Assessment of data consistency through cascades of independently recurrent inference machines for fast and robust accelerated MRI reconstruction

Objective. Machine Learning methods can learn how to reconstruct magnetic resonance images (MRI) and thereby accelerate acquisition, which is of paramount importance to the clinical workflow. Physics-informed networks incorporate the forward model of accelerated MRI reconstruction in the learning process. With increasing network complexity,...

Recurrent inference machines as inverse problem solvers for MR relaxometry

In this paper, we propose the use of Recurrent Inference Machines (RIMs) to perform T₁ and T₂ mapping. The RIM is a neural network framework that learns an iterative inference process based on the signal model, similar to conventional statistical methods for quantitative MRI (QMRI), such as the Maximum Likelihood...

Sharpness in motion corrected quantitative imaging at 7T

Sub-millimeter imaging at 7T has opened new possibilities for qualitatively and quantitatively studying brain structure as it evolves throughout the life span. However, subject motion introduces image blurring on the order of magnitude of the spatial resolution and is thus detrimental to image quality. Such motion can be corrected for, but...

Spatial versus angular resolution for tractography-assisted planning of deep brain stimulation

Given the restricted total scanning time for clinical neuroimaging, it is unclear whether clinical diffusion MRI protocols would benefit more from higher spatial resolution or higher angular resolution. In this work, we investigated the relative benefit of improving spatial or angular resolution in diffusion MRI to separate two parallel...

White matter by diffusion MRI following methylphenidate treatment: A randomized control trial in males with attention-deficit/hyperactivity disorder

Background: Methylphenidate (MPH) is highly effective in treating attention-deficit/hyperactivity disorder (ADHD). However, not much is known about its effect on the development of human brain white matter (WM). Purpose: To determine whether MPH modulates WM microstructure in an age-dependent fashion in a randomized double-blind placebo...

Reliable Dual Tensor Model Estimation in Single and Crossing Fibers Based on Jeffreys Prior

Purpose<br/>This paper presents and studies a framework for reliable modeling of diffusion MRI using a data-acquisition adaptive prior.<br/><br/>Methods<br/>Automated relevance determination estimates the mean of the posterior distribution of a rank-2 dual tensor model exploiting Jeffreys prior (JARD). This data-acquisition prior is based on the...

Quantifying Pathology in Diffusion Weighted MRI

In this thesis algorithms are proposed for quantification of pathology in Diffusion Weighted Magnetic Resonance Imaging (DW-MRI) data. Functional evidence for brain diseases can be explained by specific structural loss in the white matter of the brain. That is, certain biomarkers may exist where the disease inhibits improper functioning. Axonal...

Estimation of Diffusion Properties in Crossing Fiber Bundles

There is an ongoing debate on how to model diffusivity in fiber crossings. We propose an optimization framework for the selection of a dual tensor model and the set of diffusion weighting parameters b, such that both the diffusion shape and orientation parameters can be precisely as well as accurately estimated. For that, we have adopted the...