoulos, 2016). Subject motion can occur between slices, as much as between volumes, leading to substantial intra-volume motion artefacts in less compliant subject groups. In such groups, such as the neonatal cohort used in this work, effective motion correction needs to realign the individual slices into a self-consistent image
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