A data‐driven approach to optimising the encoding for multi‐shell diffusion MRI with application to neonatal imaging. (6th July 2020) - Tournier, Jacques‐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; et al.
Abstract : Diffusion MRI has the potential to provide important information about the connectivity and microstructure of the human brain during normal and abnormal development, noninvasively 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 (dHCP), 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 diffusion‐weighted images at b = 400, 1000 and 2600 s/mm 2 with 64, 88 and 128 directions per shell, respectively. Abstract : Using singular value decomposition, the mean diffusion‐weighted signal per b ‐value shell can be represented as the linear combination of independent components, each with its own b ‐value dependence and associated spatial weights map. Each component accounts for a decreasing amount of variance in the signal; in our neonatal data, we observe four components with clear anatomical structure. We use this decomposition to optimise the parameters of the diffusion encoding for sensitivity to these components. Highlights: A data‐driven method is presented to design multi‐shell diffusion MRI acquisition schemes ( b ‐values and no. of directions). This method optimises the multi‐shell scheme for maximum sensitivity to the information content in the signal. When applied in neonates, the data suggest that a b=0 + 3 shell strategy is appropriate.