Image reconstruction: From sparsity to data-adaptive methods and machine learning
The field of medical image reconstruction has seen roughly four types of methods. The first
type tended to be analytical methods, such as filtered backprojection (FBP) for X-ray …
type tended to be analytical methods, such as filtered backprojection (FBP) for X-ray …
Transform learning for magnetic resonance image reconstruction: From model-based learning to building neural networks
Magnetic resonance imaging (MRI) is widely used in clinical practice, but it has been
traditionally limited by its slow data acquisition. Recent advances in compressed sensing …
traditionally limited by its slow data acquisition. Recent advances in compressed sensing …
Deep-learning-based optimization of the under-sampling pattern in MRI
In compressed sensing MRI (CS-MRI), k-space measurements are under-sampled to
achieve accelerated scan times. CS-MRI presents two fundamental problems:(1) where to …
achieve accelerated scan times. CS-MRI presents two fundamental problems:(1) where to …
Reducing uncertainty in undersampled MRI reconstruction with active acquisition
The goal of MRI reconstruction is to restore a high fidelity image from partially observed
measurements. This partial view naturally induces reconstruction uncertainty that can only …
measurements. This partial view naturally induces reconstruction uncertainty that can only …
Learning-based compressive MRI
In the area of magnetic resonance imaging (MRI), an extensive range of non-linear
reconstruction algorithms has been proposed which can be used with general Fourier …
reconstruction algorithms has been proposed which can be used with general Fourier …
Active MR k-space Sampling with Reinforcement Learning
Deep learning approaches have recently shown great promise in accelerating magnetic
resonance image (MRI) acquisition. The majority of existing work have focused on designing …
resonance image (MRI) acquisition. The majority of existing work have focused on designing …
Fast data-driven learning of parallel MRI sampling patterns for large scale problems
In this study, a fast data-driven optimization approach, named bias-accelerated subset
selection (BASS), is proposed for learning efficacious sampling patterns (SPs) with the …
selection (BASS), is proposed for learning efficacious sampling patterns (SPs) with the …
Learning the sampling pattern for MRI
The discovery of the theory of compressed sensing brought the realisation that many inverse
problems can be solved even when measurements are “incomplete”. This is particularly …
problems can be solved even when measurements are “incomplete”. This is particularly …
Self-supervised deep active accelerated MRI
We propose to simultaneously learn to sample and reconstruct magnetic resonance images
(MRI) to maximize the reconstruction quality given a limited sample budget, in a self …
(MRI) to maximize the reconstruction quality given a limited sample budget, in a self …
Data-and Physics-driven Deep Learning Based Reconstruction for Fast MRI: Fundamentals and Methodologies
Magnetic Resonance Imaging (MRI) is a pivotal clinical diagnostic tool, yet its extended
scanning times often compromise patient comfort and image quality, especially in …
scanning times often compromise patient comfort and image quality, especially in …