Compressed sensing MRI: a review from signal processing perspective
JC Ye - BMC Biomedical Engineering, 2019 - Springer
Magnetic resonance imaging (MRI) is an inherently slow imaging modality, since it acquires
multi-dimensional k-space data through 1-D free induction decay or echo signals. This often …
multi-dimensional k-space data through 1-D free induction decay or echo signals. This often …
Learning a variational network for reconstruction of accelerated MRI data
Purpose To allow fast and high‐quality reconstruction of clinical accelerated multi‐coil MR
data by learning a variational network that combines the mathematical structure of …
data by learning a variational network that combines the mathematical structure of …
Accelerating magnetic resonance imaging via deep learning
This paper proposes a deep learning approach for accelerating magnetic resonance
imaging (MRI) using a large number of existing high quality MR images as the training …
imaging (MRI) using a large number of existing high quality MR images as the training …
Deep-learning methods for parallel magnetic resonance imaging reconstruction: A survey of the current approaches, trends, and issues
Following the success of deep learning in a wide range of applications, neural network-
based machine-learning techniques have received interest as a means of accelerating …
based machine-learning techniques have received interest as a means of accelerating …
Low-cost high-performance MRI
M Sarracanie, CD LaPierre, N Salameh… - Scientific reports, 2015 - nature.com
Abstract Magnetic Resonance Imaging (MRI) is unparalleled in its ability to visualize
anatomical structure and function non-invasively with high spatial and temporal resolution …
anatomical structure and function non-invasively with high spatial and temporal resolution …
Systematic evaluation of iterative deep neural networks for fast parallel MRI reconstruction with sensitivity‐weighted coil combination
Purpose To systematically investigate the influence of various data consistency layers and
regularization networks with respect to variations in the training and test data domain, for …
regularization networks with respect to variations in the training and test data domain, for …
[HTML][HTML] A review of 3D first-pass, whole-heart, myocardial perfusion cardiovascular magnetic resonance
MJ Fair, PD Gatehouse, EVR DiBella… - Journal of Cardiovascular …, 2015 - Elsevier
A comprehensive review is undertaken of the methods available for 3D whole-heart first-
pass perfusion (FPP) and their application to date, with particular focus on possible …
pass perfusion (FPP) and their application to date, with particular focus on possible …
A parallel MR imaging method using multilayer perceptron
Purpose To reconstruct MR images from subsampled data, we propose a fast reconstruction
method using the multilayer perceptron (MLP) algorithm. Methods and materials We applied …
method using the multilayer perceptron (MLP) algorithm. Methods and materials We applied …
Fast quantitative susceptibility mapping using 3D EPI and total generalized variation
Quantitative susceptibility mapping (QSM) allows new insights into tissue composition and
organization by assessing its magnetic property. Previous QSM studies have already …
organization by assessing its magnetic property. Previous QSM studies have already …
Fast -SPIRiT Compressed Sensing Parallel Imaging MRI: Scalable Parallel Implementation and Clinically Feasible Runtime
We present \ell_1-SPIRiT, a simple algorithm for auto calibrating parallel imaging (acPI) and
compressed sensing (CS) that permits an efficient implementation with clinically-feasible …
compressed sensing (CS) that permits an efficient implementation with clinically-feasible …