Applicability of deep learning-based reconstruction trained by brain and knee 3T MRI to lumbar 1.5 T MRI
N Kashiwagi, H Tanaka, Y Yamashita… - Acta Radiologica …, 2021 - journals.sagepub.com
Background Several deep learning-based methods have been proposed for addressing the
long scanning time of magnetic resonance imaging. Most are trained using brain 3T …
long scanning time of magnetic resonance imaging. Most are trained using brain 3T …
Performance characterization of a novel deep learning-based MR image reconstruction pipeline
RM Lebel - arXiv preprint arXiv:2008.06559, 2020 - arxiv.org
A novel deep learning-based magnetic resonance imaging reconstruction pipeline was
designed to address fundamental image quality limitations of conventional reconstruction to …
designed to address fundamental image quality limitations of conventional reconstruction to …
IR-FRestormer: Iterative refinement with fourier-based restormer for accelerated MRI reconstruction
Accelerated magnetic resonance imaging (MRI) aims to reconstruct high-quality MR images
from a set of under-sampled measurements. State-of-the-art methods for this task use deep …
from a set of under-sampled measurements. State-of-the-art methods for this task use deep …
A deep framework assembling principled modules for CS-MRI: unrolling perspective, convergence behaviors, and practical modeling
Compressed Sensing Magnetic Resonance Imaging (CS-MRI) significantly accelerates MR
acquisition at a sampling rate much lower than the Nyquist criterion. A major challenge for …
acquisition at a sampling rate much lower than the Nyquist criterion. A major challenge for …
VS-Net: Variable splitting network for accelerated parallel MRI reconstruction
In this work, we propose a deep learning approach for parallel magnetic resonance imaging
(MRI) reconstruction, termed a variable splitting network (VS-Net), for an efficient, high …
(MRI) reconstruction, termed a variable splitting network (VS-Net), for an efficient, high …
Hyperrecon: Regularization-agnostic cs-mri reconstruction with hypernetworks
Reconstructing under-sampled k-space measurements in Compressed Sensing MRI (CS-
MRI) is classically solved by minimizing a regularized least-squares cost function. In the …
MRI) is classically solved by minimizing a regularized least-squares cost function. In the …
GrappaNet: Combining parallel imaging with deep learning for multi-coil MRI reconstruction
Abstract Magnetic Resonance Image (MRI) acquisition is an inherently slow process which
has spurred the development of two different acceleration methods: acquiring multiple …
has spurred the development of two different acceleration methods: acquiring multiple …
Pyramid convolutional RNN for MRI image reconstruction
Fast and accurate MRI image reconstruction from undersampled data is crucial in clinical
practice. Deep learning based reconstruction methods have shown promising advances in …
practice. Deep learning based reconstruction methods have shown promising advances in …
Undersampled MR image reconstruction using an enhanced recursive residual network
When using aggressive undersampling, it is difficult to recover the high quality image with
reliably fine features. In this paper, we propose an enhanced recursive residual network …
reliably fine features. In this paper, we propose an enhanced recursive residual network …
Self-Supervised Deep Unrolled Reconstruction Using Regularization by Denoising
Deep learning methods have been successfully used in various computer vision tasks.
Inspired by that success, deep learning has been explored in magnetic resonance imaging …
Inspired by that success, deep learning has been explored in magnetic resonance imaging …