A data-driven approach to optimising the encoding for multi-shell diffusion MRI with application to neonatal imaging

Tournier, J-Donald; Christiaens, Daan; Hutter, Jana; Price, Anthony N.; Cordero-Grande, Lucilio; Hughes, Emer; Bastiani, Matteo; Sotiropoulos, Stamatios N.; Smith, Stephen M.; Rueckert, Daniel; Counsell, Serena J.; Edwards, A. David; Hajnal, Joseph V.

Abstract

Diffusion MRI has the potential to provide important information about the connectivity and microstructure of the human brain during normal and abnormal development, non-invasively and in vivo. Recent developments in MRI hardware and reconstruction methods now permit the acquisition of large amounts of data within relatively short scan times. This makes it possible to acquire more informative multi-shell data, with diffusion-sensitisation applied along many directions over multiple b-value shells. Such schemes are characterised by the number of shells acquired, and the specific b-value and number of directions sampled for each shell. However, there is currently no clear consensus as to how to optimise these parameters. In this work, we propose a means of optimising multi-shell acquisition schemes by estimating the information content of the diffusion MRI signal, and optimising the acquisition parameters for sensitivity to the observed effects, in a manner agnostic to any particular diffusion analysis method that might subsequently be applied to the data. This method was used to design the acquisition scheme for the neonatal diffusion MRI sequence used in the developing Human Connectome Project, which aims to acquire high quality data and make it freely available to the research community. The final protocol selected by the algorithm, and currently in use within the dHCP, consists of 20 b = 0 images and DW images at b = 400, 1000, 2600 s/mm2 with 64, 88, and 128 directions per shell respectively.

Citation

Tournier, J., Christiaens, D., Hutter, J., Price, A. N., Cordero-Grande, L., Hughes, E., …Hajnal, J. V. (2020). A data-driven approach to optimising the encoding for multi-shell diffusion MRI with application to neonatal imaging. NMR in Biomedicine, 33(9), Article e4348. https://doi.org/10.1101/661348

• Journal Article Type: Article
• Acceptance Date: May 18, 2020
• Online Publication Date: Jul 6, 2020
• Publication Date: Sep 1, 2020
• Deposit Date: May 18, 2020
• Publicly Available Date: Jul 7, 2021
• Journal: NMR in Biomedicine
• Print ISSN: 0952-3480
• Electronic ISSN: 1099-1492
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 33
• Issue: 9
• Article Number: e4348
• DOI: https://doi.org/10.1101/661348
• Keywords: Diffusion MRI, HARDI, Multi-shell, Neonatal imaging
• Public URL: https://nottingham-repository.worktribe.com/output/2467015
• Publisher URL: https://onlinelibrary.wiley.com/doi/full/10.1002/nbm.4348