[HTML][HTML] Deep learning approach for generating MRA images from 3D quantitative synthetic MRI without additional scans
Objectives Quantitative synthetic magnetic resonance imaging (MRI) enables synthesis of
various contrast-weighted images as well as simultaneous quantification of T1 and T2 …
various contrast-weighted images as well as simultaneous quantification of T1 and T2 …
[HTML][HTML] DC2Anet: Generating Lumbar Spine MR Images from CT Scan Data Based on Semi-Supervised Learning
Magnetic resonance imaging (MRI) plays a significant role in the diagnosis of lumbar disc
disease. However, the use of MRI is limited because of its high cost and significant operating …
disease. However, the use of MRI is limited because of its high cost and significant operating …
Multiple slice k-space deep learning for magnetic resonance imaging reconstruction
Magnetic resonance imaging (MRI) has been one of the most powerful and valuable
imaging methods for medical diagnosis and staging of disease. Due to the long scan time of …
imaging methods for medical diagnosis and staging of disease. Due to the long scan time of …
[HTML][HTML] Swin transformer for fast MRI
Magnetic resonance imaging (MRI) is an important non-invasive clinical tool that can
produce high-resolution and reproducible images. However, a long scanning time is …
produce high-resolution and reproducible images. However, a long scanning time is …
Dual-domain self-supervised learning for accelerated non-Cartesian MRI reconstruction
While enabling accelerated acquisition and improved reconstruction accuracy, current deep
MRI reconstruction networks are typically supervised, require fully sampled data, and are …
MRI reconstruction networks are typically supervised, require fully sampled data, and are …
Results of the 2020 fastMRI challenge for machine learning MR image reconstruction
MJ Muckley, B Riemenschneider… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
Accelerating MRI scans is one of the principal outstanding problems in the MRI research
community. Towards this goal, we hosted the second fastMRI competition targeted towards …
community. Towards this goal, we hosted the second fastMRI competition targeted towards …
Deep learning for musculoskeletal image analysis
The diagnosis, prognosis, and treatment of patients with musculoskeletal (MSK) disorders
require radiology imaging (using computed tomography, magnetic resonance imaging …
require radiology imaging (using computed tomography, magnetic resonance imaging …
A deep cascade of convolutional neural networks for dynamic MR image reconstruction
Inspired by recent advances in deep learning, we propose a framework for reconstructing
dynamic sequences of 2-D cardiac magnetic resonance (MR) images from undersampled …
dynamic sequences of 2-D cardiac magnetic resonance (MR) images from undersampled …
Three‐dimensional dictionary‐learning reconstruction of 23Na MRI data
NGR Behl, C Gnahm, P Bachert… - Magnetic …, 2016 - Wiley Online Library
Purpose To reduce noise and artifacts in 23Na MRI with a Compressed Sensing
reconstruction and a learned dictionary as sparsifying transform. Methods A three …
reconstruction and a learned dictionary as sparsifying transform. Methods A three …
Deep learning based MRI reconstruction with transformer
Z Wu, W Liao, C Yan, M Zhao, G Liu, N Ma… - Computer Methods and …, 2023 - Elsevier
Magnetic resonance imaging (MRI) has become one of the most powerful imaging
techniques in medical diagnosis, yet the prolonged scanning time becomes a bottleneck for …
techniques in medical diagnosis, yet the prolonged scanning time becomes a bottleneck for …