Scan‐specific artifact reduction in k‐space (SPARK) neural networks synergize with physics‐based reconstruction to accelerate MRI
Purpose To develop a scan‐specific model that estimates and corrects k‐space errors made
when reconstructing accelerated MRI data. Methods Scan‐specific artifact reduction in k …
when reconstructing accelerated MRI data. Methods Scan‐specific artifact reduction in k …
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 …
LORAKI: Autocalibrated recurrent neural networks for autoregressive MRI reconstruction in k-space
We propose and evaluate a new MRI reconstruction method named LORAKI that trains an
autocalibrated scan-specific recurrent neural network (RNN) to recover missing k-space …
autocalibrated scan-specific recurrent neural network (RNN) to recover missing k-space …
Scan-specific residual convolutional neural networks for fast MRI using residual RAKI
C Zhang, SAH Hosseini, S Moeller… - 2019 53rd Asilomar …, 2019 - ieeexplore.ieee.org
Parallel imaging is a widely-used acceleration technique for magnetic resonance imaging
(MRI). Conventional linear reconstruction approaches in parallel imaging suffer from noise …
(MRI). Conventional linear reconstruction approaches in parallel imaging suffer from noise …
Residual RAKI: A hybrid linear and non-linear approach for scan-specific k-space deep learning
Parallel imaging is the most clinically used acceleration technique for magnetic resonance
imaging (MRI) in part due to its easy inclusion into routine acquisitions. In k-space based …
imaging (MRI) in part due to its easy inclusion into routine acquisitions. In k-space based …
Improving accelerated MRI by deep learning with sparsified complex data
Z Jin, QS Xiang - Magnetic Resonance in Medicine, 2023 - Wiley Online Library
Purpose To obtain high‐quality accelerated MR images with complex‐valued reconstruction
from undersampled k‐space data. Methods The MRI scans from human subjects were …
from undersampled k‐space data. Methods The MRI scans from human subjects were …
Adaptive convolutional neural networks for accelerating magnetic resonance imaging via k-space data interpolation
Deep learning in k-space has demonstrated great potential for image reconstruction from
undersampled k-space data in fast magnetic resonance imaging (MRI). However, existing …
undersampled k-space data in fast magnetic resonance imaging (MRI). However, existing …
A k‐space‐to‐image reconstruction network for MRI using recurrent neural network
Purpose Reconstructing the images from undersampled k‐space data are an ill‐posed
inverse problem. As a solution to this problem, we propose a method to reconstruct magnetic …
inverse problem. As a solution to this problem, we propose a method to reconstruct magnetic …
GRAPPA-GANs for parallel MRI reconstruction
k-space undersampling is a standard technique to accelerate MR image acquisitions.
Reconstruction techniques including GeneRalized Autocalibrating Partial Parallel …
Reconstruction techniques including GeneRalized Autocalibrating Partial Parallel …
Deep learning with domain adaptation for accelerated projection‐reconstruction MR
Purpose The radial k‐space trajectory is a well‐established sampling trajectory used in
conjunction with magnetic resonance imaging. However, the radial k‐space trajectory …
conjunction with magnetic resonance imaging. However, the radial k‐space trajectory …