MRI advancements in musculoskeletal clinical and research practice
Over the past decades, MRI has become increasingly important for diagnosing and
longitudinally monitoring musculoskeletal disorders, with ongoing hardware and software …
longitudinally monitoring musculoskeletal disorders, with ongoing hardware and software …
Emerging trends in fast MRI using deep-learning reconstruction on undersampled k-space data: a systematic review
Magnetic Resonance Imaging (MRI) is an essential medical imaging modality that provides
excellent soft-tissue contrast and high-resolution images of the human body, allowing us to …
excellent soft-tissue contrast and high-resolution images of the human body, allowing us to …
MedShapeNet--A large-scale dataset of 3D medical shapes for computer vision
Prior to the deep learning era, shape was commonly used to describe the objects.
Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly …
Nowadays, state-of-the-art (SOTA) algorithms in medical imaging are predominantly …
Practical uncertainty quantification for space-dependent inverse heat conduction problem via ensemble physics-informed neural networks
X Jiang, X Wang, Z Wen, E Li, H Wang - International Communications in …, 2023 - Elsevier
Inverse heat conduction problems (IHCPs) are problems of estimating unknown quantities of
interest (QoIs) of the heat conduction with given temperature observations. The challenge of …
interest (QoIs) of the heat conduction with given temperature observations. The challenge of …
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 …
The state-of-the-art in cardiac mri reconstruction: Results of the cmrxrecon challenge in miccai 2023
Cardiac MRI, crucial for evaluating heart structure and function, faces limitations like slow
imaging and motion artifacts. Undersampling reconstruction, especially data-driven …
imaging and motion artifacts. Undersampling reconstruction, especially data-driven …
Gan-tl: Generative adversarial networks with transfer learning for mri reconstruction
Generative adversarial networks (GAN), which are fueled by deep learning, are an efficient
technique for image reconstruction using under-sampled MR data. In most cases, the …
technique for image reconstruction using under-sampled MR data. In most cases, the …
On retrospective k-space subsampling schemes for deep MRI reconstruction
Acquiring fully-sampled MRI k-space data is time-consuming, and collecting accelerated
data can reduce the acquisition time. Employing 2D Cartesian-rectilinear subsampling …
data can reduce the acquisition time. Employing 2D Cartesian-rectilinear subsampling …
Multi-coil mri reconstruction challenge—assessing brain mri reconstruction models and their generalizability to varying coil configurations
Deep-learning-based brain magnetic resonance imaging (MRI) reconstruction methods
have the potential to accelerate the MRI acquisition process. Nevertheless, the scientific …
have the potential to accelerate the MRI acquisition process. Nevertheless, the scientific …
Camp-net: consistency-aware multi-prior network for accelerated MRI reconstruction
Undersampling-space data in magnetic resonance imaging (MRI) reduces scan time but
pose challenges in image reconstruction. Considerable progress has been made in …
pose challenges in image reconstruction. Considerable progress has been made in …