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Time-efficient and flexible design of optimised multi-shell HARDI diffusion

Research output: Contribution to journalArticle

Jana Hutter, J. Donald Tournier, Anthony N. Price, Lucilio Cordero-Grande, Emer J. Hughes, Shaihan Malik, Johannes Steinweg, Matteo Bastiani, Stamatios N. Sotiropoulos, Saad Jbabdi, Jesper Andersson, A. David Edwards, Joseph V. Hajnal

Original languageEnglish
JournalMagnetic Resonance in Medicine
Early online date30 May 2017
DOIs
StateE-pub ahead of print - 30 May 2017

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Abstract

Purpose: Advanced diffusion magnetic resonance imaging benefits from collecting as much data as is feasible but is highly sensitive to subject motion and the risk of data loss increases with longer acquisition times. Our purpose was to create a maximally time-efficient and flexible diffusion acquisition capability with built-in robustness to partially acquired or interrupted scans. Our framework has been developed for the developing Human Connectome Project, but different application domains are equally possible. 
Methods: Complete flexibility in the sampling of diffusion space combined with free choice of phase-encode-direction and the temporal ordering of the sampling scheme was developed taking into account motion robustness, internal consistency, and hardware limits. A split-diffusion-gradient preparation, multiband acceleration, and a restart capacity were added. 
Results: The framework was used to explore different parameters choices for the desired high angular resolution diffusion imaging diffusion sampling. For the developing Human Connectome Project, a high-angular resolution, maximally time-efficient (20 min) multishell protocol with 300 diffusion-weighted volumes was acquired in >400 neonates. An optimal design of a high-resolution (1.2 × 1.2 mm2) two-shell acquisition with 54 diffusion weighted volumes was obtained using a split-gradient design. 
Conclusion: The presented framework provides flexibility to generate time-efficient and motion-robust diffusion magnetic resonance imaging acquisitions taking into account hardware constraints that might otherwise result in sub-optimal choices.

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