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

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

JOURNAL=Frontiers in Physics

VOLUME=9

YEAR=2021

URL=https://www.frontiersin.org/journals/physics/articles/10.3389/fphy.2021.752208

DOI=10.3389/fphy.2021.752208

ISSN=2296-424X

ABSTRACT=<p><bold>Purpose:</bold> We investigate the feasibility of data-driven, model-free quantitative MRI (qMRI) protocol design on <italic>in vivo</italic> brain and prostate diffusion-relaxation imaging (DRI).</p><p><bold>Methods:</bold> We select subsets of measurements within lengthy pilot scans, without identifying tissue parameters for which to optimise for. We use the “<italic>select and retrieve via direct upsampling</italic>” (SARDU-Net) algorithm, made of a <italic>selector</italic>, identifying measurement subsets, and a <italic>predictor</italic>, 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 <italic>T1-weighted spherical mean diffusion tensor</italic> (T1-SMDT, brain) and <italic>hybrid multi-dimensional</italic> MRI (HM-MRI, prostate) models, for which sub-protocol selection was not optimised explicitly.</p><p><bold>Results:</bold> 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.</p><p><bold>Conclusions:</bold> 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.</p>