Optimisation of quantitative brain diffusion-relaxation MRI acquisition protocols with physics-informed machine learning

Diffusion-relaxation MRI aims to extract quantitative measures that characterise microstructural tissue properties such as orientation, size, and shape, but long acquisition times are typically required. This work proposes a physics-informed learning framework to extract an optimal subset of diffusion-relaxation MRI measurements for enabling shorter acquisition times, predict non-measured signals, and estimate quantitative parameters. In vivo and synthetic brain 5D-Diffusion- T 1 - T 2 ∗ -weighted MRI data obtained from five healthy subjects were used for training and validation, and from a sixth participant for testing. One fully data-driven and two physics-informed machine learning methods were implemented and compared to two manual selection procedures and Cramér-Rao lower bound optimisation. The physics-informed approaches could identify measurement-subsets that yielded more consistently accurate parameter estimates in simulations than other approaches, with similar signal prediction error. Five-fold shorter protocols yielded error distributions of estimated quantitative parameters with very small effect sizes compared to estimates from the full protocol. Selected subsets commonly included a denser sampling of the shortest and longest inversion time, lowest echo time, and high b-value. The proposed framework combining machine learning and MRI physics offers a promising approach to develop shorter imaging protocols without compromising the quality of parameter estimates and signal predictions

Optimisation of quantitative brain diffusion-relaxation MRI acquisition protocols with physics-informed machine learning

Diffusion-relaxation MRI aims to extract quantitative measures that characterise microstructural tissue properties such as orientation, size, and shape, but long acquisition times are typically required. This work proposes a physics-informed learning framework to extract an optimal subset of diffusion-relaxation MRI measurements for enabling shorter acquisition times, predict non-measured signals, and estimate quantitative parameters. In vivo and synthetic brain 5D-Diffusion-T1-T2∗-weighted MRI data obtained from five healthy subjects were used for training and validation, and from a sixth participant for testing. One fully data-driven and two physics-informed machine learning methods were implemented and compared to two manual selection procedures and Cramér–Rao lower bound optimisation. The physics-informed approaches could identify measurement-subsets that yielded more consistently accurate parameter estimates in simulations than other approaches, with similar signal prediction error. Five-fold shorter protocols yielded error distributions of estimated quantitative parameters with very small effect sizes compared to estimates from the full protocol. Selected subsets commonly included a denser sampling of the shortest and longest inversion time, lowest echo time, and high b-value. The proposed framework combining machine learning and MRI physics offers a promising approach to develop shorter imaging protocols without compromising the quality of parameter estimates and signal predictions

Relationships between varus–valgus laxity of the severely osteoarthritic knee and gait, instability, clinical performance, and function

Increased varus–valgus laxity has been reported in individuals with knee osteoarthritis (OA) compared to controls. However, the majority of previous investigations may not report truly passive joint laxity, as their tests have been performed on conscious participants who could be guarding against motion with muscle contraction during laxity evaluation. The purpose of this study was to investigate how a measure of passive knee laxity, recorded when the participant is under anesthesia, is related to varus–valgus excursion during gait, clinical measures of performance, perceived instability, and self‐reported function in participants with severe knee OA. We assessed passive varus–valgus knee laxity in 29 participants (30 knees) with severe OA, as they underwent total knee arthroplasty (TKA). Participants also completed gait analysis, clinical assessment of performance (6‐min walk (6 MW), stair climbing test (SCT), isometric knee strength), and self‐reported measures of function (perceived instability, Knee injury, and Osteoarthritis Outcome Score (KOOS) a median of 18 days before the TKA procedure. We observed that greater passive varus–valgus laxity was associated with greater varus–valgus excursion during gait (R2 = 0.34, p = 0.002). Significant associations were also observed between greater laxity and greater isometric knee extension strength (p = 0.014), farther 6 MW distance (p = 0.033) and shorter SCT time (p = 0.046). No relationship was observed between passive varus–valgus laxity and isometric knee flexion strength, perceived instability, or any KOOS subscale. The conflicting associations between laxity, frontal excursion during gait, and functional performance suggest a complex relationship between laxity and knee cartilage health, clinical performance, and self‐reported function that merits further study. © 2016 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:1644–1652, 2017

Acquiring and Predicting Multidimensional Diffusion (MUDI) Data:An Open Challenge

In magnetic resonance imaging (MRI), the image contrast is the result of the subtle interaction between the physicochemical properties of the imaged living tissue and the parameters used for image acquisition. By varying parameters such as the echo time (TE) and the inversion time (TI), it is possible to collect images that capture different expressions of this sophisticated interaction. Sensitization to diffusion-summarized by the b-value-constitutes yet another explorable “dimension” to modify the image contrast, which reflects the degree of dispersion of water in various directions within the tissue microstructure. The full exploration of this multidimensional acquisition parameter space offers the promise of a more comprehensive description of the living tissue but at the expense of lengthy MRI acquisitions, often unfeasible in clinical practice. The harnessing of multidimensional information passes through the use of intelligent sampling strategies for reducing the amount of images to acquire, and the design of methods for exploiting the redundancy in such information. This chapter reports the results of the MUDI challenge, comparing different strategies for predicting the acquired densely sampled multidimensional data from sub-sampled versions of it