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 …
[HTML][HTML] From compressed-sensing to artificial intelligence-based cardiac MRI reconstruction
Cardiac magnetic resonance (CMR) imaging is an important tool for the non-invasive
assessment of cardiovascular disease. However, CMR suffers from long acquisition times …
assessment of cardiovascular disease. However, CMR suffers from long acquisition times …
Scan‐specific robust artificial‐neural‐networks for k‐space interpolation (RAKI) reconstruction: database‐free deep learning for fast imaging
M Akçakaya, S Moeller, S Weingärtner… - Magnetic resonance …, 2019 - Wiley Online Library
Purpose To develop an improved k‐space reconstruction method using scan‐specific deep
learning that is trained on autocalibration signal (ACS) data. Theory Robust artificial‐neural …
learning that is trained on autocalibration signal (ACS) data. Theory Robust artificial‐neural …
Self‐supervised learning of physics‐guided reconstruction neural networks without fully sampled reference data
B Yaman, SAH Hosseini, S Moeller… - Magnetic resonance …, 2020 - Wiley Online Library
Purpose To develop a strategy for training a physics‐guided MRI reconstruction neural
network without a database of fully sampled data sets. Methods Self‐supervised learning via …
network without a database of fully sampled data sets. Methods Self‐supervised learning via …
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 …
DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution
This paper proposes a multi-channel image reconstruction method, named
DeepcomplexMRI, to accelerate parallel MR imaging with residual complex convolutional …
DeepcomplexMRI, to accelerate parallel MR imaging with residual complex convolutional …
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 …
Deep learning for fast MR imaging: A review for learning reconstruction from incomplete k-space data
Magnetic resonance imaging is a powerful imaging modality that can provide versatile
information. However, it has a fundamental challenge that is time consuming to acquire …
information. However, it has a fundamental challenge that is time consuming to acquire …
Statistical analysis of noise in MRI
S Aja-Fernández, G Vegas-Sánchez-Ferrero - Switzerland: Springer …, 2016 - Springer
This work is the result of more than 10 years of research in the area of MRI from a signal and
noise perspective. Our interest has always been to properly model the noise that affects our …
noise perspective. Our interest has always been to properly model the noise that affects our …
[HTML][HTML] A review and experimental evaluation of deep learning methods for MRI reconstruction
Following the success of deep learning in a wide range of applications, neural network-
based machine-learning techniques have received significant interest for accelerating …
based machine-learning techniques have received significant interest for accelerating …