Super‐resolution musculoskeletal MRI using deep learning
Open accessAbstract
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
Cites
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?
Deep learning approaches have been introduced for MRI super-resolution.
“Recently deep learning approaches has been introduced, e.g. generating super-resolution single (no reference information) and multi-contrast (applying a high-resolution image of another modality as reference) brain MR images using CNNs [197]; constructing superresolution brain MRI by a CNN stacked by multi-scale fusion units [198]; and super-resolution musculoskeletal MRI ('DeepResolve') [199].”
From An overview of deep learning in medical imaging focusing on MRI (2019) · cites this paper as [199]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
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 · see citing works
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · see citing works
The Measurement of Observer Agreement for Categorical Data
Landis, J. R., & Koch, G. G. (1977) · Biometrics · doi:10.2307/2529310 · Cited by 81,148 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
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 · see citing works
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 · see citing works
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,100 · see citing works
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 · see citing works
(2010) · International Conference on Machine Learning · Cited by 13,995 · see citing works
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 · see citing works
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891 · see citing works
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 · see citing works
3D Slicer as an image computing platform for the Quantitative Imaging Network
Fedorov, A., Beichel, R., Kalpathy–Cramer, J., Finet, J., Fillion‐Robin, J., Pujol, S., Bauer, C., Jennings, D. L., Fennessy, F., Milan Sonka, A., et al. (2012) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2012.05.001 · Cited by 9,095 · see citing works
Weighted kappa: nominal scale agreement provision for scaled disagreement or partial credit
Cohen, J. (1968) · Psychological Bulletin · doi:10.1037/h0026256 · Cited by 8,736 · see citing works
Enhanced Deep Residual Networks for Single Image Super-Resolution
Lim, B. O., Son, S., Kim, H., Nah, S., & Lee, K. M. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.151 · Cited by 8,117 · see citing works
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
Kim, J., Lee, J. K., & Lee, K. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.182 · Cited by 7,870 · see citing works
Learning a Deep Convolutional Network for Image Super-Resolution
Dong, C., Loy, C. C., He, K., & Tang, X. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10593-2_13 · Cited by 5,827 · see citing works
Super-resolution image reconstruction: a technical overview
Park, S. C., Park, M., & Kang, M. G. (2003) · IEEE Signal Processing Magazine · doi:10.1109/msp.2003.1203207 · Cited by 3,325 · see citing works
Advances in Neural Information Processing Systems 37
Touretzky, D. S. (2024) · doi:10.52202/079017 · Cited by 2,874 · see citing works
OsiriX: An Open-Source Software for Navigating in Multidimensional DICOM Images
Rosset, A., Spadola, L., & Ratib, O. M. (2004) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-004-1014-6 · Cited by 1,771 · see citing works
Learning a variational network for reconstruction of accelerated MRI data
Hammernik, K., Klatzer, T., Kobler, E., Recht, M. P., Sodickson, D. K., Pock, T., & Knöll, F. (2017) · Magnetic Resonance in Medicine · doi:10.1002/mrm.26977 · Cited by 1,721 · see citing works
Deep Networks for Image Super-Resolution with Sparse Prior
Wang, Z., Liu, D., Yang, J., Han, W., & Huang, T. S. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.50 · Cited by 807 · see citing works
PETERFY, C. G., Schneider, E., & Nevitt, M. C. (2008) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2008.06.016 · Cited by 685 · see citing works
A system for grading articular cartilage lesions at arthroscopy
Noyes, F. R., & Stabler, C. L. (1989) · The American Journal of Sports Medicine · doi:10.1177/036354658901700410 · Cited by 577 · see citing works
Osteo-arthrosis and Disk Degeneration in an Urban Population
Kellgren, J., & Lawrence, J. S. (1958) · Annals of the Rheumatic Diseases · doi:10.1136/ard.17.4.388 · Cited by 471 · see citing works
Busse, R. F., Hariharan, H., Vu, A. T., & Brittain, J. H. (2006) · Magnetic Resonance in Medicine · doi:10.1002/mrm.20863 · Cited by 335 · see citing works
Supporting user-oriented analysis for multi-view domain-specific visual languages
Guerra, E., Lara, J. D., Malizia, A., & Dı́az, P. (2008) · Information and Software Technology · doi:10.1016/j.infsof.2008.09.005 · Cited by 322 · see citing works
Günther, J., Pilarski, P. M., Helfrich, G., Shen, H., & Diepold, K. J. (2014) · Procedia Technology · doi:10.1016/j.protcy.2014.09.007 · Cited by 302 · see citing works
Deep learning with domain adaptation for accelerated projection‐reconstruction MR
Han, Y., Yoo, J., Kim, H. H., Shin, H. J., Sung, K., & Ye, J. C. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27106 · Cited by 294 · see citing works
Plenge, E., Poot, D. H. J., Bernsen, M. R., Kotek, G., Houston, J. G., Wielopolski, P. A., van der Weerd, L., Niessen, W. J., & Meijering, E. H. W. (2012) · Magnetic Resonance in Medicine · doi:10.1002/mrm.24187 · Cited by 273 · see citing works
Super‐resolution in magnetic resonance imaging: A review
Reeth, É. V., Tham, I. W. K., Tan, C. H., & Poh, C. L. (2012) · Concepts in Magnetic Resonance Part A · doi:10.1002/cmr.a.21249 · Cited by 270 · see citing works
Robust Single Image Super-Resolution via Deep Networks With Sparse Prior
Liu, D., Wang, Z., Wen, B., Yang, J., Han, W., & Huang, T. S. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2564643 · Cited by 265 · see citing works
Kijowski, R., Davis, K. W., Woods, M. A., Lindstrom, M. J., Smet, A. A. D., Gold, G. E., & Busse, R. F. (2009) · Radiology · doi:10.1148/radiol.2523090028 · Cited by 256 · see citing works
Green, B., Horel, T., & Papachristos, A. V. (2017) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2016.8245 · Cited by 228 · see citing works
Du, Y. P., Parker, D. L., Davis, W. L., & Cao, G. (1994) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.1880040517 · Cited by 216 · see citing works
Bernstein, M. A., Fain, S. B., & Riederer, S. J. (2001) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.1183 · Cited by 197 · see citing works
Gold, G. E., Busse, R. F., Beehler, C., Han, E. T., Brau, A. C. S., Beatty, P. J., & Beaulieu, C. F. (2007) · American Journal of Roentgenology · doi:10.2214/ajr.06.1208 · Cited by 191 · see citing works
Water diffusion, T2, and compartmentation in frog sciatic nerve
Peled, S., Cory, D. G., Raymond, S. A., Kirschner, D. A., & Jolesz, F. A. (1999) · Magnetic Resonance in Medicine · doi:10.1002/(sici)1522-2594(199911)42:5 · Cited by 189 · see citing works
Revising the evolutionary computation abstraction
Lehman, J., & Stanley, K. O. (2010) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/1830483.1830503 · Cited by 182 · see citing works
Ankle: Isotropic MR Imaging with 3D-FSE-Cube—Initial Experience in Healthy Volunteers
Stevens, K. J., Busse, R. F., Han, E. T., Brau, A. C. S., Beatty, P. J., Beaulieu, C. F., & Gold, G. E. (2008) · Radiology · doi:10.1148/radiol.2493080227 · Cited by 141 · see citing works
Interpolation of diffusion weighted imaging datasets
Dyrby, T. B., Lundell, H., Burke, M., Reislev, N. L., Paulson, O. B., Ptito, M., & Siebner, H. R. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.09.005 · Cited by 135 · see citing works
Routine 3D magnetic resonance imaging of joints
Kijowski, R., & Gold, G. E. (2011) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.22342 · Cited by 112 · see citing works
Three-Dimensional CAIPIRINHA SPACE TSE for 5-Minute High-Resolution MRI of the Knee
Fritz, J. M., Fritz, B., Thawait, G. K., Meyer, H., Gilson, W. D., & Raithel, E. (2016) · Investigative Radiology · doi:10.1097/rli.0000000000000287 · Cited by 106 · see citing works
Duc, S. R., Pfirrmann, C. W. A., Koch, P. P., Zanetti, M., & Hodler, J. (2008) · Radiology · doi:10.1148/radiol.2462062092 · Cited by 101 · see citing works
Kijowski, R., Blankenbaker, D. G., Klaers, J. L., Shinki, K., De Smet, A. A., & Block, W. F. (2009) · Radiology · doi:10.1148/radiol.2511081133 · Cited by 101 · see citing works
Sparse representation-based MRI super-resolution reconstruction
Wang, Y., Qiao, J., Li, J., Fu, P., Chu, S., & Roddick, J. F. (2013) · Measurement · doi:10.1016/j.measurement.2013.10.026 · Cited by 85 · see citing works
Knee imaging: Rapid three‐dimensional fast spin‐echo using compressed sensing
Kijowski, R., Rosas, H. G., Samsonov, A. A., King, K., Peters, R., & Liu, F. (2016) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.25507 · Cited by 79 · see citing works
Chaudhari, A. S., Black, M. S., Eijgenraam, S. M., Wirth, W., Maschek, S., Sveinsson, B., Eckstein, F., Oei, E. H. G., Gold, G. E., & Hargreaves, B. A. (2017) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.25883 · Cited by 69 · see citing works
A simple analytic method for estimating T2 in the knee from DESS
Sveinsson, B., Chaudhari, A. S., Gold, G. E., & Hargreaves, B. A. (2016) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2016.12.018 · Cited by 65 · see citing works
Context-Sensitive Super-Resolution for Fast Fetal Magnetic Resonance Imaging
McDonagh, S., Hou, B., Alansary, A., Oktay, O., Kamnitsas, K., Rutherford, M., Hajnal, J. V., & Kainz, B. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67564-0_12 · Cited by 48 · see citing works
Chaudhari, A. S., Sveinsson, B., Moran, C. J., McWalter, E. J., Johnson, E., Zhang, T., Gold, G. E., & Hargreaves, B. A. (2017) · Magnetic Resonance in Medicine · doi:10.1002/mrm.26577 · Cited by 45 · see citing works
Isotropic reconstruction of a 4‐D MRI thoracic sequence using super‐resolution
Reeth, É. V., Tan, C. H., Tham, I. W. K., & Poh, C. L. (2014) · Magnetic Resonance in Medicine · doi:10.1002/mrm.25157 · Cited by 31 · see citing works
Simultaneous bilateral‐knee MR imaging
Kogan, F., Levine, E. G., Chaudhari, A. S., Monu, U., Epperson, K. S., Oei, E. H. G., Gold, G. E., & Hargreaves, B. A. (2017) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27045 · Cited by 25 · see citing works
Fast comprehensive single‐sequence four‐dimensional pediatric knee MRI with T2 shuffling
Bao, S., Tamir, J. I., Young, J. L., Tariq, U., Uecker, M., Lai, P., Chen, W., Lustig, M. S., & Vasanawala, S. S. (2016) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.25508 · Cited by 19 · see citing works
Ota, J., Umehara, K., Ishimaru, N., & Ishida, T. (2017) · Open Journal of Medical Imaging · doi:10.4236/ojmi.2017.74014 · Cited by 3 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- [7]Kijowski R Davis KW Woods MA Lindstrom MJ De Smet AA Gold GE Busse RF Knee joint: comprehensive assessment with 3D isotropic resolution fast spin-echo MR imaging–diagnostic performance compared with that of conventional MR imaging at 3.0 T Radiology 2009 252 486 495 10.1148/radiol.2523090028 19703886 PMC6939958 · DOI
- [20]Plenge E Poot DHJ Bernsen M Kotek G Houston G Wielopolski P van der Weerd L Niessen WJ Meijering E Super-resolution methods in MRI: Can they improve the trade-off between resolution, signal-to-noise ratio, and acquisition time? Magn Reson Med 2012 68 1983 1993 22298247
Cited by
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 · see citing works
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 · see citing works
Mazurowski, M. A., Buda, M., Saha, A., & Bashir, M. R. (2019) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26534 · Cited by 554 · see citing works
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 · see citing works
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 295 · see citing works
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 238 · see citing works
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 · see citing works
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 218 · see citing works
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 206 · see citing works
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 194 · see citing works
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 · see citing works
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 · see citing works
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 176 · see citing works
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 174 · see citing works
RetractedPedoia, 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 162 · see citing works
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 · see citing works
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 · see citing works
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 146 · see citing works
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 · see citing works
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 132 · see citing works
Liu, F., Feng, L., & Kijowski, R. (2019) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27707 · Cited by 125 · see citing works
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 · see citing works
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 110 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 121 · see citing works
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 101 · see citing works
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 99 · see citing works
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 92 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Emerging Technology in Musculoskeletal MRI and CT
Kijowski, R., & Fritz, J. M. (2022) · Radiology · doi:10.1148/radiol.220634 · Cited by 83 · see citing works
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 82 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 79 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 72 · see citing works
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 72 · see citing works
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 · see citing works
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 69 · see citing works
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 · see citing works
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 67 · see citing works
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 67 · see citing works
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 · see citing works
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 67 · see citing works
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 · see citing works
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 · see citing works
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 62 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 57 · see citing works
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 56 · see citing works
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 · see citing works
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 57 · see citing works
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 · see citing works
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 · see citing works
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 54 · see citing works
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 · see citing works
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 52 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 44 · see citing works
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 · see citing works
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 · see citing works
Hu, H. (2020) · Frontiers in Chemistry · doi:10.3389/fchem.2020.00203 · Cited by 43 · see citing works
Deep Learning Applications in Medical Image Analysis
Singha, A., Thakur, R. S., & Patel, T. (2021) · Biomedical Data Mining for Information Retrieval · doi:10.1002/9781119711278.ch11 · Cited by 44 · see citing works
Brickson, L. L., Hyun, D., Jakovljevic, M., & Dahl, J. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3049307 · Cited by 42 · see citing works
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 · see citing works
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 41 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
AI musculoskeletal clinical applications: how can AI increase my day-to-day efficiency?
Shin, Y., Kim, S., & Lee, Y. (2021) · Skeletal Radiology · doi:10.1007/s00256-021-03876-8 · Cited by 36 · see citing works
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 40 · see citing works
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 · see citing works
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 39 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Machine learning for image reconstruction
Hammernik, K., & Knöll, F. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816176-0.00007-7 · Cited by 36 · see citing works
Li, G., Lv, J., Tong, X., Wang, C., & Yang, G. (2021) · IEEE Access · doi:10.1109/access.2021.3099695 · Cited by 36 · see citing works
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 · see citing works
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 35 · see citing works
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 · see citing works
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 · see citing works
- 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
- 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
- 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
- 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
