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

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

Feasibility of data-driven, model-free quantitative MRI protocol design: application to brain and prostate diffusion-relaxation imaging

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

Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise

Generation of realistic white matter substrates with controllable morphology for diffusion MRI simulations

Numerical phantoms have played a key role in the development of diffusion MRI (dMRI) techniques seeking to estimate features of the microscopic structure of tissue by providing a ground truth for simulation experiments against which we can validate and compare techniques. One common limitation of numerical phantoms which represent white matter (WM) is that they oversimplify the true complex morphology of the tissue which has been revealed through ex vivo studies. It is important to try to generate WM numerical phantoms that capture this realistic complexity in order to understand how it impacts the dMRI signal. This thesis presents work towards improving the realism of WM numerical phantoms by generating fibres mimicking natural fibre genesis. A novel phantom generator is presented which was developed over two works, resulting in Contextual Fibre Growth (ConFiG). ConFiG grows fibres one-by-one, following simple rules motivated by real axonal guidance mechanisms. These simple rules enable ConFiG to generate phantoms with tuneable microstructural features by growing fibres while attempting to meet morphological targets such as user-specified density and orientation distribution. We compare ConFiG to the state-of-the-art approach based on packing fibres together by generating phantoms in a range of fibre configurations including crossing fibre bundles and orientation dispersion. Results demonstrate that ConFiG produces phantoms with up to 20% higher densities than the state-of-the-art, particularly in complex configurations with crossing fibres. We additionally show that the microstructural morphology of ConFiG phantoms is comparable to real tissue, producing diameter and orientation distributions close to electron microscopy estimates from real tissue as well as capturing complex fibre cross sections. ConFiG is applied to investigate the intra-axonal diffusivity and probe assumptions in a family of dMRI modelling techniques based on spherical deconvolution (SD), demonstrating that the microscopic variations in fibres’ shapes affects the diffusion within axons. This leads to variations in the per-fibre signal contrary to the assumptions inherent in SD which may have a knock-on effect in popular techniques such as tractography

Microstructure Imaging in the Human Brain with Advanced Diffusion MRI and Machine Learning

Today, a plethora of model-based diffusion MRI (dMRI) techniques exist that aim to provide quantitative metrics of cellular-scale tissue properties. In the brain, many of these techniques focus on cylindrical projections such as axons and dendrites. Capturing additional tissue features is challenging, as conventional dMRI measurements have limited sensitivity to different cellular components, and modelling cellular architecture is not trivial in heterogeneous tissues such as grey matter. Additionally, fitting complex non-linear models with traditional techniques can be time-consuming and prone to local minima, which hampers their widespread use. In this thesis, we harness recent advances in measurement technology and modelling efforts to tackle these challenges. We probe the utility of B-tensor encoding, a technique that offers additional sensitivity to tissue microstructure compared to conventional measurements, and observe that B-tensor encoding provides unique contrast in grey matter. Motivated by this and recent work showing that the diffusion signature of soma in grey matter may be captured with spherical compartments, we use B-tensor encoding measurements and a biophysical model to disentangle spherical and cylindrical cellular structures. We map apparent markers of these geometries in healthy human subjects and evaluate the extent to which they may be interpreted as correlates of soma and projections. To ensure fast and robust model fitting, we use supervised machine learning (ML) to estimate parameters. We explore limitations in ML fitting in several microstructure models, including the model developed here, and demonstrate that the choice of training data significantly impacts estimation performance. We highlight that high precision obtained using ML may mask strong biases and that visual assessment of the parameter maps is not sufficient for evaluating the quality of the estimates. We believe that the methods developed in this work provide new insight into the reliability and potential utility of advanced dMRI and ML in microstructure imaging