"Select and retrieve via direct upsampling" network (SARDU-Net): a data-driven, model-free, deep learning approach for quantitative MRI protocol design

Purpose: We introduce "Select and retrieve via direct upsampling" network (SARDU-Net), a data-driven deep learning framework for model-free quantitative MRI (qMRI) experiment design. Here we provide a practical demonstration of its utility on in vivo joint diffusion-relaxation imaging (DRI) of the prostate. Methods: SARDU-Net selects subsets of informative qMRI measurements within lengthy pilot scans. The algorithm consists of two deep neural networks (DNNs) that are trained jointly end-to-end: a selector, identifying a subset of input qMRI measurements, and a predictor, estimating fully-sampled signals from such a subset. We studied 3T prostate DRI scans performed on 3 healthy volunteers with 16 unique (b,TE) values (diffusion-/T2-weighting), and used SARDU-Net to identify sub-protocols of 12 and 9 measurements. The reproducibility of the sub-protocol selection was evaluated, and sub-protocols were assessed for their potential of informing multi-contrast analysis, as for example Hybrid Multi-dimensional MRI (HM-MRI). Results: SARDU-Net identifies informative sub-protocols of specified size from a small number of pilot scans. The procedure is reproducible across training folds and random initialisations. Moreover, SARDU-Net sub-protocols corresponding to up to ~50% scan time reduction support downstream HM-MRI modelling for which they were not optimised explicitly, providing quality of fit in the top 5% of all tested sub-protocols. Conclusions: SARDU-Net gives new opportunity to identify economical but informative qMRI data sets for clinical applications under high time pressure in a fully data-driven way from a few pilot scans. The simple SARDU-Net architecture makes the algorithm easy to train and appealing when extensive sub-protocol searches are intractable.