{"total":205,"rows":[{"id":"2108.01368","title":"Robust Compressed Sensing MRI with Deep Generative Priors","created":"2021-08-03","cited_by_count":286,"area":null},{"id":"2201.03230","title":"Swin Transformer for Fast MRI","created":"2022-01-10","cited_by_count":56,"area":null},{"id":"2102.07737","title":"Zero-Shot Self-Supervised Learning for MRI Reconstruction","created":"2021-02-15","cited_by_count":54,"area":null},{"id":"2201.09376","title":"ReconFormer: Accelerated MRI Reconstruction Using Recurrent Transformer","created":"2022-01-23","cited_by_count":52,"area":null},{"id":"2203.12215","title":"Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging","created":"2022-03-23","cited_by_count":50,"area":null},{"id":"2106.14248","title":"Multi-Modal Transformer for Accelerated MR Imaging","created":"2021-06-27","cited_by_count":43,"area":null},{"id":"2110.09766","title":"Memory-Augmented Deep Unfolding Network for Compressive Sensing","created":"2021-10-19","cited_by_count":38,"area":null},{"id":"2112.12744","title":"AI-based Reconstruction for Fast MRI -- A Systematic Review and Meta-analysis","created":"2021-12-23","cited_by_count":38,"area":null},{"id":"2201.10776","title":"DSFormer: A Dual-domain Self-supervised Transformer for Accelerated Multi-contrast MRI Reconstruction","created":"2022-01-26","cited_by_count":31,"area":null},{"id":"2102.06665","title":"Bayesian Uncertainty Estimation of Learned Variational MRI Reconstruction","created":"2021-02-12","cited_by_count":30,"area":null},{"id":"2106.08886","title":"Over-and-Under Complete Convolutional RNN for MRI Reconstruction","created":"2021-06-16","cited_by_count":29,"area":null},{"id":"2105.12227","title":"Learning a Model-Driven Variational Network for Deformable Image Registration","created":"2021-05-25","cited_by_count":25,"area":null},{"id":"2103.02087","title":"Deep J-Sense: Accelerated MRI Reconstruction via Unrolled Alternating Optimization","created":"2021-03-02","cited_by_count":19,"area":null},{"id":"2101.09379","title":"SGD-Net: Efficient Model-Based Deep Learning with Theoretical Guarantees","created":"2021-01-22","cited_by_count":18,"area":null},{"id":"2107.09892","title":"Towards Lower-Dose PET using Physics-Based Uncertainty-Aware Multimodal Learning with Robustness to Out-of-Distribution Data","created":"2021-07-21","cited_by_count":18,"area":null},{"id":"2108.04543","title":"Known Operator Learning and Hybrid Machine Learning in Medical Imaging -- A Review of the Past, the Present, and the Future","created":"2021-08-10","cited_by_count":18,"area":null},{"id":"2110.00715","title":"An Optimization-Based Meta-Learning Model for MRI Reconstruction with Diverse Dataset","created":"2021-10-02","cited_by_count":18,"area":null},{"id":"2305.06739","title":"Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review","created":"2023-05-11","cited_by_count":18,"area":null},{"id":"2108.05603","title":"Multi-Modal MRI Reconstruction Assisted with Spatial Alignment Network","created":"2021-08-12","cited_by_count":16,"area":null},{"id":"2103.05214","title":"Universal Undersampled MRI Reconstruction","created":"2021-03-09","cited_by_count":15,"area":null},{"id":"2110.03343","title":"Uncertainty-aware GAN with Adaptive Loss for Robust MRI Image Enhancement","created":"2021-10-07","cited_by_count":15,"area":null},{"id":"2305.06822","title":"Implicit Neural Networks with Fourier-Feature Inputs for Free-breathing Cardiac MRI Reconstruction","created":"2023-05-11","cited_by_count":15,"area":null},{"id":"2307.13220","title":"One for Multiple: Physics-informed Synthetic Data Boosts Generalizable Deep Learning for Fast MRI Reconstruction","created":"2023-07-25","cited_by_count":15,"area":null},{"id":"2111.06401","title":"Stacked U-Nets with Self-Assisted Priors Towards Robust Correction of Rigid Motion Artifact in Brain MRI","created":"2021-11-11","cited_by_count":14,"area":null},{"id":"2203.16392","title":"On learning adaptive acquisition policies for undersampled multi-coil MRI reconstruction","created":"2022-03-30","cited_by_count":14,"area":null},{"id":"2105.06460","title":"End-to-End Sequential Sampling and Reconstruction for MRI","created":"2021-05-13","cited_by_count":13,"area":null},{"id":"2109.08618","title":"A review and experimental evaluation of deep learning methods for MRI reconstruction","created":"2021-09-17","cited_by_count":13,"area":null},{"id":"2109.09658","title":"FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging","created":"2021-09-20","cited_by_count":13,"area":null},{"id":"2302.09244","title":"Dual-Domain Self-Supervised Learning for Accelerated Non-Cartesian MRI Reconstruction","created":"2023-02-18","cited_by_count":13,"area":null},{"id":"2407.08256","title":"Adaptive Compressed Sensing with Diffusion-Based Posterior Sampling","created":"2024-07-11","cited_by_count":13,"area":null},{"id":"2208.07181","title":"One-shot Generative Prior in Hankel-k-space for Parallel Imaging Reconstruction","created":"2022-08-15","cited_by_count":12,"area":null},{"id":"2105.08175","title":"Transfer Learning Enhanced Generative Adversarial Networks for Multi-Channel MRI Reconstruction","created":"2021-05-17","cited_by_count":11,"area":null},{"id":"2204.02480","title":"Learning Optimal K-space Acquisition and Reconstruction using Physics-Informed Neural Networks","created":"2022-04-05","cited_by_count":11,"area":null},{"id":"2402.04097","title":"Analysis of Deep Image Prior and Exploiting Self-Guidance for Image Reconstruction","created":"2024-02-06","cited_by_count":11,"area":null},{"id":"2109.09738","title":"An Optimal Control Framework for Joint-channel Parallel MRI Reconstruction without Coil Sensitivities","created":"2021-09-20","cited_by_count":10,"area":null},{"id":"2207.00698","title":"Uncertainty Quantification for Deep Unrolling-Based Computational Imaging","created":"2022-07-02","cited_by_count":10,"area":null},{"id":"2308.02631","title":"Uncertainty Estimation and Propagation in Accelerated MRI Reconstruction","created":"2023-08-04","cited_by_count":10,"area":null},{"id":"2312.10271","title":"Robustness of Deep Learning for Accelerated MRI: Benefits of Diverse Training Data","created":"2023-12-16","cited_by_count":10,"area":null},{"id":"2105.05827","title":"20-fold Accelerated 7T fMRI Using Referenceless Self-Supervised Deep Learning Reconstruction","created":"2021-05-12","cited_by_count":9,"area":null},{"id":"2107.05533","title":"Deformation-Compensated Learning for Image Reconstruction without Ground Truth","created":"2021-07-12","cited_by_count":9,"area":null},{"id":"2202.13490","title":"Limitations of Deep Learning for Inverse Problems on Digital Hardware","created":"2022-02-28","cited_by_count":9,"area":null},{"id":"2310.07504","title":"PtychoDV: Vision Transformer-Based Deep Unrolling Network for Ptychographic Image Reconstruction","created":"2023-10-11","cited_by_count":9,"area":null},{"id":"2104.01102","title":"Deep Manifold Learning for Dynamic MR Imaging","created":"2021-03-09","cited_by_count":8,"area":null},{"id":"2202.01494","title":"PARCEL: Physics-based Unsupervised Contrastive Representation Learning for Multi-coil MR Imaging","created":"2022-02-03","cited_by_count":8,"area":null},{"id":"2203.05725","title":"Dual-Domain Reconstruction Networks with V-Net and K-Net for fast MRI","created":"2022-03-11","cited_by_count":8,"area":null},{"id":"2205.03519","title":"Self-supervised Deep Unrolled Reconstruction Using Regularization by Denoising","created":"2022-05-07","cited_by_count":8,"area":null},{"id":"2205.03883","title":"WKGM: Weight-K-space Generative Model for Parallel Imaging Reconstruction","created":"2022-05-08","cited_by_count":8,"area":null},{"id":"2307.12672","title":"Global k-Space Interpolation for Dynamic MRI Reconstruction using Masked Image Modeling","created":"2023-07-24","cited_by_count":8,"area":null},{"id":"2307.14363","title":"Unsupervised reconstruction of accelerated cardiac cine MRI using Neural Fields","created":"2023-07-24","cited_by_count":8,"area":null},{"id":"2102.00783","title":"An End-To-End-Trainable Iterative Network Architecture for Accelerated Radial Multi-Coil 2D Cine MR Image Reconstruction","created":"2021-02-01","cited_by_count":7,"area":null},{"id":"2201.09400","title":"Fast MRI Reconstruction: How Powerful Transformers Are?","created":"2022-01-23","cited_by_count":7,"area":null},{"id":"2206.04797","title":"Memory-efficient model-based deep learning with convergence and robustness guarantees","created":"2022-06-06","cited_by_count":7,"area":null},{"id":"2306.11023","title":"PINQI: An End-to-End Physics-Informed Approach to Learned Quantitative MRI Reconstruction","created":"2023-06-19","cited_by_count":7,"area":null},{"id":"2106.10195","title":"Non-Iterative Phase Retrieval With Cascaded Neural Networks","created":"2021-06-18","cited_by_count":6,"area":null},{"id":"2108.12460","title":"High Fidelity Deep Learning-based MRI Reconstruction with Instance-wise Discriminative Feature Matching Loss","created":"2021-08-27","cited_by_count":6,"area":null},{"id":"2112.05758","title":"Edge-Enhanced Dual Discriminator Generative Adversarial Network for Fast MRI with Parallel Imaging Using Multi-view Information","created":"2021-12-10","cited_by_count":6,"area":null},{"id":"2206.03698","title":"What do we learn? Debunking the Myth of Unsupervised Outlier Detection","created":"2022-06-08","cited_by_count":6,"area":null},{"id":"2211.00725","title":"LARO: Learned Acquisition and Reconstruction Optimization to accelerate Quantitative Susceptibility Mapping","created":"2022-11-01","cited_by_count":6,"area":null},{"id":"2412.10776","title":"Boosting ViT-based MRI Reconstruction from the Perspectives of Frequency Modulation, Spatial Purification, and Scale Diversification","created":"2024-12-14","cited_by_count":6,"area":null},{"id":"2103.04003","title":"Memory-efficient Learning for High-Dimensional MRI Reconstruction","created":"2021-03-06","cited_by_count":5,"area":null},{"id":"2103.14528","title":"Model-based Reconstruction with Learning: From Unsupervised to Supervised and Beyond","created":"2021-03-26","cited_by_count":5,"area":null},{"id":"2112.09760","title":"Learned Half-Quadratic Splitting Network for MR Image Reconstruction","created":"2021-12-17","cited_by_count":5,"area":null},{"id":"2203.03196","title":"Undersampled MRI Reconstruction with Side Information-Guided Normalisation","created":"2022-03-07","cited_by_count":5,"area":null},{"id":"2204.11189","title":"PUERT: Probabilistic Under-sampling and Explicable Reconstruction Network for CS-MRI","created":"2022-04-24","cited_by_count":5,"area":null},{"id":"2205.07062","title":"An Interpretable MRI Reconstruction Network with Two-grid-cycle Correction and Geometric Prior Distillation","created":"2022-05-14","cited_by_count":5,"area":null},{"id":"2206.02838","title":"Invertible Sharpening Network for MRI Reconstruction Enhancement","created":"2022-06-06","cited_by_count":5,"area":null},{"id":"2208.03904","title":"SelfCoLearn: Self-supervised collaborative learning for accelerating dynamic MR imaging","created":"2022-08-08","cited_by_count":5,"area":null},{"id":"2210.06330","title":"CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping","created":"2022-10-12","cited_by_count":5,"area":null},{"id":"2304.00725","title":"CG-3DSRGAN: A classification guided 3D generative adversarial network for image quality recovery from low-dose PET images","created":"2023-04-03","cited_by_count":5,"area":null},{"id":"2309.16831","title":"Propagation and Attribution of Uncertainty in Medical Imaging Pipelines","created":"2023-09-28","cited_by_count":5,"area":null},{"id":"2102.00047","title":"Model Adaptation for Image Reconstruction using Generalized Stein's Unbiased Risk Estimator","created":"2021-01-29","cited_by_count":4,"area":null},{"id":"2102.13066","title":"On Instabilities of Conventional Multi-Coil MRI Reconstruction to Small Adverserial Perturbations","created":"2021-02-25","cited_by_count":4,"area":null},{"id":"2109.11524","title":"End-to-End AI-based MRI Reconstruction and Lesion Detection Pipeline for Evaluation of Deep Learning Image Reconstruction","created":"2021-09-23","cited_by_count":4,"area":null},{"id":"2202.14005","title":"Deep, Deep Learning with BART","created":"2022-02-28","cited_by_count":4,"area":null},{"id":"2206.08984","title":"Multi-scale Super-resolution Magnetic Resonance Spectroscopic Imaging with Adjustable Sharpness","created":"2022-06-17","cited_by_count":4,"area":null},{"id":"2207.08412","title":"Multi-branch Cascaded Swin Transformers with Attention to k-space Sampling Pattern for Accelerated MRI Reconstruction","created":"2022-07-18","cited_by_count":4,"area":null},{"id":"2207.13560","title":"D3C2-Net: Dual-Domain Deep Convolutional Coding Network for Compressive Sensing","created":"2022-07-27","cited_by_count":4,"area":null},{"id":"2302.10273","title":"ViGU: Vision GNN U-Net for Fast MRI","created":"2023-01-23","cited_by_count":4,"area":null},{"id":"2306.00530","title":"CL-MRI: Self-Supervised Contrastive Learning to Improve the Accuracy of Undersampled MRI Reconstruction","created":"2023-06-01","cited_by_count":4,"area":null},{"id":"2309.10227","title":"Learning Dynamic MRI Reconstruction with Convolutional Network Assisted Reconstruction Swin Transformer","created":"2023-09-19","cited_by_count":4,"area":null},{"id":"2402.15939","title":"Deep Separable Spatiotemporal Learning for Fast Dynamic Cardiac MRI","created":"2024-02-24","cited_by_count":4,"area":null},{"id":"2505.24136","title":"Sparsity-Driven Parallel Imaging Consistency for Improved Self-Supervised MRI Reconstruction","created":"2025-05-30","cited_by_count":4,"area":null},{"id":"2105.01800","title":"Generative Adversarial Networks (GAN) Powered Fast Magnetic Resonance Imaging -- Mini Review, Comparison and Perspectives","created":"2021-05-04","cited_by_count":3,"area":null},{"id":"2111.09492","title":"Reference-based Magnetic Resonance Image Reconstruction Using Texture Transformer","created":"2021-11-18","cited_by_count":3,"area":null},{"id":"2111.15498","title":"Assessment of Data Consistency through Cascades of Independently Recurrent Inference Machines for fast and robust accelerated MRI reconstruction","created":"2021-11-30","cited_by_count":3,"area":null},{"id":"2205.01770","title":"Data-Consistent Non-Cartesian Deep Subspace Learning for Efficient Dynamic MR Image Reconstruction","created":"2022-05-03","cited_by_count":3,"area":null},{"id":"2206.06947","title":"K-Space Transformer for Undersampled MRI Reconstruction","created":"2022-06-14","cited_by_count":3,"area":null},{"id":"2207.08393","title":"GLEAM: Greedy Learning for Large-Scale Accelerated MRI Reconstruction","created":"2022-07-18","cited_by_count":3,"area":null},{"id":"2210.12548","title":"JoJoNet: Joint-contrast and Joint-sampling-and-reconstruction Network for Multi-contrast MRI","created":"2022-10-22","cited_by_count":3,"area":null},{"id":"2302.02688","title":"HyperSLICE: HyperBand optimized Spiral for Low-latency Interactive Cardiac Examination","created":"2023-02-06","cited_by_count":3,"area":null},{"id":"2303.00111","title":"PixCUE: Joint Uncertainty Estimation and Image Reconstruction in MRI using Deep Pixel Classification","created":"2023-02-28","cited_by_count":3,"area":null},{"id":"2303.12735","title":"SMUG: Towards robust MRI reconstruction by smoothed unrolling","created":"2023-03-14","cited_by_count":3,"area":null},{"id":"2304.11400","title":"Fast MRI Reconstruction via Edge Attention","created":"2023-04-22","cited_by_count":3,"area":null},{"id":"2304.12507","title":"Learning Task-Specific Strategies for Accelerated MRI","created":"2023-04-25","cited_by_count":3,"area":null},{"id":"2306.11238","title":"CAMP-Net: Consistency-Aware Multi-Prior Network for Accelerated MRI Reconstruction","created":"2023-06-20","cited_by_count":3,"area":null},{"id":"2306.11977","title":"Encoding Enhanced Complex CNN for Accurate and Highly Accelerated MRI","created":"2023-06-21","cited_by_count":3,"area":null},{"id":"2306.12365","title":"Attention Hybrid Variational Net for Accelerated MRI Reconstruction","created":"2023-06-21","cited_by_count":3,"area":null},{"id":"2405.02958","title":"Score-based Generative Priors Guided Model-driven Network for MRI Reconstruction","created":"2024-05-05","cited_by_count":3,"area":null},{"id":"2405.12357","title":"Paired Conditional Generative Adversarial Network for Highly Accelerated Liver 4D MRI","created":"2024-05-20","cited_by_count":3,"area":null},{"id":"2406.02626","title":"A Brief Overview of Optimization-Based Algorithms for MRI Reconstruction Using Deep Learning","created":"2024-06-03","cited_by_count":3,"area":null}],"next_cursor":"100"}