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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find MRI Reconstruction models and implementations
fastMRI
IXI
SKM-TEA
Latest papers
NPB-REC: A Non-parametric Bayesian Deep-learning Approach for Undersampled MRI Reconstruction with Uncertainty Estimation
samahkh/npb-rec
•
•
We introduce "NPB-REC", a non-parametric fully Bayesian framework, for MRI reconstruction from undersampled data with uncertainty estimation.
Graph Image Prior for Unsupervised Dynamic MRI Reconstruction
The inductive bias of the convolutional neural network (CNN) can act as a strong prior for image restoration, which is known as the Deep Image Prior (DIP).
Progressive Divide-and-Conquer via Subsampling Decomposition for Accelerated MRI
Starting from decomposing the original maximum-a-posteriori problem of accelerated MRI, we present a rigorous derivation of the proposed PDAC framework, which could be further unfolded into an end-to-end trainable network.
TC-DiffRecon: Texture coordination MRI reconstruction method based on diffusion model and modified MF-UNet method
We also suggest the incorporation of the MF-UNet module, designed to enhance the quality of MRI images generated by the model while mitigating the over-smoothing issue to a certain extent.
Inference Stage Denoising for Undersampled MRI Reconstruction
vios-s/Inference_Denoising_MRI_Recon
•
•
In this work, by employing a conditional hyperparameter network, we eliminate the need of augmentation, yet maintain robust performance under various levels of Gaussian noise.
NLCG-Net: A Model-Based Zero-Shot Learning Framework for Undersampled Quantitative MRI Reconstruction
Typical quantitative MRI (qMRI) methods estimate parameter maps after image reconstructing, which is prone to biases and error propagation.
Robust MRI Reconstruction by Smoothed Unrolling (SMUG)
To address this problem, we propose a novel image reconstruction framework, termed Smoothed Unrolling (SMUG), which advances a deep unrolling-based MRI reconstruction model using a randomized smoothing (RS)-based robust learning approach.
DiffCMR: Fast Cardiac MRI Reconstruction with Diffusion Probabilistic Models
xmed-lab/diffcmr
•
•
Performing magnetic resonance imaging (MRI) reconstruction from under-sampled k-space data can accelerate the procedure to acquire MRI scans and reduce patients' discomfort.
SubZero: Subspace Zero-Shot MRI Reconstruction
heng14/subzero
•
•
Recently introduced zero-shot self-supervised learning (ZS-SSL) has shown potential in accelerated MRI in a scan-specific scenario, which enabled high-quality reconstructions without access to a large training dataset.
Volumetric Reconstruction Resolves Off-Resonance Artifacts in Static and Dynamic PROPELLER MRI
sarafridov/volumetric-propeller
•
•
Off-resonance artifacts in magnetic resonance imaging (MRI) are visual distortions that occur when the actual resonant frequencies of spins within the imaging volume differ from the expected frequencies used to encode spatial information.
