【持续更新】Awesome Accelerated-MRI-Reconstruction-Papers

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Awesome Accelerated-MRI-Reconstruction-Papers

This artical is folked from jkkronk/Accelerated-MRI-Papers: List of Papers in MRI Reconstruction (github.com), and updating in real time.

NOT MAINTAINED

A awesome list of a few papers on MRI reconstruction.

If your paper is not on the list, please feel free to raise an issue or drop me an e-mail.

What is Accelerated MRI-Reconstruction?

MRI is acquiring data in the Fourier domain, called kspace, and to fully sampling the data in kspace is needed to get an accurate image without artefacts. This is a time-consuming task that results in a brain scan taking up to 30 minutes. Accelerated MRI-Reconstruction seeks to reduce the acquisition time to improve efficiency, reduce motion artefacts and improve patient comfort. Accelerated MRI can be done by either introducing new hardware, such as extra receiver coils (called parallel imaging), or apply algorithms for better reconstruction. An excellent detailed introduction can found in fastMRI dataset paper. Below is an example of a fully sampled and undersampled counterpart. MRI-Reconstruction can be compared with super-resolution as the main goal is to estimate unsampled frequencies.

Yutong Chen have written a great meta review paper on accelerated MRI that can be found here: https://arxiv.org/abs/2112.12744

Supervised Deep Learning Methods

Title	Short	Year	PDF	CODE
Density Compensated Unrolled Networks for Non-Cartesian MRI Reconstruction	PDNet	2021	PDF	CODE
Deep J-Sense: Accelerated MRI Reconstruction via Unrolled Alternating Optimization	pMRI reconstruction	2021	PDF	CODE
Ultra-Fast T2-Weighted MR Reconstruction Using Complementary T1-Weighted Information	Combined sequences	2021	PDF	CODE
Multi-Modal MRI Reconstruction with Spatial Alignment Network	Combined sequences/modalities	2021	PDF	CODE
Joint Frequency and Image Space Learning for Fourier Imaging	Do reconstruction in kspace and image space	2020	PDF
End-to-End Variational Networks for Accelerated MRI Reconstruction	E2E Varnet	2020	PDF	CODE
XPDNet for MRI Reconstruction: an Application to the fastMRI 2020 Brain Challenge	Supervised unrolled	2020	PDF
GrappaNet: Combining Parallel Imaging with Deep Learning for Multi-Coil MRI Reconstruction	Supervised kspace	2020	PDF	CODE
GrappaNet: Combining Parallel Imaging with Deep Learning for Multi-Coil MRI Reconstruction	Supervised kspace	2020	PDF	CODE
Neumann Networks for Linear Inverse Problems in Imaging	Supervised end-to-end reconstruction	2019	PDF	CODE
LORAKI: Autocalibrated Recurrent Neural Networks for Autoregressive MRI Reconstruction in k-Space	Supervised CNN Kspace	2019	PDF
Image reconstruction by domain transform manifold learning	Supervised Manifold learning	2019	PDF	CODE
KIKI-net: cross-domain convolutional neural networ ks forreconstructing undersampled magnetic resonan ce images	Supervised CNN	2019	PDF	CODE
Learning a Variational Network for Reconstruction of Accelerated MRI Data	Supervised Variational Network	2017	PDF	CODE
A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction	Supervised Cascade Network	2017	PDF	CODE
Deep ADMM-Net for Compressive Sensing MRI	Supervised and Compressed Sensing (CS)	2016	PDF	CODE

Unsupervised Deep Learning Methods

Title	Short	Year	PDF	CODE
ENSURE: A general approach for unsupervised training of deep image reconstruction algorithms	SURE/GSURE	2021	PDF
Unsupervised MRI Reconstruction with Generative Adversarial Networks	Unsupervised with GAN	2020	PDF	CODE
Unsupervised Deep Basis Pursuit: Learning inverse problems without ground-truth data	Supervised and Unsupervised end-to-end reconstruction	2019	PDF

Untrained Methods

Title	Short	Year	PDF	CODE
Unsupervised Deep Basis Pursuit: Learning inverse problems without ground-truth data	DIP	2020	PDF
Accelerated MRI with Un-trained Neural Networks	Untrained	2020	PDF

Low Rank Methods

Title	Short	Year	PDF	CODE
LORAKI: Autocalibrated Recurrent Neural Networks for Autoregressive MRI Reconstruction in k-Space	Learnig LORAKS	2019	PDF
A General Framework for Compressed Sensing and Parallel MRI Using Annihilating Filter Based Low-Rank Hankel Matrix	Parallel imaging and Compressed Sensing (CS)	2016	PDF
Autocalibrated loraks for fast constrained MRI reconstruction	LORAKS	2015	PDF

Prior Based Methods

Title	Short	Year	PDF	CODE
Bayesian Image Reconstruction using Deep Generative Models	Unsupervised in the sense not trained end-to-end reconstruction	2021	PDF
Joint reconstruction and bias field correction for undersampled MR imaging	VAE reconstruction with Joint biasfield and reconstruction	2020	PDF
MR Image Reconstruction Using Deep Density Priors	VAE	2019	PDF	CODE

Classical Methods for Parallel Imaging and Compress Sensing

Title	Short	Year	PDF	CODE
ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: Where SENSE meets GRAPPA	Parallel imaging	2014	PDF	CODE
Joint image reconstruction and sensitivity estimation in SENSE (JSENSE)	Parallel imaging	2007	PDF	CODE
Sparse MRI: The Application of Compressed Sensingfor Rapid MR Imaging	Compressed Sensing (CS)	2007	PDF	CODE
Undersampled Radial MRI with Multiple Coils. Iterative Image Reconstruction Using a Total Variation Constraint	Compressed Sensing (CS)	2007	PDF
Robust Uncertainty Principles: Exact Signal Reconstruction from Highly Incomplete Frequency Information	Compressed Sensing (CS)	2004	PDF	CODE
POCSENSE: POCS-based reconstruction for sensitivity encoded magnetic resonance imaging	Parallel imaging	2004	PDF	CODE
Generalized autocalibrating partially parallel acquisitions (GRAPPA)	Parallel imaging	2002	PDF	CODE
SENSE: sensitivity encoding for fast MRI	Parallel imaging	1999	PDF	CODE
Simultaneous Acquisition of Spatial Harmonics (SMASH): Fast Imaging with Radiofrequency Coil Arrays Encoded Magnetic Resonance Imaging	Parallel imaging	1997	PDF

Uncertainty Estimation

Title	Short	Year	PDF	CODE
Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction	Epistemic Uncertainty Estimation	2021	PDF
Uncertainty Quantification in Deep MRI Reconstruction	Uncertainty	2021	PDF
Sampling possible reconstructions of undersampled acquisitions in MR imaging	Uncertainty Estimation	2020	PDF

Robustness

Title	Short	Year	PDF	CODE
Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge	Robustness in FastMRI	2021	PDF
Measuring Robustness in Deep Learning Based Compressive Sensing	Robustness	2021	PDF
Improving Robustness of Deep-Learning-Based Image Reconstruction	Robustness	2020	PDF
On instabilities of deep learning in image reconstruction and the potential costs of AI	Robustness review	2019	PDF	CODE
Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: Database-free deep learning for fast imaging	Supervised CNN Kspace	2019	PDF	CODE

Other

Title	Short	Year	PDF	CODE
A review of deep learning methods for MRI reconstruction	Review paper	2021	PDF
fastMRI+: Clinical Pathology Annotations for Knee and Brain Fully Sampled Multi-Coil MRI Data	Lesion annotations for fastMRI	2021	PDF	CODE
Data augmentation for deep learning based accelerated MRI reconstruction with limited data	Data Augmentation for reconstruction	2021	PDF	CODE
Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction	Competition results	2021	PDF
Benchmarking MRI Reconstruction Neural Networks on Large Public Datasets	Benchmark	2020	PDF
Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction	Survey	2019	PDF
fastMRI: An Open Dataset and Benchmarks for Accelerated MRI	Machine learning baselines and public dataset	2019	PDF	CODE

Thanks

Thanks to jkkronk/Accelerated-MRI-Papers: List of Papers in MRI Reconstruction (github.com)