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Partial Fourier reconstruction of complex MR images using complex‐valued convolutional neural networks
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fastMRI+: Clinical Pathology Annotations for Knee and Brain Fully Sampled Multi-Coil MRI Data
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Spatio-temporal multi-task network cascade for accurate assessment of cardiac CT perfusion
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Multi-view convolutional neural networks for automated ocular structure and tumor segmentation in retinoblastoma
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Systematic evaluation of iterative deep neural networks for fast parallel MRI reconstruction with sensitivity‐weighted coil combination
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Joint Deep Model-based MR Image and Coil Sensitivity Reconstruction Network (Joint-ICNet) for Fast MRI
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Unsupervised MRI Reconstruction via Zero-Shot Learned Adversarial Transformers
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Projection-Based cascaded U-Net model for MR image reconstruction
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Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction
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Analysis of deep complex‐valued convolutional neural networks for MRI reconstruction and phase‐focused applications
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Memory-efficient Learning for High-Dimensional MRI Reconstruction
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State-of-the-Art Machine Learning MRI Reconstruction in 2020: Results of the Second fastMRI Challenge
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A deep unrolling network inspired by total variation for compressed sensing MRI
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XPDNet for MRI Reconstruction: an Application to the fastMRI 2020 Brain Challenge
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Multi-channel MR Reconstruction (MC-MRRec) Challenge - Comparing Accelerated MR Reconstruction Models and Assessing Their Genereralizability to Datasets Collected with Different Coils
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Prior-Guided Image Reconstruction for Accelerated Multi-Contrast MRI via Generative Adversarial Networks
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Unsupervised MRI Reconstruction with Generative Adversarial Networks
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An Adaptive Intelligence Algorithm for Undersampled Knee MRI Reconstruction
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End-to-End Variational Networks for Accelerated MRI Reconstruction
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Benchmarking Deep Nets MRI Reconstruction Models on the Fastmri Publicly Available Dataset
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Advancing machine learning for MR image reconstruction with an open competition: Overview of the 2019 fastMRI challenge
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Σ-net: Ensembled Iterative Deep Neural Networks for Accelerated Parallel MR Image Reconstruction
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
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Pyramid Convolutional RNN for MRI Image Reconstruction
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Dual-domain Cascade of U-nets for Multi-channel Magnetic Resonance Image Reconstruction
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GrappaNet: Combining Parallel Imaging With Deep Learning for Multi-Coil MRI Reconstruction
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i-RIM applied to the fastMRI challenge
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Optimal Transport Driven CycleGAN for Unsupervised Learning in Inverse Problems
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VS-Net: Variable splitting network for accelerated parallel MRI reconstruction
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DeepcomplexMRI: Exploiting deep residual network for fast parallel MR imaging with complex convolution
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LORAKI: Autocalibrated Recurrent Neural Networks for Autoregressive MRI Reconstruction in k-Space
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Recurrent inference machines for reconstructing heterogeneous MRI data
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A Hybrid, Dual Domain, Cascade of Convolutional Neural Networks for Magnetic Resonance Image Reconstruction
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fastMRI: An Open Dataset and Benchmarks for Accelerated MRI
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MRI Reconstruction Via Cascaded Channel-Wise Attention Network
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Scan‐specific robust artificial‐neural‐networks for k‐space interpolation (RAKI) reconstruction: Database‐free deep learning for fast imaging
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DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction
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KIKI‐net: cross‐domain convolutional neural networks for reconstructing undersampled magnetic resonance images
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Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN
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MoDL: Model-Based Deep Learning Architecture for Inverse Problems
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Convolutional Recurrent Neural Networks for Dynamic MR Image Reconstruction
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A Transfer‐Learning Approach for Accelerated MRI Using Deep Neural Networks
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Compressed Sensing MRI Reconstruction Using a Generative Adversarial Network With a Cyclic Loss
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Learned Primal-Dual Reconstruction
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Recurrent Inference Machines for Solving Inverse Problems
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ADMM-Net: A Deep Learning Approach for Compressive Sensing MRI
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Image reconstruction by domain-transform manifold learning
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A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction
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Learning a variational network for reconstruction of accelerated MRI data
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Is a Complex-Valued Stepsize Advantageous in Complex-Valued Gradient Learning Algorithms?
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Learning to learn by gradient descent by gradient descent
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Learning Representations Using Complex-Valued Nets
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U-Net: Convolutional Networks for Biomedical Image Segmentation
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On learning optimized reaction diffusion processes for effective image restoration
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Adam: A Method for Stochastic Optimization
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Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
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ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: Where SENSE meets GRAPPA
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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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On the difficulty of training recurrent neural networks
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Wirtinger Calculus Based Gradient Descent and Levenberg-Marquardt Learning Algorithms in Complex-Valued Neural Networks
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Slow and fast scales for superprocess limits of age-structured populations
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Sparse MRI: The application of compressed sensing for rapid MR imaging
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Image quality assessment: from error visibility to structural similarity
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On the Surprising Behavior of Distance Metrics in High Dimensional Spaces
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SENSE: Sensitivity encoding for fast MRI
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Simultaneous acquisition of spatial harmonics (SMASH): Fast imaging with radiofrequency coil arrays
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DIRECT: Deep Image REConstruction Toolkit 2022
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Evaluation of the Robustness of Learned MR Image Reconstruction to Systematic Deviations Between Training and Test Data for the Models from the fastMRI Challenge
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andCukur T 2020a Prior-guided image reconstruction for acceleratedmulti-contrast
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A and SodicksonDK2020bGrappaNet: combining parallel imagingwith deep learning
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CaanMWAandWellingM2019Recurrent inferencemachines for reconstructing
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RMandHammernikK 2019Σ-net: Ensembled Iterative DeepNeuralNetworks for Accelerated
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andÖktemO2018 Learned primal-dual reconstruction IEEETrans.Med. Imaging 37 1322–32 AggarwalHK,ManiMP and JacobM2019MoDL:model-based deep learning architecture for inverse problems IEEETrans.Med
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PyTorch Lightning The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate
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LeeH J andHwangD2018KIKI-net: cross-domain convolutional neural networks for reconstructing
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Statistical Parametric Mapping The Analysis Of Functional Brain Images
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Berkeley Advanced Reconstruction Toolbox
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Creation of fully sampled MR data repository for compressed sensing of the knee SMRT Conf
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Linear Inverse Problems
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A Threshold Selection Method from Gray-Level Histograms
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KIKINet (third row-first), and the CascadeNet (third row-third) enforced DC explicitly by a formulated DC term
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The CIRIM 5C (first row-fourth), the RIM (second row-second), and the IRIM (second row-fourth) enforced Data Consistency (DC) implicitly by gradient descent
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Submitted to Magnetic Resonance in Medicine Scan Specific Artifact Reduction in K-space (SPARK) Neural Networks Synergize with Physics-based Reconstruction to Accelerate MRI
