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 …
Systematic evaluation of iterative deep neural networks for fast parallel MRI reconstruction with sensitivity‐weighted coil combination
Purpose To systematically investigate the influence of various data consistency layers and
regularization networks with respect to variations in the training and test data domain, for …
regularization networks with respect to variations in the training and test data domain, for …
Medical informed machine learning: A scoping review and future research directions
Combining domain knowledge (DK) and machine learning is a recent research stream to
overcome multiple issues like limited explainability, lack of data, and insufficient robustness …
overcome multiple issues like limited explainability, lack of data, and insufficient robustness …
Exploring the acceleration limits of deep learning variational network–based two-dimensional brain MRI
A Radmanesh, MJ Muckley, T Murrell… - Radiology: Artificial …, 2022 - pubs.rsna.org
Purpose To explore the limits of deep learning–based brain MRI reconstruction and identify
useful acceleration ranges for general-purpose imaging and potential screening. Materials …
useful acceleration ranges for general-purpose imaging and potential screening. Materials …
Image quality assessment for magnetic resonance imaging
Image quality assessment (IQA) algorithms aim to reproduce the human's perception of the
image quality. The growing popularity of image enhancement, generation, and recovery …
image quality. The growing popularity of image enhancement, generation, and recovery …
Low‐dose CT denoising via convolutional neural network with an observer loss function
Purpose: Convolutional neural network (CNN)‐based denoising is an effective method for
reducing complex computed tomography (CT) noise. However, the image blur induced by …
reducing complex computed tomography (CT) noise. However, the image blur induced by …
Jointly Learning Non-Cartesian k-Space Trajectories and Reconstruction Networks for 2D and 3D MR Imaging through Projection
CG Radhakrishna, P Ciuciu - Bioengineering, 2023 - mdpi.com
Compressed sensing in magnetic resonance imaging essentially involves the optimization
of (1) the sampling pattern in k-space under MR hardware constraints and (2) image …
of (1) the sampling pattern in k-space under MR hardware constraints and (2) image …
[HTML][HTML] ⊥-loss: A symmetric loss function for magnetic resonance imaging reconstruction and image registration with deep learning
Convolutional neural networks (CNNs) are increasingly adopted in medical imaging, eg, to
reconstruct high-quality images from undersampled magnetic resonance imaging (MRI) …
reconstruct high-quality images from undersampled magnetic resonance imaging (MRI) …
Reconstruction of shoulder MRI using deep learning and compressed sensing: a validation study on healthy volunteers
T Dratsch, F Siedek, C Zäske, K Sonnabend… - European Radiology …, 2023 - Springer
Background To investigate the potential of combining compressed sensing (CS) and deep
learning (DL) for accelerated two-dimensional (2D) and three-dimensional (3D) magnetic …
learning (DL) for accelerated two-dimensional (2D) and three-dimensional (3D) magnetic …
Truncated residual based plug-and-play ADMM algorithm for MRI reconstruction
Plug-and-play alternating direction method of multiplier (PnP-ADMM) can be used to solve
the magnetic resonance imaging (MRI) reconstruction problem, which allows plugging the …
the magnetic resonance imaging (MRI) reconstruction problem, which allows plugging the …