Frontiers in Physics (Nov 2021)

Feasibility of Data-Driven, Model-Free Quantitative MRI Protocol Design: Application to Brain and Prostate Diffusion-Relaxation Imaging

  • Francesco Grussu,
  • Francesco Grussu,
  • Francesco Grussu,
  • Stefano B. Blumberg,
  • Marco Battiston,
  • Lebina S. Kakkar,
  • Hongxiang Lin,
  • Andrada Ianuş,
  • Torben Schneider,
  • Torben Schneider,
  • Saurabh Singh,
  • Roger Bourne,
  • Shonit Punwani,
  • David Atkinson,
  • Claudia A. M. Gandini Wheeler-Kingshott,
  • Claudia A. M. Gandini Wheeler-Kingshott,
  • Claudia A. M. Gandini Wheeler-Kingshott,
  • Eleftheria Panagiotaki,
  • Thomy Mertzanidou,
  • Daniel C. Alexander

DOI
https://doi.org/10.3389/fphy.2021.752208
Journal volume & issue
Vol. 9

Abstract

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Purpose: We investigate the feasibility of data-driven, model-free quantitative MRI (qMRI) protocol design on in vivo brain and prostate diffusion-relaxation imaging (DRI).Methods: We select subsets of measurements within lengthy pilot scans, without identifying tissue parameters for which to optimise for. We use the “select and retrieve via direct upsampling” (SARDU-Net) algorithm, made of a selector, identifying measurement subsets, and a predictor, estimating fully-sampled signals from the subsets. We implement both using artificial neural networks, which are trained jointly end-to-end. We deploy the algorithm on brain (32 diffusion-/T1-weightings) and prostate (16 diffusion-/T2-weightings) DRI scans acquired on three healthy volunteers on two separate 3T Philips systems each. We used SARDU-Net to identify sub-protocols of fixed size, assessing reproducibility and testing sub-protocols for their potential to inform multi-contrast analyses via the T1-weighted spherical mean diffusion tensor (T1-SMDT, brain) and hybrid multi-dimensional MRI (HM-MRI, prostate) models, for which sub-protocol selection was not optimised explicitly.Results: In both brain and prostate, SARDU-Net identifies sub-protocols that maximise information content in a reproducible manner across training instantiations using a small number of pilot scans. The sub-protocols support T1-SMDT and HM-MRI multi-contrast modelling for which they were not optimised explicitly, providing signal quality-of-fit in the top 5% against extensive sub-protocol comparisons.Conclusions: Identifying economical but informative qMRI protocols from subsets of rich pilot scans is feasible and potentially useful in acquisition-time-sensitive applications in which there is not a qMRI model of choice. SARDU-Net is demonstrated to be a robust algorithm for data-driven, model-free protocol design.

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