MRI Reconstruction
151 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
Latest papers with no code
On Retrospective k-space Subsampling schemes For Deep MRI Reconstruction
This work investigates the impact of the $k$-space subsampling scheme on the quality of reconstructed accelerated MRI measurements produced by trained DL models.
Holistic Multi-Slice Framework for Dynamic Simultaneous Multi-Slice MRI Reconstruction
Deep learning (DL)-based methods have shown promising results for single-slice MR reconstruction, but the addition of SMS acceleration raises unique challenges due to the composite k-space signals and the resulting images with strong inter-slice artifacts.
Spatiotemporal implicit neural representation for unsupervised dynamic MRI reconstruction
The high-quality and inner continuity of the images provided by INR has great potential to further improve the spatiotemporal resolution of dynamic MRI, without the need of any training data.
SPIRiT-Diffusion: SPIRiT-driven Score-Based Generative Modeling for Vessel Wall imaging
Diffusion model is the most advanced method in image generation and has been successfully applied to MRI reconstruction.
CloudBrain-ReconAI: An Online Platform for MRI Reconstruction and Image Quality Evaluation
Efficient collaboration between engineers and radiologists is important for image reconstruction algorithm development and image quality evaluation in magnetic resonance imaging (MRI).
Deep unfolding as iterative regularization for imaging inverse problems
Recently, deep unfolding methods that guide the design of deep neural networks (DNNs) through iterative algorithms have received increasing attention in the field of inverse problems.
On the Robustness of deep learning-based MRI Reconstruction to image transformations
We find a new instability source of MRI image reconstruction, i. e., the lack of reconstruction robustness against spatial transformations of an input, e. g., rotation and cutout.
Compressed Sensing MRI Reconstruction Regularized by VAEs with Structured Image Covariance
The covariance can model changing uncertainty dependencies caused by structure in the image, such as edges or objects, and provides a new distance metric from the manifold of learned images.
Stable Deep MRI Reconstruction using Generative Priors
We propose a novel deep neural network based regularizer which is trained in a generative setting on reference magnitude images only.
A Faithful Deep Sensitivity Estimation for Accelerated Magnetic Resonance Imaging
To understand the behavior of the network, the mutual promotion of sensitivity estimation and image reconstruction is revealed through the visualization of network intermediate results.
