A Deep Cascade of Convolutional Neural Networks for Dynamic MR Image Reconstruction

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474

U-Net: Convolutional Networks for Biomedical Image Segmentation

Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404

Adam: A Method for Stochastic Optimization

Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562

Learning representations by back-propagating errors

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892

Book Review: Pattern Recognition and Machine Learning

Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741

Image Super-Resolution Using Deep Convolutional Networks

Dong, C., Loy, C. C., He, K., & Tang, X. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2439281 · Cited by 10,105

Accelerated 3D myelin water imaging using joint spatio‐temporal reconstruction

Lee, J., Yi, J., Kim, J., Ryu, K., Han, D., Kim, S., Lee, S., Kim, D. Y., & Kim, D. (2022) · Medical Physics · doi:10.1002/mp.15788 · Cited by 4

A cascade of preconditioned conjugate gradient networks for accelerated magnetic resonance imaging

Kim, M., & Chung, W. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.107090 · Cited by 4

Accurate and Lightweight MRI Super-Resolution Via Multi-Scale Bidirectional Fusion Attention Network

Xu, L., Li, G., & Chen, Q. (2022) · PLoS ONE · doi:10.1371/journal.pone.0277862 · Cited by 4

Cascade Multiscale Swin-Conv Network for Fast MRI Reconstruction

Ye, S., Xie, X., Xiong, D., Ouyang, L., & Zhang, X. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-18910-4_16 · Cited by 4

Propagation and Attribution of Uncertainty in Medical Imaging Pipelines

Feiner, L., Menten, M. J., Hammernik, K., Hager, P., Huang, W., Rueckert, D., Braren, R., & Kaissis, G. A. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-44336-7_1 · Cited by 5

SADIR: Shape-Aware Diffusion Models for 3D Image Reconstruction

Jayakumar, N., Hossain, T., & Zhang, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-46914-5_23 · Cited by 4

k-t CLAIR: Self-consistency Guided Multi-prior Learning for Dynamic Parallel MR Image Reconstruction

Zhang, L., & Chen, W. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-52448-6_30 · Cited by 4

Enhancing quality and speed in database‐free neural network reconstructions of undersampled MRI with SCAMPI

Siedler, T. M., Jakob, P. M., & Herold, V. (2024) · Magnetic Resonance in Medicine · doi:10.1002/mrm.30114 · Cited by 4

Deep Network Cascade for Dynamic Cardiac MRI Reconstruction With Motion Feature Incorporation and the Fourier Neural Attention

Liu, J., Qin, C., & Yaghoobi, M. (2024) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2024.3402335 · Cited by 4

CoRRECT: A Deep Unfolding Framework for Motion-Corrected Quantitative R2* Mapping

Xu, X., Gan, W., Kothapalli, S. V. V. N., Yablonskiy, D. A., & Kamilov, U. S. (2025) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-025-01236-y · Cited by 4

High‐performance dynamic magnetic resonance image reconstruction and synthesis employing deep feature learning convolutional networks

Jeyaraj, P. R., & Nadar, E. R. S. (2019) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22381 · Cited by 4

BlackBox: Generalizable reconstruction of extremal values from incomplete spatio-temporal data

Ivek, T., & Vlah, D. (2020) · Extremes · doi:10.1007/s10687-020-00396-x · Cited by 4

Model Meets Deep Learning in Image Inverse Problems

Wang, N., & Sun, J. Z. (2020) · CSIAM Transactions on Applied Mathematics · doi:10.4208/csiam-am.2020-0016 · Cited by 5

Spatial Semantic-Preserving Latent Space Learning for Accelerated DWI Diagnostic Report Generation

Gasimova, A., Seegoolam, G., Chen, L., Bentley, P., & Rueckert, D. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59728-3_33 · Cited by 5

Triple-D network for efficient undersampled magnetic resonance images reconstruction

Zhao, L., Bao, Q., Yang, C., Chen, F., Wu, G., Sun, L., Zhang, Z., & Liu, C. (2020) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2020.11.010 · Cited by 4

Multi-Domain Neumann Network with Sensitivity Maps for Parallel MRI Reconstruction

Lee, J.-H., Kang, J., Oh, S., & Ye, D. H. (2022) · Sensors · doi:10.3390/s22103943 · Cited by 4

Deep compressed sensing MRI via a gradient‐enhanced fusion model

Dai, Y., Wang, C., & Wang, H. (2023) · Medical Physics · doi:10.1002/mp.16164 · Cited by 4

A Structural Oriented Training Method for GAN Based Fast Compressed Sensing MRI

An, H., & Zhang, Y. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34110-7_40 · Cited by 4

Convolutional Dictionary Learning by End-To-End Training of Iterative Neural Networks

Kofler, A., Wald, C., Schaeffter, T., Haltmeier, M., & Kolbitsch, C. (2022) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco55093.2022.9909604 · Cited by 4

k-Space Deep Learning for Parallel MRI: Application to Time-Resolved MR AngiographyPreprint

Cha, E., Kim, E. Y., & Ye, J. C. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.00806 · Cited by 4

Generalizing Deep Learning MRI Reconstruction across Different DomainsPreprint

Ouyang, C., Schlemper, J., Biffi, C., Seegoolam, G., Caballero, J., Price, A. N., Hajnal, J. V., & Rueckert, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.10815 · Cited by 4

Conditional WGANs with Adaptive Gradient Balancing for Sparse MRI ReconstructionPreprint

Malkiel, I., Ahn, S., Taviani, V., Menini, A., Wolf, L., & Hardy, C. J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.00985 · Cited by 4

k-t NEXT: Dynamic MR Image Reconstruction Exploiting Spatio-temporal CorrelationsPreprint

Qin, C., Schlemper, J., Duan, J., Seegoolam, G., Price, A. N., Hajnal, J. V., & Rueckert, D. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.09425 · Cited by 4

Gauss-Newton Unrolled Neural Networks and Data-driven Priors for Regularized PSSE with RobustnessPreprint

Yang, Q., Sadeghi, A., Wang, G., Giannakis, G. B., & Sun, J. Q. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.01667 · Cited by 4

End-to-End AI-based MRI Reconstruction and Lesion Detection Pipeline for Evaluation of Deep Learning Image ReconstructionPreprint

Zhao, R., Zhang, Y., Yaman, B., Lungren, M. P., & Hansen, M. S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.11524 · Cited by 4

Machine Learning for Clinical Electrophysiology

Loewe, A., Luongo, G., & Sánchez, J. (2022) · Lecture notes in bioengineering · doi:10.1007/978-981-19-6649-1_6 · Cited by 4

Motion Compensated Unsupervised Deep Learning for 5D MRI

Kettelkamp, J., Romanin, L., Piccini, D., Priya, S., & Jacob, M. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43999-5_40 · Cited by 3

Perceptual contrast and residual self-attention generative adversarial network-based for highly under-sampled MRI reconstruction

Lin, S., Fan, X., Ma, F., Liu, F., Wang, L., Wang, Y., & Qiu, H. (2023) · Digital Signal Processing · doi:10.1016/j.dsp.2023.104277 · Cited by 3

Attention hybrid variational net for accelerated MRI reconstruction

Shen, G., Hao, B., Li, M., Farris, C. W., Paschalidis, I. C., Anderson, S. W., & Zhang, X. (2023) · APL Machine Learning · doi:10.1063/5.0165485 · Cited by 3

AVS-Net: Attention-based Variable Splitting Network for P-MRI Acceleration

Zhang, Y., Li, J., Wang, Z., Duan, J., & Li, J. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385266 · Cited by 3

Evaluating Compressive Sensing on the Security of Computer Vision Systems

Cheng, Y., Zhou, B., Chen, Y., Chen, Y., Ji, X., & Xu, W. (2024) · ACM Transactions on Sensor Networks · doi:10.1145/3645093 · Cited by 3

Accelerated four-dimensional free-breathing whole-liver water-fat magnetic resonance imaging with deep dictionary learning and chemical shift modeling

Li, S., Wang, Z., Ding, Z., She, H., & Du, Y. P. (2024) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-23-1396 · Cited by 3

Effect of MR head coil geometry on deep‐learning‐based MR image reconstruction

Dubljevic, N., Moore, S., Lauzon, M. L., de Souza, R. M., & Frayne, R. (2024) · Magnetic Resonance in Medicine · doi:10.1002/mrm.30130 · Cited by 3

Estimation-Denoising Integration Network Architecture With Updated Parameter for MRI Reconstruction

Wu, T., Liu, S., Zhang, H., & Zeng, T. (2025) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2025.3531729 · Cited by 3

Spatial-frequency aware zero-centric residual unfolding network for MRI reconstruction

Lian, Y., Liu, Z., Wang, J., & Lu, S. (2025) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2025.110334 · Cited by 3

FDuDoCLNet: Fully dual-domain contrastive learning network for parallel MRI reconstruction

Zhang, H., Yang, T., Wang, H., Fan, J., Zhang, W., & Ji, M. (2025) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2025.110336 · Cited by 3

Deep-ER: Deep Learning ECCENTRIC Reconstruction for fast high-resolution neurometabolic imaging

Weiser, P. J., Langs, G., Bogner, W., Motyka, S., Strasser, B., Golland, P., Singh, N., Dietrich, J., Uhlmann, E. J., Batchelor, T. T., et al. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121045 · Cited by 3

Deep graph embedding based on Laplacian eigenmaps for MR fingerprinting reconstruction

Li, P., & Hu, Y. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103481 · Cited by 3

Exploring the potential performance of 0.2 T low-field unshielded MRI scanner using deep learning techniques

Li, L., He, Q., Wei, S., Wang, H., Wang, Z., & Yang, W. (2025) · Magnetic Resonance Materials in Physics Biology and Medicine · doi:10.1007/s10334-025-01234-6 · Cited by 3

MHWT: Wide-range attention modeling using window transformer for multi-modal MRI reconstruction

Han, Q., & Du, H. (2025) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2025.110362 · Cited by 3

Deep Convolutional Neural Network Enhanced Non-uniform Fast Fourier Transform for Undersampled MRI Reconstruction

Li, Y., Qi, H., Hu, Z., Sun, H., Li, G., Zhang, Z., Liu, Y., Guo, H., & Chen, H. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02378-7 · Cited by 3

Sampling-Pattern-Agnostic MRI Reconstruction Through Adaptive Consistency Enforcement with Diffusion Model

Malyala, A., Zhang, Z., Wang, C., & Chen, Q. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-87756-8_14 · Cited by 3

Enhancing Hippocampal Subfield Visualization Through Deep Learning Reconstructed MRI Scans

Clodi, N., Bender, B., Hecke, G., Hauptvogel, K., Gohla, G., Hauser, T., Ghibes, P., Hergan, K., Ernemann, U., & Estler, A. (2025) · Diagnostics · doi:10.3390/diagnostics15121523 · Cited by 3

Hierarchical refinement with adaptive deformation cascaded for multi-scale medical image registration

Hussain, N., Yan, Z., Cao, W., & Anwar, M. (2025) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2025.110449 · Cited by 3

Robust Physics-Based Deep MRI Reconstruction via Diffusion Purification

Alkhouri, I., Liang, S., Wang, R., Qu, Q., & Ravishankar, S. (2026) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2025.3631742 · Cited by 6

Content-aware compressive magnetic resonance image reconstruction

Weller, D. S., Salerno, M., & Meyer, C. H. (2018) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2018.06.008 · Cited by 3

Learning How to Interpolate Fourier Data With Unknown Autoregressive Structure: An Ensemble-Based Approach

Kim, T. H., & Haldar, J. P. (2019) · Asilomar Conference on Signals, Systems and Computers (ACSSC) · doi:10.1109/ieeeconf44664.2019.9048755 · Cited by 3

MR Image Reconstruction Based on Densely Connected Residual Generative Adversarial Network–DCR-GAN

Aghabiglou, A., & Ekşioğlu, E. M. (2021) · Communications in computer and information science · doi:10.1007/978-3-030-88113-9_55 · Cited by 3

Fractional Sailfish Optimizer with Deep Convolution Neural Network for Compressive Sensing Based Magnetic Resonance Image Reconstruction

Kumar, P. A., Gunasundari, R., & Aarthi, R. (2021) · The Computer Journal · doi:10.1093/comjnl/bxab160 · Cited by 3

Enhanced Imaging for Forward Looking MIMO SAR Via Un-Supervised Deep Basis Pursuit

Kotte, V. V., Gishkori, S., Masood, M., & Al-Naffouri, T. Y. (2022) · IEEE Radar Conference (RadarConf) · doi:10.1109/radarconf2248738.2022.9764257 · Cited by 3

A miniature U-net for k-space-based parallel magnetic resonance imaging reconstruction with a mixed loss function

Xu, L., Xu, J., Zheng, Q. S., Yuan, J., & Liu, J. (2022) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-21-1212 · Cited by 3

Deep learning‐based Accelerated and Noise‐Suppressed Estimation (DANSE) of quantitative Gradient‐Recalled Echo (qGRE) magnetic resonance imaging metrics associated with human brain neuronal structure and hemodynamic properties

Kahali, S., Kothapalli, S. V. V. N., Xu, X., Kamilov, U. S., & Yablonskiy, D. A. (2022) · NMR in Biomedicine · doi:10.1002/nbm.4883 · Cited by 3

FNSAM: Image super-resolution using a feedback network with self-attention mechanism

Huang, Y., Wang, W., & Li, M. (2023) · Technology and Health Care · doi:10.3233/thc-236033 · Cited by 3

Artificial intelligence in cardiac MRI

Ghodrati, V., & Finn, J. P. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-90534-3.00007-x · Cited by 3

DIR3D: Cascaded Dual-Domain Inter-Scale Mutual Reinforcement 3D Network for highly accelerated 3D MR image reconstruction

Sun, Y., Liu, X., Liu, Y., Hou, Y., & Pang, Y. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105810 · Cited by 3

Deep learning for medical image reconstruction

Zhao, J. H., Huang, Q., Liang, D., Chen, Y., & Wang, G. (2024) · Elsevier eBooks · doi:10.1016/b978-0-32-385124-4.00019-2 · Cited by 3

Fourier Transformer for Joint Super-Resolution and Reconstruction of MR Image

Chen, J., Wu, F., Wang, W., & Sheng, H. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-53308-2_26 · Cited by 3

NoSENSE: Learned Unrolled Cardiac MRI Reconstruction Without Explicit Sensitivity Maps

Zimmermann, F. F., & Kofler, A. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-52448-6_43 · Cited by 3

Learning Dynamic MRI Reconstruction with Convolutional Network Assisted Reconstruction Swin Transformer

Xu, D., Liu, H., Ruan, D., & Sheng, K. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-47425-5_1 · Cited by 3

TTMRI: Multislice texture transformer network for undersampled MRI reconstruction

Zhang, X., Liu, Z., Wan, Y., Ling, B. W., & Xiong, D. (2024) · IET Image Processing · doi:10.1049/ipr2.13089 · Cited by 3

Multidisciplinary quantitative and qualitative assessment of IDH-mutant gliomas with full diagnostic deep learning image reconstruction

Ruff, C., Bombach, P., Roder, C., Weinbrenner, E., Artzner, C. P., Zerweck, L., Paulsen, F., Hauser, T., Ernemann, U., & Gohla, G. (2024) · European Journal of Radiology Open · doi:10.1016/j.ejro.2024.100617 · Cited by 3

Generalizable, sequence‐invariant deep learning image reconstruction for subspace‐constrained quantitative MRI

Hu, Z., Chen, Z., Cao, T., Lee, H., Xie, Y., Li, D., & Christodoulou, A. (2025) · Magnetic Resonance in Medicine · doi:10.1002/mrm.30433 · Cited by 3

Artificial intelligence in cardiovascular magnetic resonance imaging

Castellaccio, A., Arostegui, N. A., Jiménez, M. P., Tapia, D. Q., Zurita, M. B., & Galván, E. V. (2025) · Radiología (English Edition) · doi:10.1016/j.rxeng.2025.03.001 · Cited by 3

Efficient Model-Based Deep Learning via Network Pruning and Fine-Tuning

Park, C. Y., Gan, W., Zou, Z., Hu, Y., Sun, Z., & Kamilov, U. S. (2025) · Journal of Mathematical Imaging and Vision · doi:10.1007/s10851-025-01241-1 · Cited by 3

APG-Net: APG Optimization-Induced Multiscale Attention Network for MRI Reconstruction From Undersampled k-Space Measurements

Gan, H., Zhao, Z., Chen, Y., Shan, S., Shi, B., & Liu, C. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3554865 · Cited by 3

Comparison of Neural Network Architectures for Physics-Driven Deep Learning MRI Reconstruction

Yaman, B., Hosseini, S. A. H., Moeller, S., & Akçakaya, M. (2019) · IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) · doi:10.1109/iemcon.2019.8936238 · Cited by 3

Single patient convolutional neural networks for real-time MR reconstruction: coherent low-resolution versus incoherent undersampling

Dietz, B., Yun, J., Yip, E., Gabos, Z., Fallone, B. G., & Wachowicz, K. (2020) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab7d13 · Cited by 3

A Wide Multimodal Dense U-Net for Fast Magnetic Resonance Imaging

Falvo, A., Comminiello, D., Scardapane, S., Scarpiniti, M., & Uncini, A. (2020) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco47968.2020.9287519 · Cited by 3

Proximal-Gen for fast compressed sensing recovery

Cai, L., Fu, Y., Zhu, T., Xiang, Y., & Zeng, H. (2021) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2021.103358 · Cited by 3

DEMO: Deep MR Parametric Mapping with Unsupervised Multi-Tasking Framework

Cheng, J., Liu, Y., Zhu, Y., & Liang, D. (2021) · Investigative Magnetic Resonance Imaging · doi:10.13104/imri.2021.25.4.300 · Cited by 3

Assessment of Dose-Reduction Strategies in Wavelength-Selective Neutron Tomography

Daugherty, M. C., DiStefano, V. H., LaManna, J. M., Jacobson, D. L., Kienzle, P., Kim, Y., Hussey, D. S., & Bajcsy, P. (2023) · SN Computer Science · doi:10.1007/s42979-023-02059-7 · Cited by 3

A Modified GAN for Compressed Sensing MRI

Geng, Z., Tao, J., & Xu, J. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1642/1/012001 · Cited by 3

A Survey On Recent Approaches In Image Reconstruction

Pullanji, V. P., & Jayasree, M. (2020) · International Conference on Power, Instrumentation, Control and Computing (PICC) · doi:10.1109/picc51425.2020.9362425 · Cited by 3

Learning Unrolling-Based Neural Network for Magnetic Resonance Imaging Reconstruction

Yan, Q., Liu, L., & Mei, L. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-06427-2_11 · Cited by 3

Magnitude-image based data-consistent deep learning method for MRI super resolution

Lin, Z., & Chen, Z. (2022) · Proceedings - IEEE Symposium on Computer-Based Medical Systems · doi:10.1109/cbms55023.2022.00060 · Cited by 3

On Learned Operator Correction in Inverse Problems

Lunz, S., Hauptmann, A., Tarvainen, T., Schönlieb, C., & Arridge, S. (2021) · SIAM Journal on Imaging Sciences · doi:10.1137/20m1338460 · Cited by 3

Detection and Correction of Cardiac MR Motion Artefacts during Reconstruction from K-spacePreprint

Oksuz, L., Clough, J. R., Ruijsink, B., Anton, E. P., Bustin, A., da Cruz, G. J. L., Prieto, C., Rueckert, D., King, A. P., & Schnabel, J. A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.05695 · Cited by 3

LANTERN: learn analysis transform network for dynamic magnetic resonance imaging with small datasetPreprint

Wang, S., Chen, Y., Xiao, T., Ke, Z., Liu, Q., & Zheng, H. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1908.09140 · Cited by 3

Self-supervised Fine-tuning for Correcting Super-Resolution Convolutional Neural NetworksPreprint

Lucas, A. M., López-Tapia, S., Molina, R., & Katsaggelos, A. K. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.12879 · Cited by 3

OT-driven Multi-Domain Unsupervised Ultrasound Image Artifact Removal using a Single CNNPreprint

Huh, J., Khan, S., & Ye, J. C. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.05205 · Cited by 3

Model Adaptation for Image Reconstruction using Generalized Stein's Unbiased Risk EstimatorPreprint

Aggarwal, H. K., & Jacob, M. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2102.00047 · Cited by 3

Deep Learning Techniques for Compressive Sensing-Based Reconstruction and Inference -- A Ubiquitous Systems PerspectivePreprint

Machidon, A. L., & Pejović, V. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.13191 · Cited by 3

Adaptive Magnetic ResonancePreprint

Beracha, I., Seginer, A., & Tal, A. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.03.16.484410 · Cited by 3

Special Issue: Artificial Intelligence in Advanced Medical Imaging

Jia, H., Jiao, Q., & Liu, M. (2024) · Bioengineering · doi:10.3390/bioengineering11121229 · Cited by 3

T1/T2 Relaxation Temporal Modelling from Accelerated Acquisitions Using a Latent Transformer

Tänzer, M., Wang, F., Qiao, M., Bai, W., Rueckert, D., Yang, G., & Nielles‐Vallespin, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-52448-6_28 · Cited by 2

Evaluating Machine Learning-Based MRI Reconstruction Using Digital Image Quality Phantoms

Tan, F. Z., Delfino, J. G., & Zeng, R. (2024) · Bioengineering · doi:10.3390/bioengineering11060614 · Cited by 2

Optimizing hip MRI: enhancing image quality and elevating inter-observer consistency using deep learning-powered reconstruction

Kang, Y., Li, W., Lv, Q., Tao, Q., Sun, J., Dang, J., Niu, X., Liu, Z., Li, S., Zhang, Z., et al. (2025) · BMC Medical Imaging · doi:10.1186/s12880-025-01554-y · Cited by 2

Photoacoustic image reconstruction with the FD-UGAN model

Zhang, J., Yan, S., Lei, Y., Wang, P., Gao, X. Z., Li, X., & Cao, H. (2025) · Applied Optics · doi:10.1364/ao.559626 · Cited by 2

An All-in-One Approach for Accelerated Cardiac MRI Reconstruction

Hamedani, K. A., Razizadeh, N., Nabavi, S., & Moghaddam, M. E. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-87756-8_45 · Cited by 2

HyperCMR: Enhanced Multi-contrast CMR Reconstruction with Eagle Loss

Xu, R., Özer, C., & Öksüz, İ. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-87756-8_15 · Cited by 2

Assessment of Robustness of MRI Radiomic Features in the Abdomen: Impact of Deep Learning Reconstruction and Accelerated Acquisition

Zhong, J., Xing, Y., Hu, Y., Liu, X., Dai, S., Ding, D., Lu, J. A., Yang, J., Song, Y. S., Lu, M., et al. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01503-9 · Cited by 2

Spatial Frequency Adaptive Spatiotemporal Learning for Accelerating CMR Reconstruction

Li, B., Zhang, W., Mukhopadhyay, S. C., Li, Y., & Liu, Z. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3591859 · Cited by 2

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Zhang, C., Tu, X., Cui, Z., Gu, X., Li, K., & Lu, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-24493-8 · Cited by 2

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Mahmoodi, A., Yeluru, A., Aguirre-Chavez, J., Lamar-Bruno, K., Punjabi, K. P., Malkasian, S., Song, A. S., Masutani, E., & Hsiao, A. (2025) · Journal of Thoracic Imaging · doi:10.1097/rti.0000000000000864 · Cited by 4

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Qin, C., Schlemper, J., Caballero, J., Price, A. N., Hajnal, J. V., & Rueckert, D. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2863670 · Cited by 606

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Xu, S., Krumm, P., Lingg, A., Qi, H., Hammernik, K., & Küstner, T. (2023) · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · doi:10.58530/2022/0953

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