1 citations · 1 across the 6 of their papers we have counts for
7 papers
Combined Diffusion-Relaxation MRI to Assess Muscle Microstructure and Composition
Matteo Figini, Paddy J. Slator, Valeria E. Contarino +3
Quantifying muscle tissue properties is crucial for understanding pathophysiological changes occurring in skeletal muscle (SM). In particular, T2 relaxation and diffusion MRI (dMRI…
Ultra-Strong Gradient Diffusion MRI with Self-Supervised Learning for Prostate Cancer Characterization
Tanishq Patil, Snigdha Sen, Kieran G. Foley +7
Diffusion MRI (dMRI) enables non-invasive assessment of prostate microstructure but conventional dMRI metrics such as the Apparent Diffusion Coefficient in multiparametric MRI and…
MRI Parameter Mapping via Gaussian Mixture VAE: Breaking the Assumption of Independent Pixels
Moucheng Xu, Yukun Zhou, Tobias Goodwin-Allcock +4
We introduce and demonstrate a new paradigm for quantitative parameter mapping in MRI. Parameter mapping techniques, such as diffusion MRI and quantitative MRI, have the potential…
ssVERDICT: Self-Supervised VERDICT-MRI for Enhanced Prostate Tumour Characterisation
Snigdha Sen, Saurabh Singh, Hayley Pye +6
Purpose: Demonstrating and assessing self-supervised machine learning fitting of the VERDICT (Vascular, Extracellular and Restricted DIffusion for Cytometry in Tumours) model for p…
Fitting a Directional Microstructure Model to Diffusion-Relaxation MRI Data with Self-Supervised Machine Learning
Jason P. Lim, Stefano B. Blumberg, Neil Narayan +4
Machine learning is a powerful approach for fitting microstructural models to diffusion MRI data. Early machine learning microstructure imaging implementations trained regressors t…
Experimental Design for Multi-Channel Imaging via Task-Driven Feature Selection
Stefano B. Blumberg, Paddy J. Slator, Daniel C. Alexander
This paper presents a data-driven, task-specific paradigm for experimental design, to shorten acquisition time, reduce costs, and accelerate the deployment of imaging devices. Curr…