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

Abstract

Brain; Protocol design; Quantitative MRI (qMRI)

Cerebro; Diseño de protocolo; Resonancia magnética cuantitativa (qMRI)

Cervell; Disseny del protocol; Ressonància magnètica quantitativa (qMRI)

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.

This project was funded by the Engineering and Physical Sciences Research Council (EPSRC EP/R006032/1, M020533/1, G007748, I027084, N018702). This project has received funding under the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 634541 and 666992, and from: Rosetrees Trust (United Kingdom, funding FG); Prostate Cancer United Kingdom Targeted Call 2014 (Translational Research St.2, project reference PG14-018-TR2); Cancer Research United Kingdom grant ref. A21099; Spinal Research (United Kingdom), Wings for Life (Austria), Craig H. Neilsen Foundation (United States) for jointly funding the INSPIRED study; Wings for Life (#169111); United Kingdom Multiple Sclerosis Society (grants 892/08 and 77/2017); the Department of Health’s National Institute for Health Research (NIHR) Biomedical Research Centres and UCLH NIHR Biomedical Research Centre; Champalimaud Centre for the Unknown, Lisbon (Portugal); European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 101003390. FG is currently supported by the investigator-initiated PREdICT study at the Vall d’Hebron Institute of Oncology (Barcelona), funded by AstraZeneca and CRIS Cancer Foundation.