Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge
Purpose To advance research in the field of machine learning for MR image reconstruction
with an open challenge. Methods We provided participants with a dataset of raw k‐space …
with an open challenge. Methods We provided participants with a dataset of raw k‐space …
How machine learning is powering neuroimaging to improve brain health
This report presents an overview of how machine learning is rapidly advancing clinical
translational imaging in ways that will aid in the early detection, prediction, and treatment of …
translational imaging in ways that will aid in the early detection, prediction, and treatment of …
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 …
development of two complementary methods: acquiring multiple views of the anatomy …
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 …
Humus-net: Hybrid unrolled multi-scale network architecture for accelerated mri reconstruction
In accelerated MRI reconstruction, the anatomy of a patient is recovered from a set of
undersampled and noisy measurements. Deep learning approaches have been proven to …
undersampled and noisy measurements. Deep learning approaches have been proven to …
ReconFormer: Accelerated MRI reconstruction using recurrent transformer
The accelerating magnetic resonance imaging (MRI) reconstruction process is a challenging
ill-posed inverse problem due to the excessive under-sampling operation in-space. In this …
ill-posed inverse problem due to the excessive under-sampling operation in-space. In this …
An adaptive intelligence algorithm for undersampled knee MRI reconstruction
N Pezzotti, S Yousefi, MS Elmahdy… - IEEE …, 2020 - ieeexplore.ieee.org
Adaptive intelligence aims at empowering machine learning techniques with the additional
use of domain knowledge. In this work, we present the application of adaptive intelligence to …
use of domain knowledge. In this work, we present the application of adaptive intelligence to …
Dual‐domain reconstruction network with V‐Net and K‐Net for fast MRI
Purpose To introduce a dual‐domain reconstruction network with V‐Net and K‐Net for
accurate MR image reconstruction from undersampled k‐space data. Methods Most state‐of …
accurate MR image reconstruction from undersampled k‐space data. Methods Most state‐of …
Bayesian uncertainty estimation of learned variational MRI reconstruction
Recent deep learning approaches focus on improving quantitative scores of dedicated
benchmarks, and therefore only reduce the observation-related (aleatoric) uncertainty …
benchmarks, and therefore only reduce the observation-related (aleatoric) uncertainty …
[PDF][PDF] State-of-the-art machine learning MRI reconstruction in 2020: Results of the second fastMRI challenge
MJ Muckley, B Riemenschneider… - arXiv preprint arXiv …, 2020 - hal.science
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 …