#! https://zhuanlan.zhihu.com/p/643451854
This artical is folked from jkkronk/Accelerated-MRI-Papers: List of Papers in MRI Reconstruction (github.com) , and updating in real time.
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 .
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
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
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
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
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
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
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
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
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
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 to jkkronk/Accelerated-MRI-Papers: List of Papers in MRI Reconstruction (github.com)
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