In medical image analysis, the utilization of biophysical models for signal analysis offers
valuable insights into the underlying tissue types and microstructural processes. In diffusion …

Diffusion-relaxation MRI aims to extract quantitative measures that characterise
microstructural tissue properties such as orientation, size, and shape, but long acquisition …

Cancer is a prominent cause of premature mortality worldwide and often preventable
through early detection. Magnetic resonance imaging (MRI) is a vital modality in the …

Purpose: This paper explores different machine learning (ML) algorithms for analyzing
diffusion nuclear magnetic resonance imaging (dMRI) models when analytical fitting shows …

Recent advancements in deep learning-based AI technologies provide an automatic tool to
revolutionise medical image computing. Training a deep learning model requires a large …

Purpose: Gliomas are a highly heterogeneous group of primary brain tumors with poor
prognosis, and treatment monitoring is challenging with its current diagnostic tool being …

Synopsis Diffusion-weighted MRI (DW-MRI) models are traditionally fitted via a
computationally expensive non-linear least squares approach. Recent work has moved to …