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Awesome-MRI-Reconstruction

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Papers

Review

  • Deep learning for fast MR imaging: a review for learning reconstruction from incomplete k-space data [paper]
  • Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks [paper]
  • AI-Based Reconstruction for Fast MRI—A Systematic Review and Meta-Analysis [paper]
  • Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction [paper]

Tutorials

  • MRI acquisition & image reconstruction tutorial [code]

arXiv papers

  • Measurement-conditioned Denoising Diffusion Probabilistic Model for Under-sampled Medical Image Reconstruction [paper]
  • Dual-Domain Reconstruction Networks with V-Net and K-Net for fast MRI [paper]
  • Fast MRI Reconstruction: How Powerful Transformers Are? [paper]
  • Swin Transformer for Fast MRI [paper] [code]
  • Federated Learning of Generative Image Priors for MRI Reconstruction [paper]
  • Contrastive Learning for Local and Global Learning MRI Reconstruction [paper]
  • Equilibrated Zeroth-Order Unrolled Deep Networks for Accelerated MRI [paper]
  • Specificity-Preserving Federated Learning for MR Image Reconstruction [paper]
  • Learned Half-Quadratic Splitting Network for Magnetic Resonance Image Reconstruction [paper]
  • MRI Reconstruction Using Deep Energy-Based Model [paper] [Submited to Magnetic Resonance in Medicine]
  • High Fidelity Deep Learning-based MRI Reconstruction with Instance-wise Discriminative Feature Matching Loss [paper] [code] [Submited to Magnetic Resonance in Medicine]
  • Multi-Modal MRI Reconstruction Assisted with Spatial Alignment Network [paper] [code]
  • Deep MRI Reconstruction with Radial Subsampling [paper] [code]
  • Multi-Modal MRI Reconstruction with Spatial Alignment Network [paper] [code]
  • Accelerated Multi-Modal MR Imaging with Transformers [paper] [code]
  • Accelerated MRI Reconstruction with Separable and Enhanced Low-Rank Hankel Regularization [paper]
  • Joint Calibrationless Reconstruction and Segmentation of Parallel MRI [paper]
  • Adaptive Gradient Balancing for Undersampled MRI Reconstruction and Image-to-Image Translation [paper]
  • Zero-Shot Self-Supervised Learning for MRI Reconstruction [paper]
  • Regularization-Agnostic Compressed Sensing MRI Reconstruction with Hypernetworks [paper] [code]
  • Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers [paper]

2022

  • Joint Reconstruction of Vascular Structure and Function Maps in Dynamic Contrast Enhanced MRI Using Vascular Heterogeneity Priors (TMI) [paper]
  • Spatiotemporal Flexible Sparse Reconstruction for Rapid Dynamic Contrast-Enhanced MRI (TBE) []paper]
  • Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas (MedIA)[paper]
  • IDPCNN: Iterative denoising and projecting CNN for MRI reconstruction (J. CAM) [paper]
  • Learning Optimal K-space Acquisition and Reconstruction using Physics-Informed Neural Networks (CVPR) [paper]
  • Transformer-empowered Multi-scale Contextual Matching and Aggregation for Multi-contrast MRI Super-resolution (CVPR) [paper] [code]
  • Vox2Cortex: Fast Explicit Reconstruction of Cortical Surfaces from 3D MRI Scans with Geometric Deep Neural Networks (CVPR) [paper]
  • 2D probabilistic undersampling pattern optimization for MR image reconstruction (MedIA) [paper]
  • Sampling possible reconstructions of undersampled acquisitions in MR imaging with a deep learned prior (TMI) [paper]
  • Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers (TMI) [paper]
  • Pyramid Convolutional RNN for MRI Image Reconstruction (TMI) [paper]
  • Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction (TMI) [paper]
  • Low-Rank and Framelet Based Sparsity Decomposition for Interventional MRI Reconstructionn (TBE) [paper]
  • A Plug-and-Play Approach To Multiparametric Quantitative MRI: Image Reconstruction Using Pre-Trained Deep Denoisers (ISBI) [paper]
  • LONDN-MRI: Adaptive Local Neighborhood-Based Networks for MR Image Reconstruction from Undersampled Data (ISBI) [paper]
  • Leaders: Learnable Deep Radial Subsampling for Mri Reconstruction (ISBI) [paper]
  • MPTGAN: A Multimodal Prior-Based Triple-Branch Network for Fast Prostate Mri Reconstruction (ISBI) [paper]
  • Dynamic Cardiac MRI Reconstruction Using Combined Tensor Nuclear Norm and Casorati Matrix Nuclear Norm Regularizations (ISBI) [paper]
  • Distributed Memory-Efficient Physics-Guided Deep Learning Reconstruction for Large-Scale 3d Non-Cartesian MRI (ISBI) [paper]
  • Joint Alignment and Reconstruction of Multislice Dynamic MRI Using Variational Manifold Learning (ISBI) [paper]
  • Compressed Sensing MRI Reconstruction with Co-VeGAN: Complex-Valued Generative Adversarial Network (WACV) [paper]

2021

  • Robust Compressed Sensing MRI with Deep Generative Priors (NeurIPS) [paper] [code]
  • Fine-grained MRI Reconstruction using Attentive Selection Generative Adversarial Networks (ICASSP) [paper]
  • Brain MRI super-resolution using coupled-projection residual network (Neurocomputing) [paper]
  • Variational multi-task MRI reconstruction: Joint reconstruction, registration and super-resolution (MedIA) [paper]
  • Uncertainty Quantification in Deep MRI Reconstruction (TMI)[paper]
  • Brain Surface Reconstruction from MRI Images based on Segmentation Networks Applying Signed Distance Maps (ISBI) [paper]
  • Density Compensated Unrolled Networks For Non-Cartesian MRI Reconstruction (ISBI) [paper]
  • Calibrationless MRI Reconstruction With A Plug-In Denoiser (ISBI) [paper]
  • Joint Deep Model-Based MR Image and Coil Sensitivity Reconstruction Network (Joint-ICNet) for Fast MRI (CVPR) [paper]
  • Multi-Contrast MRI Super-Resolution via a Multi-Stage Integration Network (MICCAI) [paper] [code]
  • Two-Stage Self-Supervised Cycle-Consistency Network for Reconstruction of Thin-Slice MR Images (MICCAI) [paper]
  • Task Transformer Network for Joint MRI Reconstruction and Super-Resolution (MICCAI) [paper] [code]
  • Over-and-Under Complete Convolutional RNN for MRI Reconstruction (MICCAI) [paper]
  • Universal Undersampled MRI Reconstruction (MICCAI) [paper]
  • Deep J-Sense: Accelerated MRI Reconstruction via Unrolled Alternating Optimization (MICCAI) [paper] [code]
  • Self-supervised Learning for MRI Reconstruction with a Parallel Network Training Framework (MICCAI) [paper] [code]
  • Memory-Efficient Learning for High-Dimensional MRI Reconstruction (MICCAI) [paper] [code]
  • IREM: High-Resolution Magnetic Resonance Image Reconstruction via Implicit Neural Representation (MICCAI) [paper]
  • Fast Magnetic Resonance Imaging on Regions of Interest: From Sensing to Reconstruction (MICCAI) [paper]
  • Temporal Feature Fusion with Sampling Pattern Optimization for Multi-echo Gradient Echo Acquisition and Image Reconstruction (MICCAI) [paper]
  • Generalised Super Resolution for Quantitative MRI Using Self-supervised Mixture of Experts (MICCAI) [paper] [code]
  • DA-VSR: Domain Adaptable Volumetric Super-Resolution for Medical Images (MICCAI) [paper]
  • Towards Ultrafast MRI via Extreme k-Space Undersampling and Superresolution (MICCAI) [paper]
  • Interpretable Deep Learning for Multimodal Super-Resolution of Medical Images (MICCAI) [paper]
  • MRI Super-Resolution Through Generative Degradation Learning (MICCAI) [paper]
  • Data augmentation for deep learning based accelerated MRI reconstruction with limited data (ICML) [paper] [code]
  • Deep Geometric Distillation Network for Compressive Sensing MRI (IEEE-EMBS BHI oral) [paper] [code]
  • Dual-Octave Convolution for Accelerated Parallel MR Image Reconstruction (AAAI) [paper code]
  • DONet: Dual-Octave Network for Fast MR Image Reconstruction (TNNLS) [paper]
  • Adaptive Gradient Balancing for Undersampled MRI Reconstruction and Image-to-Image Translation (ICCP) [paper]
  • Motion-Guided Physics-Based Learning for Cardiac MRI Reconstruction [paper]
  • Systematic evaluation of iterative deep neural networks for fast parallel MRI reconstruction with sensitivity-weighted coil combination [paper]
  • Regularization-Agnostic Compressed Sensing MRI Reconstruction with Hypernetworks [paper] [code]

2020

  • DuDoRNet: Learning a Dual-Domain Recurrent Network for Fast MRI Reconstruction with Deep T1 Prior (CVPR) [paper] [code]
  • GrappaNet: Combining Parallel Imaging With Deep Learning for Multi-Coil MRI Reconstruction (CVPR) [paper]
  • Deep-Learning-Based Optimization of the Under-Sampling Pattern in MRI (TCI) [paper]
  • Improving Amide Proton Transfer-Weighted MRI Reconstruction Using T2-Weighted Images (MICCAI) [paper]
  • End-to-End Variational Networks for Accelerated MRI Reconstruction (MICCAI) [paper]
  • MRI Image Reconstruction via Learning Optimization Using Neural ODEs (MICCAI) [paper]
  • Learning a Gradient Guidance for Spatially Isotropic MRI Super-Resolution Reconstruction (MICCAI) [paper]
  • Deep Attentive Wasserstein Generative Adversarial Networks for MRI Reconstruction with Recurrent Context-Awareness (MICCAI) [paper]
  • Model-Driven Deep Attention Network for Ultra-fast Compressive Sensing MRI Guided by Cross-contrast MR Image (MICCAI) [paper]
  • CDF-Net: Cross-Domain Fusion Network for Accelerated MRI Reconstruction (MICCAI) [paper]

2019

  • k-Space Deep Learning for Accelerated MRI [paper] [code]

2018

  • KIKI-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images [paper]
  • Learning a variational network for reconstruction of accelerated MRI data [paper] [code]
  • Bayesian Deep Learning for Accelerated MR Image Reconstruction [paper]

2017

  • A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction [paper] [code]
  • A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction [paper] [code]

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