Cited by 406Advanced Image Processing TechniquesAdvanced MRI Techniques and ApplicationsArtificial Intelligence in Healthcare and Education

The DOI matches its Crossref record; 51 of the 55 references it cites were found in a registry or an index, and 3 are not checked yet.

Super‐resolution musculoskeletal MRI using deep learning

Open access

Chaudhari, Akshay S., Fang, Zhongnan, Kogan, Feliks, Wood, Jeff, Stevens, Kathryn J., Gibbons, Eric K., Lee, Jin Hyung, Gold, Garry E., Hargreaves, Brian A.·2018·Magnetic Resonance in Medicine

DOI 10.1002/mrm.27178PMC PMC6107420

Open this paper in CoChat — free account

Abstract

PURPOSE: To develop a super-resolution technique using convolutional neural networks for generating thin-slice knee MR images from thicker input slices, and compare this method with alternative through-plane interpolation methods. METHODS: We implemented a 3D convolutional neural network entitled DeepResolve to learn residual-based transformations between high-resolution thin-slice images and lower-resolution thick-slice images at the same center locations. DeepResolve was trained using 124 double echo in steady-state (DESS) data sets with 0.7-mm slice thickness and tested on 17 patients. Ground-truth images were compared with DeepResolve, clinically used tricubic interpolation, and Fourier interpolation methods, along with state-of-the-art single-image sparse-coding super-resolution. Comparisons were performed using structural similarity, peak SNR, and RMS error image quality metrics for a multitude of thin-slice downsampling factors. Two musculoskeletal radiologists ranked the 3 data sets and reviewed the diagnostic quality of the DeepResolve, tricubic interpolation, and ground-truth images for sharpness, contrast, artifacts, SNR, and overall diagnostic quality. Mann-Whitney U tests evaluated differences among the quantitative image metrics, reader scores, and rankings. Cohen's Kappa (κ) evaluated interreader reliability. RESULTS: DeepResolve had significantly better structural similarity, peak SNR, and RMS error than tricubic interpolation, Fourier interpolation, and sparse-coding super-resolution for all downsampling factors (p < .05, except 4 × and 8 × sparse-coding super-resolution downsampling factors). In the reader study, DeepResolve significantly outperformed (p < .01) tricubic interpolation in all image quality categories and overall image ranking. Both readers had substantial scoring agreement (κ = 0.73). CONCLUSION: DeepResolve was capable of resolving high-resolution thin-slice knee MRI from lower-resolution thicker slices, achieving superior quantitative and qualitative diagnostic performance to both conventionally used and state-of-the-art methods.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1002/mrm.27178

Cites

51 of 55 references verified0 citation errors · 1 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
Generative Adversarial Nets
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,119
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
Untitled work
(2010) · International Conference on Machine Learning · Cited by 13,995
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696

Show all 52 references

Research works printed but not linked: 4

10.1148/radiol.2523090028 · DOI
10.1016/j.measurement.2013.10.026 · DOI
HestnessJ NarangS ArdalaniN et al.Deep learning scaling is predictable empirically. arXiv:1712.00409;2017. · arXiv
ZhuY BradicJ.Breaking the curse of dimensionality in regression. arXiv:1708.00430;2017. · arXiv

Sorted by citations

An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.11.002 · Cited by 2,058
An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2018) · NORA - Norwegian Open Research Archives · Cited by 1,012
Deep learning in radiology: An overview of the concepts and a survey of the state of the art with focus on MRI
Mazurowski, M. A., Buda, M., Saha, A., & Bashir, M. R. (2019) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26534 · Cited by 554
Machine learning techniques for biomedical image segmentation: An overview of technical aspects and introduction to state‐of‐art applications
Seo, H., Badiei Khuzani, M., Vasudevan, V., Huang, C., Ren, H., Xiao, R., Jia, X., & Xing, L. (2020) · Medical Physics · doi:10.1002/mp.13649 · Cited by 300
Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians
Lin, D. J., Johnson, P. M., Knöll, F., & Lui, Y. W. (2020) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27078 · Cited by 299
Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging—State-of-the-Art and Challenges
Chen, Z., Pawar, K., Ekanayake, M. M., Pain, C. D., Zhong, S., & Egan, G. F. (2022) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-022-00721-9 · Cited by 247
Multi-Contrast Super-Resolution MRI Through a Progressive Network
Lyu, Q., Shan, H., Steber, C. R., Helis, C., Whitlow, C., Chan, M., & Wang, G. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2974858 · Cited by 213
Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods
Mali, S. A., Ibrahim, A., Woodruff, H. C., Andrearczyk, V., Müller, H., Primakov, S. P., Salahuddin, Z., Chatterjee, A., & Lambin, P. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11090842 · Cited by 228
Comparison of Objective Image Quality Metrics to Expert Radiologists’ Scoring of Diagnostic Quality of MR Images
Mason, A., Rioux, J., Clarke, S. E., Costa, A. F., Schmidt, M. H., Keough, V., Huynh, T. J., & Beyea, S. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2930338 · Cited by 222
Deep learning in generating radiology reports: A survey
Monshi, M. M. A., Poon, J., & Chung, Y. Y. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101878 · Cited by 201
Multimodal Transformer for Accelerated MR Imaging
Feng, C.-M., Yan, Y., Chen, G., Xu, Y. D., Hu, Y., Shao, L., & Fu, H. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3180228 · Cited by 190
Task Transformer Network for Joint MRI Reconstruction and Super-Resolution
Feng, C.-M., Yan, Y., Fu, H., Chen, L., & Xu, Y. D. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87231-1_30 · Cited by 176
Convolutional Neural Network in Medical Image Analysis: A Review
Kshatri, S. S., & Singh, D. (2023) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-09898-w · Cited by 191
A comprehensive review of deep learning-based single image super-resolution
Bashir, S. M. A., Wang, Y., Khan, M., & Niu, Y. (2021) · PeerJ Computer Science · doi:10.7717/peerj-cs.621 · Cited by 185
Retracted
3D convolutional neural networks for detection and severity staging of meniscus and PFJ cartilage morphological degenerative changes in osteoarthritis and anterior cruciate ligament subjects
Pedoia, V., Norman, B., Mehany, S. N., Bucknor, M. D., Link, T. M., & Majumdar, S. (2018) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26246 · Cited by 165
Applications of Deep Learning to Neuro-Imaging Techniques
Zhu, G., Jiang, B., Tong, L., Xie, Y., Zaharchuk, G., & Wintermark, M. (2019) · Frontiers in Neurology · doi:10.3389/fneur.2019.00869 · Cited by 162
Multi-contrast MRI Super-Resolution via a Multi-stage Integration Network
Feng, C.-M., Fu, H., Yuan, S., & Xu, Y. D. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87231-1_14 · Cited by 156
Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast
Iglesias, J. E., Billot, B., Balbastre, Y., Tabari, A., Conklin, J., González, R. G., Alexander, D. C., Golland, P., Edlow, B. L., & Fischl, B. R. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118206 · Cited by 159
DeepDTI: High-fidelity six-direction diffusion tensor imaging using deep learning
Tian, Q., Bilgiç, B., Fan, Q., Liao, C., Ngamsombat, C., Hu, Y., Witzel, T., Setsompop, K., Polimeni, J. R., & Huang, S. Y. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117017 · Cited by 137
Current applications and future directions of deep learning in musculoskeletal radiology
Chea, P., & Mandell, J. C. (2019) · Skeletal Radiology · doi:10.1007/s00256-019-03284-z · Cited by 134
MANTIS: Model‐Augmented Neural neTwork with Incoherent k‐space Sampling for efficient MR parameter mapping
Liu, F., Feng, L., & Kijowski, R. (2019) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27707 · Cited by 125
MRI Super-Resolution With Ensemble Learning and Complementary Priors
Lyu, Q., Shan, H., & Wang, G. (2020) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2020.2964201 · Cited by 125
Self-attention convolutional neural network for improved MR image reconstruction
Wu, Y., Ma, Y., Liu, J., Du, J., & Xing, L. (2019) · Information Sciences · doi:10.1016/j.ins.2019.03.080 · Cited by 111
Artificial Intelligence in Imaging: The Radiologist’s Role
Rubin, D. L. (2019) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2019.05.036 · Cited by 110
Super-resolution reconstruction of single anisotropic 3D MR images using residual convolutional neural network
Du, J., He, Z., Wang, L., Gholipour, A., Zhou, Z., Chen, D., & Jia, Y. (2019) · Neurocomputing · doi:10.1016/j.neucom.2018.10.102 · Cited by 112
Deep Learning Super-Resolution Reconstruction for Fast and Motion-Robust T2-weighted Prostate MRI
Bischoff, L. M., Peeters, J., Weinhold, L., Krausewitz, P., Ellinger, J., Katemann, C., Isaak, A., Weber, O. M., Kuetting, D. L. R., Attenberger, U. I., et al. (2023) · Radiology · doi:10.1148/radiol.230427 · Cited by 114
SOUP-GAN: Super-Resolution MRI Using Generative Adversarial Networks
Zhang, K., Hu, H. R., Philbrick, K. A., Conte, G. M., Sobek, J. D., Rouzrokh, P., & Erickson, B. J. (2022) · Tomography · doi:10.3390/tomography8020073 · Cited by 109
AI-driven 3D bioprinting for regenerative medicine: From bench to bedside
Zhang, Z., Zhou, X., Fang, Y., Xiong, Z., & Zhang, T. (2024) · Bioactive Materials · doi:10.1016/j.bioactmat.2024.11.021 · Cited by 126
Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century
Naqa, I. E., Haider, M. A., Giger, M. L., & Haken, R. K. T. (2020) · British Journal of Radiology · doi:10.1259/bjr.20190855 · Cited by 104
Impact of quality, type and volume of data used by deep learning models in the analysis of medical images
Luca, A. R., Ursuleanu, T. F., Gheorghe, L., Grigorovici, R., Iancu, S., Hlusneac, M., & Grigorovici, A. (2022) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.100911 · Cited by 110
Super-resolution of brain MRI images based on denoising diffusion probabilistic model
Wu, Z., Chen, X., Xie, S., Shen, J. G., & Zeng, Y. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104901 · Cited by 101
Clinical Assessment of Deep Learning–based Super-Resolution for 3D Volumetric Brain MRI
Rudie, J. D., Gleason, T., Barkovich, M. J., Wilson, D. M., Shankaranarayanan, A., Zhang, T., Wang, L., Gong, E., Zaharchuk, G., & Villanueva-Meyer, J. E. (2022) · Radiology Artificial Intelligence · doi:10.1148/ryai.210059 · Cited by 91
Medical Image Synthesis via Deep Learning
Yu, B., Wang, Y., Wang, L., Shen, D. G., & Zhou, L. (2020) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-33128-3_2 · Cited by 85
Novel deep learning-based noise reduction technique for prostate magnetic resonance imaging
Wang, X., Ma, J., Bhosale, P., Ibarra-Rovira, J. J., Qayyum, A., Sun, J., Bayram, E., & Szklaruk, J. (2021) · Abdominal Radiology · doi:10.1007/s00261-021-02964-6 · Cited by 83
Emerging Technology in Musculoskeletal MRI and CT
Kijowski, R., & Fritz, J. M. (2022) · Radiology · doi:10.1148/radiol.220634 · Cited by 83
Simultaneous NODDI and GFA parameter map generation from subsampled q‐space imaging using deep learning
Gibbons, E. K., Hodgson, K. K., Chaudhari, A. S., Richards, L. G., Majersik, J. J., Adluru, G., & DiBella, E. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27568 · Cited by 83
Artificial Intelligence–Driven Ultra-Fast Superresolution MRI
Lin, D. J., Walter, S. S., & Fritz, J. M. (2022) · Investigative Radiology · doi:10.1097/rli.0000000000000928 · Cited by 81
Deep‐learning based super‐resolution for 3D isotropic coronary MR angiography in less than a minute
Küstner, T., Muñoz, C., Psenicny, A., Bustin, A., Fuin, N., Qi, H., Neji, R., Kunze, K., Hajhosseiny, R., Prieto, C., et al. (2021) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28911 · Cited by 81
Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution
Feng, C.-M., Yan, Y., Yu, K., Xu, Y. D., Fu, H., Yang, J. J., & Shao, L. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3253557 · Cited by 85
Prospective Deployment of Deep Learning in MRI : A Framework for Important Considerations, Challenges, and Recommendations for Best Practices
Chaudhari, A. S., Sandino, C. M., Cole, E. K., Larson, D. B., Gold, G. E., Vasanawala, S. S., Lungren, M. P., Hargreaves, B. A., & Langlotz, C. P. (2020) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27331 · Cited by 84
Evaluation of deep learning reconstructed high-resolution 3D lumbar spine MRI
Sun, S., Tan, E. T., Mintz, D. N., Sahr, M. E., Endo, Y., Nguyen, J., Lebel, R. M., Carrino, J. A., & Sneag, D. B. (2022) · European Radiology · doi:10.1007/s00330-022-08708-4 · Cited by 79
Improving the Speed of MRI with Artificial Intelligence
Johnson, P. M., Recht, M. P., & Knöll, F. (2020) · Seminars in Musculoskeletal Radiology · doi:10.1055/s-0039-3400265 · Cited by 77
Artificial Intelligence in Spinal Imaging and Patient Care: A Review of Recent Advances
Lee, S., Jung, J., Mahatthanatrakul, A., & Kim, J.-S. L. (2024) · Neurospine · doi:10.14245/ns.2448388.194 · Cited by 76
The Current State of Artificial Intelligence in Medical Imaging and Nuclear Medicine
Lee, L. I. T., Kanthasamy, S., Ayyalaraju, R. S., & Ganatra, R. H. (2019) · BJR|Open · doi:10.1259/bjro.20190037 · Cited by 74
Artificial Intelligence in Musculoskeletal Imaging: Review of Current Literature, Challenges, and Trends
Hirschmann, A. C., Cyriac, J., Stieltjes, B., Kober, T., Richiardi, J., & Omoumi, P. (2019) · Seminars in Musculoskeletal Radiology · doi:10.1055/s-0039-1684024 · Cited by 76
Low-count whole-body PET with deep learning in a multicenter and externally validated study
Chaudhari, A. S., Mittra, E., Davidzon, G. A., Gulaka, P., Gandhi, H., Brown, A., Zhang, T., Srinivas, S., Gong, E., Zaharchuk, G., et al. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00497-2 · Cited by 83
Super-Resolution of Cardiac MR Cine Imaging using Conditional GANs and Unsupervised Transfer Learning
Xia, Y., Ravikumar, N., Greenwood, J. P., Neubauer, S., Petersen, S. E., & Frangi, A. F. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102037 · Cited by 74
Utility of deep learning super‐resolution in the context of osteoarthritis MRI biomarkers
Chaudhari, A. S., Stevens, K. J., Wood, J. P., Chakraborty, A. K., Gibbons, E. K., Fang, Z., Desai, A., Lee, J. H., Gold, G. E., & Hargreaves, B. A. (2019) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26872 · Cited by 70
MRI Advancements in Musculoskeletal Clinical and Research Practice
Sneag, D. B., Abel, F., Potter, H. G., Fritz, J. M., Koff, M. F., Chung, C. B., Pedoia, V., & Tan, E. T. (2023) · Radiology · doi:10.1148/radiol.230531 · Cited by 69
Rapid Knee MRI Acquisition and Analysis Techniques for Imaging Osteoarthritis
Chaudhari, A. S., Kogan, F., Pedoia, V., Majumdar, S., Gold, G. E., & Hargreaves, B. A. (2019) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26991 · Cited by 68
Impact of GAN-based lesion-focused medical image super-resolution on the robustness of radiomic features
de Farias, E. C., di Noia, C., Han, C., Sala, E., Castelli, M., & Rundo, L. (2021) · Scientific Reports · doi:10.1038/s41598-021-00898-z · Cited by 66
How AI May Transform Musculoskeletal Imaging
Guermazi, A., Omoumi, P., Tordjman, M., Fritz, J. M., Kijowski, R., Regnard, N.-E., Carrino, J. A., Kahn, C. E., Knöll, F., Rueckert, D., et al. (2024) · Radiology · doi:10.1148/radiol.230764 · Cited by 73
Using super-resolution generative adversarial network models and transfer learning to obtain high resolution digital periapical radiographs
Morán, M. B. H., Faria, M. D. B., Giraldi, G. A., Bastos, L. F., & Conci, A. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104139 · Cited by 63
Low-dose chest X-ray image super-resolution using generative adversarial nets with spectral normalization
Xu, L., Zeng, X., Huang, Z., Li, W., & Zhang, H. (2019) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2019.101600 · Cited by 62
SuperDTI: Ultrafast DTI and fiber tractography with deep learning
Li, H., Liang, Z., Zhang, C., Liu, R., Li, J., Zhang, W., Liang, D., Shen, B., Zhang, X., Ge, Y., et al. (2021) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28937 · Cited by 67
Deep learning–based acceleration of Compressed Sense MR imaging of the ankle
Foreman, S. C., Neumann, J., Han, J., Harrasser, N., Weiss, K., Peeters, J., Karampinos, D. C., Makowski, M. R., Gersing, A. S., & Woertler, K. (2022) · European Radiology · doi:10.1007/s00330-022-08919-9 · Cited by 60
Diagnostic Accuracy of Quantitative Multicontrast 5-Minute Knee MRI Using Prospective Artificial Intelligence Image Quality Enhancement
Chaudhari, A. S., Grissom, M. J., Fang, Z., Sveinsson, B., Lee, J. H., Gold, G. E., Hargreaves, B. A., & Stevens, K. J. (2020) · American Journal of Roentgenology · doi:10.2214/ajr.20.24172 · Cited by 60
A deep learning method for automatic segmentation of the bony orbit in MRI and CT images
Hamwood, J., Schmutz, B., Collins, M. J., Allenby, M. C., & Alonso‐Caneiro, D. (2021) · Scientific Reports · doi:10.1038/s41598-021-93227-3 · Cited by 59
Learning osteoarthritis imaging biomarkers from bone surface spherical encoding
Martinez, A. M., Calivá, F., Flament, I., Liu, F., Lee, J., Cao, P., Shah, R., Majumdar, S., & Pedoia, V. (2020) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28251 · Cited by 60
Artificial intelligence, machine learning and deep learning in musculoskeletal imaging: Current applications
D’Angelo, T., Caudo, D., Blandino, A., Albrecht, M. H., Vogl, T. J., Gruenewald, L. D., Gaeta, M., Yel, İ., Koch, V., Martin, S. S., et al. (2022) · Journal of Clinical Ultrasound · doi:10.1002/jcu.23321 · Cited by 59
3D Registration of pre-surgical prostate MRI and histopathology images via super-resolution volume reconstruction
Sood, R., Shao, W., Kunder, C. A., Teslovich, N. C., Wang, J. B., Soerensen, S. J. C., Madhuripan, N., Jawahar, A., Brooks, J. D., Ghanouni, P., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101957 · Cited by 55
A survey of deep learning models in medical therapeutic areas
Nogales, A., García-Tejedor, Á. J., Martín, D. M., Vara, J. S., & Rodríguez, C. A. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102020 · Cited by 59
Deep learning based high-resolution reconstruction of trabecular bone microstructures from low-resolution CT scans using GAN-CIRCLE
Guha, I., Nadeem, S. A., You, C., Zhang, X., Levy, S. M., Wang, G., Torner, J. C., & Saha, P. K. (2020) · SPIE Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging · doi:10.1117/12.2549318 · Cited by 54
Analysis of a Deep Learning-Based Superresolution Algorithm Tailored to Partial Fourier Gradient Echo Sequences of the Abdomen at 1.5 T
Afat, S., Wessling, D., Afat, C., Nickel, D. M., Arberet, S., Herrmann, J., Othman, A. E., & Gassenmaier, S. (2021) · Investigative Radiology · doi:10.1097/rli.0000000000000825 · Cited by 54
Training a neural network for Gibbs and noise removal in diffusion MRI
Muckley, M. J., Ades‐Aron, B., Papaioannou, A., Lemberskiy, G., Solomon, E., Lui, Y. W., Sodickson, D. K., Fieremans, E., Novikov, D. S., & Knöll, F. (2020) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28395 · Cited by 57
Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review
Singh, D., Monga, A., de Moura, H. L., Zhang, X., Zibetti, M. V. W., & Regatte, R. R. (2023) · Bioengineering · doi:10.3390/bioengineering10091012 · Cited by 53
Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends
Feng, L., Ma, D., & Liu, F. (2020) · NMR in Biomedicine · doi:10.1002/nbm.4416 · Cited by 55
Feasibility of an accelerated 2D-multi-contrast knee MRI protocol using deep-learning image reconstruction: a prospective intraindividual comparison with a standard MRI protocol
Herrmann, J., Keller, G., Gassenmaier, S., Nickel, D. M., Koerzdoerfer, G., Mostapha, M., Almansour, H., Afat, S., & Othman, A. E. (2022) · European Radiology · doi:10.1007/s00330-022-08753-z · Cited by 51
Deep learning-based super-resolution gradient echo imaging of the pancreas: Improvement of image quality and reduction of acquisition time
Chaika, M., Afat, S., Wessling, D., Afat, C., Nickel, D. M., Kannengießer, S., Herrmann, J., Almansour, H., Männlin, S., Othman, A. E., et al. (2022) · Diagnostic and Interventional Imaging · doi:10.1016/j.diii.2022.06.006 · Cited by 51
Deep Learning in MR Image Processing
Lee, D., Lee, J., Ko, J., Yoon, J., Ryu, K., & Nam, Y. (2019) · Investigative Magnetic Resonance Imaging · doi:10.13104/imri.2019.23.2.81 · Cited by 50
WNet: A Data-Driven Dual-Domain Denoising Model for Sparse-View Computed Tomography With a Trainable Reconstruction Layer
Cheslerean-Boghiu, T., Hofmann, F. C., Schultheiß, M., Pfeiffer, F., Pfeiffer, D., & Lasser, T. (2023) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2023.3240078 · Cited by 48
Undersampled MR image reconstruction using an enhanced recursive residual network
Bao, L., Ye, F., Cai, C., Wu, J., Zeng, K., van Zijl, P. C. M., & Chen, Z. (2019) · Journal of Magnetic Resonance · doi:10.1016/j.jmr.2019.07.020 · Cited by 48
Scan-Specific Generative Neural Network for MRI Super-Resolution Reconstruction
Sui, Y., Afacan, O., Jaimes, C. E., Gholipour, A., & Warfield, S. K. (2022) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3142610 · Cited by 45
Generative adversarial network‐based super‐resolution of diffusion‐weighted imaging: Application to tumour radiomics in breast cancer
Fan, M., Liu, Z., Xu, M., Wang, S., Zeng, T., Gao, X., & Li, L. (2020) · NMR in Biomedicine · doi:10.1002/nbm.4345 · Cited by 45
Multi-scale deformable transformer for multi-contrast knee MRI super-resolution
Zou, B., Ji, Z., Zhu, C., Dai, Y., Zhang, W., & Kui, X. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104154 · Cited by 44
The promise and limitations of artificial intelligence in musculoskeletal imaging
Debs, P., & Fayad, L. M. (2023) · Frontiers in Radiology · doi:10.3389/fradi.2023.1242902 · Cited by 46
MRI super‐resolution reconstruction for MRI‐guided adaptive radiotherapy using cascaded deep learning: In the presence of limited training data and unknown translation model
Chun, J., Zhang, H., Gach, H. M., Olberg, S., Mazur, T. R., Green, O., Kim, T., Kim, H. J., Kim, J. S., Mutic, S., et al. (2019) · Medical Physics · doi:10.1002/mp.13717 · Cited by 42
An Application of Generative Adversarial Networks for Super Resolution Medical Imaging
Sood, R., Topiwala, B., Choutagunta, K., Sood, R., & Rusu, M. (2018) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2018.00055 · Cited by 43
Recent Advances of Bioresponsive Nano-Sized Contrast Agents for Ultra-High-Field Magnetic Resonance Imaging
Hu, H. (2020) · Frontiers in Chemistry · doi:10.3389/fchem.2020.00203 · Cited by 43
Deep Learning Applications in Medical Image Analysis
Singha, A., Thakur, R. S., & Patel, T. (2021) · Wiley eBooks · doi:10.1002/9781119711278.ch11 · Cited by 47
Reverberation Noise Suppression in Ultrasound Channel Signals Using a 3D Fully Convolutional Neural Network
Brickson, L. L., Hyun, D., Jakovljevic, M., & Dahl, J. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3049307 · Cited by 42
Time-saving opportunities in knee osteoarthritis: T2 mapping and structural imaging of the knee using a single 5-min MRI scan
Eijgenraam, S. M., Chaudhari, A. S., Reijman, M., Bierma‐Zeinstra, S., Hargreaves, B. A., Runhaar, J., Heijboer, F. W. J., Gold, G. E., & Oei, E. H. G. (2019) · European Radiology · doi:10.1007/s00330-019-06542-9 · Cited by 41
Deep Generative Adversarial Networks: Applications in Musculoskeletal Imaging
Shin, Y., Yang, J., & Lee, Y. H. (2021) · Radiology Artificial Intelligence · doi:10.1148/ryai.2021200157 · Cited by 44
Autoencoder-Inspired Convolutional Network-Based Super-Resolution Method in MRI
Park, S., Gach, H. M., Kim, S., Lee, S. J., & Motai, Y. (2021) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2021.3076152 · Cited by 41
Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge
Sarasaen, C., Chatterjee, S., Breitkopf, M., Rose, G., Nürnberger, A., & Speck, O. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102196 · Cited by 40
Rapid dealiasing of undersampled, non‐Cartesian cardiac perfusion images using U‐net
Fan, L., Shen, D., Haji‐Valizadeh, H., Naresh, N. K., Carr, J., Freed, B. H., Lee, D. C., & Kim, D. J. (2020) · NMR in Biomedicine · doi:10.1002/nbm.4239 · Cited by 40
Effects of Deep Learning Reconstruction Technique in High-Resolution Non-contrast Magnetic Resonance Coronary Angiography at a 3-Tesla Machine
Yokota, Y., Takeda, C., Kidoh, M., Oda, S., Aoki, R., Ito, K., Morita, K., Haraoka, K., Yamashita, Y., Iizuka, H., et al. (2020) · Canadian Association of Radiologists Journal · doi:10.1177/0846537119900469 · Cited by 40
AI musculoskeletal clinical applications: how can AI increase my day-to-day efficiency?
Shin, Y., Kim, S. J., & Lee, Y. H. (2021) · Skeletal Radiology · doi:10.1007/s00256-021-03876-8 · Cited by 39
Multimodal and multicontrast image fusion via deep generative models
Dimitri, G. M., Spasov, S., Duggento, A., Passamonti, L., Lió, P., & Toschi, N. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.07.017 · Cited by 41
Super-resolution MRI through Deep LearningPreprint
Lyu, Q., You, C., Shan, H., & Wang, G. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.06776 · Cited by 40
Improving in vivo human cerebral cortical surface reconstruction using data-driven super-resolution
Tian, Q., Bilgiç, B., Fan, Q., Ngamsombat, C., Zaretskaya, N., Fultz, N. E., Ohringer, N. A., Chaudhari, A. S., Hu, Y., Witzel, T., et al. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa237 · Cited by 37
Deep learning-based thin-section MRI reconstruction improves tumour detection and delineation in pre- and post-treatment pituitary adenoma
Lee, D. H., Park, J. E., Nam, Y. K., Lee, J., Kim, S., Kim, Y., & Kim, H. S. (2021) · Scientific Reports · doi:10.1038/s41598-021-00558-2 · Cited by 38
Combined 5‐minute double‐echo in steady‐state with separated echoes and 2‐minute proton‐density‐weighted 2D FSE sequence for comprehensive whole‐joint knee MRI assessment
Chaudhari, A. S., Stevens, K. J., Sveinsson, B., Wood, J. P., Beaulieu, C. F., Oei, E. H. G., Rosenberg, J., Kogan, F., Alley, M. T., Gold, G. E., et al. (2018) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26582 · Cited by 37
Combined Deep Learning-based Super-Resolution and Partial Fourier Reconstruction for Gradient Echo Sequences in Abdominal MRI at 3 Tesla: Shortening Breath-Hold Time and Improving Image Sharpness and Lesion Conspicuity
Almansour, H., Herrmann, J., Gassenmaier, S., Lingg, A., Nickel, D. M., Kannengießer, S., Arberet, S., Othman, A. E., & Afat, S. (2022) · Academic Radiology · doi:10.1016/j.acra.2022.06.003 · Cited by 36
Machine learning for image reconstruction
Hammernik, K., & Knöll, F. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816176-0.00007-7 · Cited by 37
High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network With Attention and Cyclic Loss
Li, G., Lv, J., Tong, X., Wang, C., & Yang, G. (2021) · IEEE Access · doi:10.1109/access.2021.3099695 · Cited by 36
Applying Artificial Intelligence to Mitigate Effects of Patient Motion or Other Complicating Factors on Image Quality
Nguyen, X. V., Öztek, M. A., Nelakurti, D. D., Brunnquell, C. L., Mossa‐Basha, M., Haynor, D. R., & Prevedello, L. M. S. (2020) · Topics in Magnetic Resonance Imaging · doi:10.1097/rmr.0000000000000249 · Cited by 35
Rapid reconstruction of highly undersampled, non‐Cartesian real‐time cine k‐space data using a perceptual complex neural network (PCNN)
Shen, D., Ghosh, S., Haji‐Valizadeh, H., Pathrose, A., Schiffers, F., Lee, D. C., Freed, B. H., Markl, M., Cossairt, O. S., Katsaggelos, A. K., et al. (2020) · NMR in Biomedicine · doi:10.1002/nbm.4405 · Cited by 36
Super‐resolution head and neck MRA using deep machine learning
Koktzoglou, I., Huang, R. H., Ankenbrandt, W. J., Walker, M. T., & Edelman, R. R. (2021) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28738 · Cited by 35
Multicontrast MRI Super-Resolution via Transformer-Empowered Multiscale Contextual Matching and Aggregation
Lyu, J., Li, G., Wang, C., Cai, Q., Dou, Q., Zhang, D. D., & Qin, J. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3250491 · Cited by 34

Authors: Chaudhari, A. S., Fang, Z., Kogan, F., Wood, J., Stevens, K. J., Gibbons, E. K., Lee, J. H., Gold, G. E., & Hargreaves, B. A.
Affiliations: Stanford Medicine (US)
LVIS Corporation Palo Alto California
University of Utah (US)
Stanford University (US)
Corresponding author: Chaudhari, Akshay S.
ORCID: Chaudhari, Akshay S.
Fang, Zhongnan
Kogan, Feliks
Stevens, Kathryn J.
Gibbons, Eric K.
Lee, Jin Hyung
Gold, Garry E.
Hargreaves, Brian A.
Venue: Magnetic Resonance in Medicine
Publisher: Wiley
Published: 2018-03-26
Volume: 80
Issue: 5
Pages: 2139-2154
ISSN: 0740-3194 (ISSN-L)
1522-2594
DOI: 10.1002/mrm.27178
PMC: PMC6107420
OpenAlex: W2794977498
Cited by: 406
References: 52
Citations by year: 2026: 30 · 2025: 50 · 2024: 52 · 2023: 51 · 2022: 46 · 2021: 88 · 2020: 53 · 2019: 26 · 2018: 9
FWCI: 22.01
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: onlinelibrary.wiley.com/termsAndConditions#vor (vor)
Locations: Magnetic Resonance in Medicine (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: National Institutes of Health (R01 AR063643, R01 EB002524, K24 AR062068, P41 EB015891, N01‐AR‐2‐2258, N01‐AR‐2‐2259, N01‐AR‐2‐2260, N01‐AR‐2‐2261, N01‐AR‐2‐2262)
GE Healthcare
National Institute of Arthritis and Musculoskeletal and Skin Diseases (AR062068, AR063643, AR22258, AR22259, AR22260, AR22261, AR22262)
National Institute of Biomedical Imaging and Bioengineering (EB002524, EB015891)
Thomas and Stacey Siebel Foundation
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image Processing Techniques
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords: MRI super-resolution, knee MRI, musculoskeletal MRI, convolutional neural networks, Fourier interpolation, image quality, reader study, inter-reader reliability
MeSH terms: Deep Learning, Algorithms, Cartilage Diseases, Humans, Knee, Magnetic Resonance Imaging, Pilot Projects, Phantoms, Imaging, Osteoarthritis, Knee, Imaging, Three-Dimensional, Signal-To-Noise Ratio

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Super‐resolution musculoskeletal MRI using deep learning

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks