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
Generative Image Priors for MRI Reconstruction Trained from Magnitude-Only Images
mrirecon/spreco
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Purpose: In this work, we present a workflow to construct generic and robust generative image priors from magnitude-only images.
Learning Weakly Convex Regularizers for Convergent Image-Reconstruction Algorithms
axgoujon/convex_ridge_regularizers
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We propose to learn non-convex regularizers with a prescribed upper bound on their weak-convexity modulus.
SMRD: SURE-based Robust MRI Reconstruction with Diffusion Models
nvlabs/smrd
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However, diffusion models require careful tuning of inference hyperparameters on a validation set and are still sensitive to distribution shifts during testing.
Motion Compensated Dynamic MRI Reconstruction with Local Affine Optical Flow Estimation
This paper proposes a novel framework to reconstruct the dynamic magnetic resonance images (DMRI) with motion compensation (MC).
Compressed Sensing MRI Reconstruction using a Generative Adversarial Network with a Cyclic Loss
In this paper, we propose a novel deep learning-based generative adversarial model, RefineGAN, for fast and accurate CS-MRI reconstruction.
Coupled Dictionary Learning for Multi-contrast MRI Reconstruction
In this paper, we propose a Coupled Dictionary Learning based multi-contrast MRI reconstruction (CDLMRI) approach to leverage an available guidance contrast to restore the target contrast.
Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction
In addition, we introduce a semantic interpretability score, measuring the visibility of the region of interest in both ground truth and reconstructed images, which allows us to objectively quantify the usefulness of the image quality for image post-processing and analysis.
Scan‐specific robust artificial‐neural‐networks for k‐space interpolation (RAKI) reconstruction: Database‐free deep learning for fast imaging
geopi1/DeepMRI
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The noise performance and reconstruction quality of the RAKI method was compared with GRAPPA in phantom, as well as in neurological and cardiac in vivo datasets.
MRI Reconstruction via Cascaded Channel-wise Attention Network
We consider an MRI reconstruction problem with input of k-space data at a very low undersampled rate.
Reducing Uncertainty in Undersampled MRI Reconstruction with Active Acquisition
ezerilli/fast_MRI
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CVPR 2019
The goal of MRI reconstruction is to restore a high fidelity image from partially observed measurements.
