MRI Reconstruction
147 papers with code • 6 benchmarks • 3 datasets
In its most basic form, MRI reconstruction consists in retrieving a complex-valued image from its under-sampled Fourier coefficients. Besides, it can be addressed as a encoder-decoder task, in which the normative model in the latent space will only capture the relevant information without noise or corruptions. Then, we decode the latent space in order to have a reconstructed MRI.
Benchmarks
These leaderboards are used to track progress in MRI Reconstruction
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Libraries
Use these libraries to find MRI Reconstruction models and implementations
fastMRI
IXI
SKM-TEA
Most implemented papers
Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstruction
wdika/mridc
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Machine Learning methods can learn how to reconstruct Magnetic Resonance Images and thereby accelerate acquisition, which is of paramount importance to the clinical workflow.
Homotopic Gradients of Generative Density Priors for MR Image Reconstruction
yqx7150/HGGDP
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Deep learning, particularly the generative model, has demonstrated tremendous potential to significantly speed up image reconstruction with reduced measurements recently.
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).
Accelerated MRI with Un-trained Neural Networks
Convolutional Neural Networks (CNNs) are highly effective for image reconstruction problems.
XPDNet for MRI Reconstruction: an application to the 2020 fastMRI challenge
zaccharieramzi/fastmri-reproducible-benchmark
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We present a new neural network, the XPDNet, for MRI reconstruction from periodically under-sampled multi-coil data.
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.
Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstruction
directgroup/direct
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CVPR 2022
Magnetic Resonance Imaging can produce detailed images of the anatomy and physiology of the human body that can assist doctors in diagnosing and treating pathologies such as tumours.
Deep Generative Adversarial Networks for Compressed Sensing Automates MRI
gongenhao/GANCS
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A multilayer convolutional neural network is then jointly trained based on diagnostic quality images to discriminate the projection quality.
Self-Supervised Learning of Physics-Guided Reconstruction Neural Networks without Fully-Sampled Reference Data
byaman14/SSDU
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Results: Results on five different knee sequences at acceleration rate of 4 shows that proposed self-supervised approach performs closely with supervised learning, while significantly outperforming conventional compressed sensing and parallel imaging, as characterized by quantitative metrics and a clinical reader study.
Learning Multiscale Convolutional Dictionaries for Image Reconstruction
To close the performance gap, we thus propose a multiscale convolutional dictionary structure.
