Super-resolution of magnetic resonance images using Generative Adversarial Networks
Abstract Magnetic Resonance Imaging (MRI) typically comes at the cost of small spatial
coverage, high expenses and long scan times. Accelerating MRI acquisition by taking less …
coverage, high expenses and long scan times. Accelerating MRI acquisition by taking less …
[HTML][HTML] A survey of emerging applications of diffusion probabilistic models in mri
Diffusion probabilistic models (DPMs) which employ explicit likelihood characterization and
a gradual sampling process to synthesize data, have gained increasing research interest …
a gradual sampling process to synthesize data, have gained increasing research interest …
Attention 3D U-Net with Multiple Skip Connections for Segmentation of Brain Tumor Images
Among researchers using traditional and new machine learning and deep learning
techniques, 2D medical image segmentation models are popular. Additionally, 3D …
techniques, 2D medical image segmentation models are popular. Additionally, 3D …
High-resolution MRI synthesis using a data-driven framework with denoising diffusion probabilistic modeling
Objective. High-resolution magnetic resonance imaging (MRI) can enhance lesion
diagnosis, prognosis, and delineation. However, gradient power and hardware limitations …
diagnosis, prognosis, and delineation. However, gradient power and hardware limitations …
An arbitrary scale super-resolution approach for 3d mr images via implicit neural representation
High Resolution (HR) medical images provide rich anatomical structure details to facilitate
early and accurate diagnosis. In magnetic resonance imaging (MRI), restricted by hardware …
early and accurate diagnosis. In magnetic resonance imaging (MRI), restricted by hardware …
Deep‐learning based super‐resolution for 3D isotropic coronary MR angiography in less than a minute
Purpose To develop and evaluate a novel and generalizable super‐resolution (SR) deep‐
learning framework for motion‐compensated isotropic 3D coronary MR angiography …
learning framework for motion‐compensated isotropic 3D coronary MR angiography …
RIRGAN: An end-to-end lightweight multi-task learning method for brain MRI super-resolution and denoising
M Yu, M Guo, S Zhang, Y Zhan, M Zhao… - Computers in Biology …, 2023 - Elsevier
A common problem in the field of deep-learning-based low-level vision medical images is
that most of the research is based on single task learning (STL), which is dedicated to …
that most of the research is based on single task learning (STL), which is dedicated to …
IREM: High-resolution magnetic resonance image reconstruction via implicit neural representation
For collecting high-quality high-resolution (HR) MR image, we propose a novel image
reconstruction network named IREM, which is trained on multiple low-resolution (LR) MR …
reconstruction network named IREM, which is trained on multiple low-resolution (LR) MR …
Exploring separable attention for multi-contrast MR image super-resolution
Super-resolving the magnetic resonance (MR) image of a target contrast under the guidance
of the corresponding auxiliary contrast, which provides additional anatomical information, is …
of the corresponding auxiliary contrast, which provides additional anatomical information, is …
A systematic review and identification of the challenges of deep learning techniques for undersampled magnetic resonance image reconstruction
Deep learning (DL) in magnetic resonance imaging (MRI) shows excellent performance in
image reconstruction from undersampled k-space data. Artifact-free and high-quality MRI …
image reconstruction from undersampled k-space data. Artifact-free and high-quality MRI …