Search Results for author:
Found 14 papers, 7 papers with code
Stable Deep MRI Reconstruction using Generative Priors
After training, the regularizer encodes higher-level domain statistics which we demonstrate by synthesizing images without data.
Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging
We consider inverse problems with both linear and non-linear forward models for computational MRI, and review the classical approaches for solving these.
Alternating Learning Approach for Variational Networks and Undersampling Pattern in Parallel MRI Applications
The quality of the VNs and SPs was compared against other approaches, including joint learning methods and VN learning with fixed variable density Poisson-disc SPs, using two different datasets and different acceleration factors (AF).
fastMRI+: Clinical Pathology Annotations for Knee and Brain Fully Sampled Multi-Coil MRI Data
Improving speed and image quality of Magnetic Resonance Imaging (MRI) via novel reconstruction approaches remains one of the highest impact applications for deep learning in medical imaging.
Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction
To this end, we solve the linear inverse problem of undersampled MRI reconstruction in a variational setting.
Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction
Accelerating MRI scans is one of the principal outstanding problems in the MRI research community.
CG-SENSE revisited: Results from the first ISMRM reproducibility challenge
The reference implementations were in good agreement, both visually and in terms of image similarity metrics.
End-to-End Variational Networks for Accelerated MRI Reconstruction
The slow acquisition speed of magnetic resonance imaging (MRI) has led to the development of two complementary methods: acquiring multiple views of the anatomy simultaneously (parallel imaging) and acquiring fewer samples than necessary for traditional signal processing methods (compressed sensing).
Ranked #1 on
MRI Reconstruction
on fastMRI Knee 4x
Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge
Conclusion: The challenge led to new developments in machine learning for image reconstruction, provided insight into the current state of the art in the field, and highlighted remaining hurdles for clinical adoption.
Training a Neural Network for Gibbs and Noise Removal in Diffusion MRI
Both machine learning methods were able to mitigate artifacts in diffusion-weighted images and diffusion parameter maps.
Image and Video Processing
Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction
Both linear and non-linear methods are covered, followed by a discussion of recent efforts to further improve parallel imaging using machine learning, and specifically using artificial neural networks.
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
Accelerating Magnetic Resonance Imaging (MRI) by taking fewer measurements has the potential to reduce medical costs, minimize stress to patients and make MRI possible in applications where it is currently prohibitively slow or expensive.
Multicompartment Magnetic Resonance Fingerprinting
Magnetic resonance fingerprinting (MRF) is a technique for quantitative estimation of spin-relaxation parameters from magnetic-resonance data.
Medical Physics Numerical Analysis Numerical Analysis Optimization and Control
Learning a Variational Network for Reconstruction of Accelerated MRI Data
Due to its high computational performance, i. e., reconstruction time of 193 ms on a single graphics card, and the omission of parameter tuning once the network is trained, this new approach to image reconstruction can easily be integrated into clinical workflow.
