Year: 2020
Type: article
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.
Source: NeuroImage
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,000
Funders: King’s College London, Wellcome Trust, NIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer Research, National Institute for Health and Care Research, King's College London, European Commission, Fonds Wetenschappelijk Onderzoek, Seventh Framework Programme, Medical Research Council, Engineering and Physical Sciences Research Council, European Research Council, Guy's and St Thomas' NHS Foundation Trust, Seventh Framework Programme, Centre For Medical Engineering, King’s College London
Awards: G1054121244, G1093531541, G120984652, G1211483440, G1247657515, King's College London Medical Engineering Centre of Research Excellence, Non invasive assessment of human placental function by quantitative MRI, The Developing Brain, G2417159676, G2681742788, G393177772, Non invasive assessment of human placental function by quantitative MRI, Data-driven microstructure imaging with multi-dimensional diffusion MRI in early brain development, G5296857752, G5537678747, G5762252440, G5893523844, King's College London Medical Engineering Centre of Research Excellence, G5935007269, G63770550, G8048453011, G841698483, G8775246936, G8836229864, G938335843, G961285871
OpenAlex ID: W3019456751