Deep learning enables reduced gadolinium dose for contrast-enhanced brain MRI
Abstract
BACKGROUND: There are concerns over gadolinium deposition from gadolinium-based contrast agents (GBCA) administration. PURPOSE: To reduce gadolinium dose in contrast-enhanced brain MRI using a deep learning method. STUDY TYPE: Retrospective, crossover. POPULATION: Sixty patients receiving clinically indicated contrast-enhanced brain MRI. SEQUENCE: -weighted inversion-recovery prepped fast-spoiled-gradient-echo (IR-FSPGR) imaging was acquired at both 1.5T and 3T. In 60 brain MRI exams, the IR-FSPGR sequence was obtained under three conditions: precontrast, postcontrast images with 10% low-dose (0.01mmol/kg) and 100% full-dose (0.1 mmol/kg) of gadobenate dimeglumine. We trained a deep learning model using the first 10 cases (with mixed indications) to approximate full-dose images from the precontrast and low-dose images. Synthesized full-dose images were created using the trained model in two test sets: 20 patients with mixed indications and 30 patients with glioma. ASSESSMENT: For both test sets, low-dose, true full-dose, and the synthesized full-dose postcontrast image sets were compared quantitatively using peak-signal-to-noise-ratios (PSNR) and structural-similarity-index (SSIM). For the test set comprised of 20 patients with mixed indications, two neuroradiologists scored blindly and independently for the three postcontrast image sets, evaluating image quality, motion-artifact suppression, and contrast enhancement compared with precontrast images. STATISTICAL ANALYSIS: Results were assessed using paired t-tests and noninferiority tests. RESULTS: The proposed deep learning method yielded significant (n = 50, P < 0.001) improvements over the low-dose images (>5 dB PSNR gains and >11.0% SSIM). Ratings on image quality (n = 20, P = 0.003) and contrast enhancement (n = 20, P < 0.001) were significantly increased. Compared to true full-dose images, the synthesized full-dose images have a slight but not significant reduction in image quality (n = 20, P = 0.083) and contrast enhancement (n = 20, P = 0.068). Slightly better (n = 20, P = 0.039) motion-artifact suppression was noted in the synthesized images. The noninferiority test rejects the inferiority of the synthesized to true full-dose images for image quality (95% CI: -14-9%), artifacts suppression (95% CI: -5-20%), and contrast enhancement (95% CI: -13-6%). DATA CONCLUSION: With the proposed deep learning method, gadolinium dose can be reduced 10-fold while preserving contrast information and avoiding significant image quality degradation. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 5 J. MAGN. RESON. IMAGING 2018;48:330-340.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Cites
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 · 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
Loss Functions for Image Restoration With Neural Networks
Zhao, H., Gallo, O., Frosio, I., & Kautz, J. (2016) · IEEE Transactions on Computational Imaging · doi:10.1109/tci.2016.2644865 · Cited by 2,659 · 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
Image reconstruction by domain-transform manifold learning
Zhu, B., Liu, J. Z., Cauley, S. F., Rosen, B. R., & Rosen, M. S. (2018) · Nature · doi:10.1038/nature25988 · Cited by 1,694 · see citing works
Intracranial gadolinium deposition after contrast-enhanced MR imaging
McDonald, R. J., McDonald, J. S., Kallmes, D. F., Jentoft, M. E., Murray, D. L., Thielen, K. R., Williamson, E. E., & Eckel, L. J. (2015) · Radiology · doi:10.1148/radiol.15150025 · Cited by 1,358 · see citing works
Kanda, T., Ishii, K., Kawaguchi, H., Kitajima, K., & Takenaka, D. (2013) · Radiology · doi:10.1148/radiol.13131669 · Cited by 1,341 · see citing works
Kanda, T., Fukusato, T., Matsuda, M., Toyoda, K., Oba, H., Kotoku, J., Haruyama, T., Kitajima, K., & Furui, S. (2015) · Radiology · doi:10.1148/radiol.2015142690 · Cited by 877 · see citing works
Gadolinium deposition in the brain: summary of evidence and recommendations
Gulani, V., Calamante, F., Shellock, F. G., Kanal, E., & Reeder, S. B. (2017) · The Lancet Neurology · doi:10.1016/s1474-4422(17)30158-8 · Cited by 787 · see citing works
Kappos, L., Moeri, D., Radue, E. W., Schoetzau, A., Schweikert, K., Barkhof, F., Miller, D., Guttmann, C. R., Weiner, H. L., Gasperini, C., et al. (1999) · The Lancet · doi:10.1016/s0140-6736(98)03053-0 · Cited by 499 · see citing works
Burrel, M., Llovet, J. M., Ayuso, C., Iglesias, C., Sala, M., Miquel, R., de Caralt, T. M., Ayuso, J. R., Solé, M., Sánchez, M., et al. (2003) · Hepatology · doi:10.1002/hep.1840380430 · Cited by 446 · see citing works
Errante, Y., Cirimele, V., Mallio, C. A., Lazzaro, V. D., Zobel, B. B., & Quattrocchi, C. C. (2014) · Investigative Radiology · doi:10.1097/rli.0000000000000072 · Cited by 421 · see citing works
Murata, N., Gonzalez‐Cuyar, L. F., Murata, K., et al. (2016) · Investigative Radiology · doi:10.1097/rli.0000000000000252 · Cited by 408 · see citing works
Gadolinium deposition in nephrogenic fibrosing dermopathy
Boyd, A. S., Zic, J. A., & Abraham, J. L. (2006) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2006.10.048 · Cited by 374 · see citing works
McFarland, H. F., Frank, J. A., Albert, P. S., Smith, M. E., Martinꝉ, R., Harris, J. O., Patronas, N. J., Maloni, H. W., & McFarlin, D. E. (1992) · Annals of Neurology · doi:10.1002/ana.410320609 · Cited by 347 · see citing works
SimpleElastix: A User-Friendly, Multi-lingual Library for Medical Image Registration
Marstal, K., Berendsen, F. F., Staring, M., & Klein, S. (2016) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2016.78 · Cited by 341 · see citing works
McDonald, R. J., McDonald, J. S., Kallmes, D. F., Jentoft, M. E., Paolini, M. A., Murray, D. L., Williamson, E. E., & Eckel, L. J. (2017) · Radiology · doi:10.1148/radiol.2017161595 · Cited by 319 · see citing works
q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans
Golkov, V., Dosovitskiy, A., Sperl, J. I., Menzel, M. I., Czisch, M., Sämann, P. G., Brox, T., & Cremers, D. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2551324 · Cited by 316 · see citing works
Contrast-enhanced NMR imaging: animal studies using gadolinium-DTPA complex
Brasch, R. C., Weinmann, H., & Wesbey, G. E. (1984) · American Journal of Roentgenology · doi:10.2214/ajr.142.3.625 · Cited by 303 · see citing works
Gadolinium-DTPA as a contrast agent in MRI: initial clinical experience in 20 patients
Carr, D., Brown, J., Bydder, G., et al. (1984) · American Journal of Roentgenology · doi:10.2214/ajr.143.2.215 · Cited by 299 · see citing works
Semelka, R. C., Martín, D. R., Balcı, C., et al. (2001) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.1057 · Cited by 274 · see citing works
Dynamic contrast-enhanced MRI as a predictor of tumour response to radiotherapy
Zahra, M. A., Hollingsworth, K. G., Sala, E., et al. (2006) · The Lancet Oncology · doi:10.1016/s1470-2045(06)71012-9 · Cited by 272 · see citing works
Robert, P., Lehéricy, S., Grand, S., et al. (2015) · Investigative Radiology · doi:10.1097/rli.0000000000000181 · Cited by 257 · see citing works
Miller, D. H., Barkhof, F., & Nauta, J. J. P. (1993) · Brain · doi:10.1093/brain/116.5.1077 · Cited by 256 · see citing works
Ramalho, J., Castillo, M., AlObaidy, M., et al. (2015) · Radiology · doi:10.1148/radiol.2015150872 · Cited by 230 · see citing works
Deep residual learning for compressed sensing MRI
Lee, D., Yoo, J., & Ye, J. C. (2017) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2017.7950457 · Cited by 213 · see citing works
Deep neural network‐based computer‐assisted detection of cerebral aneurysms in MR angiography
Nakao, T., Hanaoka, S., Nomura, Y., Sato, I., Nemoto, M., Miki, S., Maeda, E., Yoshikawa, T., Hayashi, N., & Abe, O. (2017) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.25842 · Cited by 188 · see citing works
Khawaja, A., Cassidy, D., Shakarchi, J. A., et al. (2015) · Insights into Imaging · doi:10.1007/s13244-015-0420-2 · Cited by 180 · see citing works
Silver, N. (1997) · Brain · doi:10.1093/brain/120.7.1149 · Cited by 175 · see citing works
Deep Generative Adversarial Networks for Compressed Sensing Automates MRIPreprint
Mardani, M., Gong, E., Cheng, J. Y., Vasanawala, S. S., Zaharchuk, G., Alley, M. T., Thakur, N. H., Han, S., Dally, W. J., Pauly, J. M., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.00051 · Cited by 126 · see citing works
Bahrami, K., Shi, F., Rekik, I., & Shen, D. G. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46976-8_5 · Cited by 117 · see citing works
Boxerman, J. L., Prah, D. E., Paulson, E. S., et al. (2012) · American Journal of Neuroradiology · doi:10.3174/ajnr.a2934 · Cited by 112 · see citing works
National Institutes of Health Perspective on Reports of Gadolinium Deposition in the Brain
Malayeri, A. A., Brooks, K. M., Bryant, L. H., Evers, R. W., Kumar, P., Reich, D. S., & Bluemke, D. A. (2016) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2015.11.009 · Cited by 108 · see citing works
Tibussek, D., Rademacher, C., Caspers, J., et al. (2017) · Radiology · doi:10.1148/radiol.2017161151 · Cited by 90 · see citing works
Bjørnerud, A., Vatnehol, S. A. S., Larsson, C., et al. (2017) · Radiology · doi:10.1148/radiol.2017170391 · Cited by 74 · see citing works
Recurrent generative adversarial neural networks for compressive imaging
Mardani, M., Gong, E., Cheng, J. Y., et al. (2017) · IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing · doi:10.1109/camsap.2017.8313209 · Cited by 25 · see citing works
Deep artifact learning for compressed sensing and parallel MRIPreprint
Lee, D., Yoo, J. J., & Ye, J. C. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.01120 · Cited by 18 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Esses SJ, 2017, Automated image quality evaluation of T2‐weighted liver MRI utilizing deep learning architecture, J Magn Reson Imaging
- Çiçek Ö, 2016, International Conference on Medical Image Computing and Computer‐Assisted Intervention, 424
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
“They can also be used to reduce the gadolinium dose in contrast-enhanced brain MRI by an order of magnitude [87] without significant reduction in image quality.”
BackgroundPreparing Medical Imaging Data for Machine Learning
Willemink, M. J., Koszek, W. A., Hardell, C., Wu, J., Fleischmann, D., Harvey, H., Folio, L. R., Summers, R. M., Rubin, D. L., & Lungren, M. P. (2020) · Radiology · doi:10.1148/radiol.2020192224 · Cited by 1,020 · see citing works
“For example, a head CT might have findings supporting a diagnosis of stroke, but an MRI could definitively confirm.”
BackgroundInfluentialAn 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
Langlotz, C. P., Allen, B., Erickson, B. J., Kalpathy-Cramer, J., Bigelow, K., Cook, T. S., Flanders, A. E., Lungren, M. P., Mendelson, D. S., Rudie, J. D., et al. (2019) · Radiology · doi:10.1148/radiol.2019190613 · Cited by 368 · see citing works
Artificial Intelligence in Medicine: Where Are We Now?
Kulkarni, S., Seneviratne, N., Baig, M. S., & Khan, A. H. A. (2019) · Academic Radiology · doi:10.1016/j.acra.2019.10.001 · Cited by 289 · see citing works
A review on medical imaging synthesis using deep learning and its clinical applications
Wang, T., Lei, Y., Fu, Y., Wynne, J. F., Curran, W. J., Liu, T., & Yang, X. (2020) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.13121 · Cited by 278 · see citing works
Survey on Deep Neural Networks in Speech and Vision Systems
Alam, M., Samad, M. D., Vidyaratne, L., Glandon, A., & Iftekharuddin, K. M. (2020) · Neurocomputing · doi:10.1016/j.neucom.2020.07.053 · Cited by 257 · see citing works
“Advances in deep neural networks have shown tremendous potential in almost all areas of medical imaging such as ophthalmology [215], dental radiography [216], skin cancer imaging [217], brain imaging [218], cardiac imaging [219, 220], urology [221], lung imaging [222], stroke imaging [223], and so on.”
BackgroundThe current state of artificial intelligence in ophthalmology
Kapoor, R., Walters, S. P., & Al‐Aswad, L. A. (2018) · Survey of Ophthalmology · doi:10.1016/j.survophthal.2018.09.002 · Cited by 198 · see citing works
Gadolinium Deposition in the Brain: Current Updates
Choi, J. W., & Moon, W. (2018) · Korean Journal of Radiology · doi:10.3348/kjr.2018.0356 · Cited by 183 · see citing works
“A recent study has suggested a possibility that a deep learning method can be used to enhance the image quality of low-dose postcontrast enhanced images up to a level comparable to fulldose post-contrast enhanced images (104).”
BackgroundApplications 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
“Finally, Gong et al. demonstrated the benefits of including multi-contrast approaches (i.e., proton-density images along with ASL difference images) with multi-lateral guided filters and deep networks to boost the SNR and resolution of ASL (65).”
MethodDiagnosing of disease using machine learning
Singh, P., Singh, N., Singh, K. K., & Singh, A. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-821229-5.00003-3 · Cited by 159 · see citing works
Can Virtual Contrast Enhancement in Brain MRI Replace Gadolinium?
Kleesiek, J., Morshuis, J. N., Isensee, F., Deike‐Hofmann, K., Paech, D., Kickingereder, P., Köthe, U., Rother, C., Forsting, M., Wick, W., et al. (2019) · Investigative Radiology · doi:10.1097/rli.0000000000000583 · Cited by 149 · see citing works
“The difficulty of predicting small vessels also has been encountered by Gong and colleagues.(22) Notably, there are several decisive differences when comparing our work to their approach.”
BackgroundMethodArtificial Intelligence Applications in Stroke
Mouridsen, K., Thurner, P., & Zaharchuk, G. (2020) · Stroke · doi:10.1161/strokeaha.119.027479 · Cited by 139 · see citing works
“5 While it is difficult to significantly shorten acquisition times given the speed of modern multidetector CT scanners, CNNs can be used to reduce radiation dose.”
Zhang, Q., Burrage, M. K., Lukaschuk, E., Shanmuganathan, M., Popescu, I. A., Nikolaidou, C., Mills, R., Werys, K., Hann, E., Barutcu, A., et al. (2021) · Circulation · doi:10.1161/circulationaha.121.054432 · Cited by 136 · see citing works
An Overview of Artificial Intelligence in Oncology
Farina, E. M. J. D. M., Nabhen, J. J., Dacoregio, M. I., Batalini, F., & de Moraes, F. Y. (2022) · Future Science OA · doi:10.2144/fsoa-2021-0074 · Cited by 137 · see citing works
Conte, G. M., Weston, A. D., Vogelsang, D. C., Philbrick, K. A., Cai, J. C., Barbera, M., Sanvito, F., Lachance, D. H., Jenkins, R. B., Tobin, W. O., et al. (2021) · Radiology · doi:10.1148/radiol.2021203786 · Cited by 131 · see citing works
Clinical applications of artificial intelligence in radiology
Mello-Thoms, C. R., & Mello, C. A. B. (2023) · British Journal of Radiology · doi:10.1259/bjr.20221031 · Cited by 131 · see citing works
“66”
Liu, F., Feng, L., & Kijowski, R. (2019) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27707 · Cited by 125 · see citing works
“23,26,38 However, it would be more interesting to compare the U‐Net with other newly developed deep learning networks for the implementation of the MANTIS framework.”
BackgroundDeep Learning for Predicting Enhancing Lesions in Multiple Sclerosis from Noncontrast MRI
Narayana, P. A., Coronado, I., Sujit, S. J., Wolinsky, J. S., LUBLIN, F. D., & Gabr, R. E. (2019) · Radiology · doi:10.1148/radiol.2019191061 · Cited by 124 · see citing works
“In fact, DL has been recently used to detect enhancing lesions in MS at a reduced GBCA dose (16).”
MethodPreetha, C. J., Meredig, H., Brugnara, G., Mahmutoglu, M. A., Foltyn‐Dumitru, M., Isensee, F., Kessler, T., Pflüger, I., Schell, M., Neuberger, U., et al. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00205-3 · Cited by 129 · see citing works
Buchlak, Q. D., Esmaili, N., Leveque, J., Bennett, C. C., Farrokhi, F. R., & Piccardi, M. (2021) · Journal of Clinical Neuroscience · doi:10.1016/j.jocn.2021.04.043 · Cited by 112 · see citing works
Deep learning for fast MR imaging: A review for learning reconstruction from incomplete k-space data
Wang, S., Xiao, T., Liu, Q., & Zheng, H. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102579 · Cited by 112 · see citing works
“In addition, there is a series of works using U-Net or improved networks based on U-Net for fast MRI reconstruction studies [37], [44]–[48].”
MethodNoninterpretive Uses of Artificial Intelligence in Radiology
Richardson, M. L., Garwood, E. R., Lee, Y. Z., Li, M., Lo, H. S., Nagaraju, A., Nguyen, X. V., Probyn, L. J., Rajiah, P. S., Sin, J. M., et al. (2020) · Academic Radiology · doi:10.1016/j.acra.2020.01.012 · Cited by 105 · 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
“80 Similar efforts have been demonstrated in thoracic CT using DL to assess acceptable image quality prior to interpretation.”
BackgroundSANTIS: Sampling‐Augmented Neural neTwork with Incoherent Structure for MR image reconstruction
Liu, F., Samsonov, A. A., Chen, L., Kijowski, R., & Feng, L. (2019) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27827 · Cited by 98 · see citing works
“38,39 However, additional studies on larger image data sets are needed to more thoroughly investigate the applicability and utility of this intriguing feature of SANTIS.”
BackgroundMethodClinical 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
“While AI methods have been shown to improve image quality for a host of different structural MRI applications (7–9), it is important to determine whether they generalize beyond an individual scanner or practice setting, given that many AI algorithms have shown poorer performance when applied to…”
BackgroundMethodDanieli, L., Riccitelli, G. C., Distefano, D., Prodi, E., Ventura, E., Cianfoni, A., Kaelin‐Lang, A., Reinert, M., & Pravatà, E. (2019) · American Journal of Neuroradiology · doi:10.3174/ajnr.a6096 · Cited by 89 · see citing works
Denoising arterial spin labeling perfusion MRI with deep machine learning
Xie, D., Li, Y., Yang, H., Li, B., Wang, T., Zhou, F., Zhang, L., & Wang, Z. (2020) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2020.01.005 · Cited by 89 · see citing works
“Though Kim et al. [47] and Gong et al. [46] used Batch Normalization (BN) [57] layer in their network, recent researches [53] [58] [59] [60] found that BN undermined the accuracy for image super-resolution tasks.”
BackgroundArtificial intelligence in stroke imaging: Current and future perspectives
Yedavalli, V. S., Tong, E., Martin, D., Yeom, K. W., & Forkert, N. D. (2020) · Clinical Imaging · doi:10.1016/j.clinimag.2020.09.005 · Cited by 87 · see citing works
Magnetic resonance parameter mapping using model‐guided self‐supervised deep learning
Liu, F., Kijowski, R., Fakhri, G. E., & Feng, L. (2021) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28659 · Cited by 84 · see citing works
“5,20,41 The detailed CNN structure is shown in Supporting Information Figure S2.”
BackgroundMethodEfficacy and Safety of Gadopiclenol for Contrast-Enhanced MRI of the Central Nervous System
Loevner, L. A., Kolumbán, B., Hutóczki, G., et al. (2022) · Investigative Radiology · doi:10.1097/rli.0000000000000944 · Cited by 83 · see citing works
Artificial Intelligence Applications for Workflow, Process Optimization and Predictive Analytics
Guillon, L. L., Camirand, D., Guilbert, F., & Forghani, R. (2020) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2020.08.008 · Cited by 84 · see citing works
SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI
Tian, Q., Li, Z., Fan, Q., Polimeni, J. R., Bilgiç, B., Salat, D. H., & Huang, S. Y. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119033 · Cited by 81 · see citing works
Pierre, K., Haneberg, A. G., Kwak, S., Peters, K. R., Hochhegger, B., Sananmuang, T., Tunlayadechanont, P., Tighe, P. J., Mancuso, A., & Forghani, R. (2023) · Seminars in Roentgenology · doi:10.1053/j.ro.2023.02.003 · Cited by 79 · 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
Artificial intelligence in paediatric radiology: Future opportunities
Davendralingam, N., Sebire, N. J., Arthurs, O. J., & Shelmerdine, S. C. (2020) · British Journal of Radiology · doi:10.1259/bjr.20200975 · Cited by 77 · see citing works
“22 In one adult’s brain MRI study (including healthy volunteers and patients with gliomas), a deep learning architecture was able to generate ‘virtual contrast’ MRI images from unenhanced MRI sequences with a structural similarity index of 0.872 ( ± 0.031).”
BackgroundInfluentialSynthesizing MR Image Contrast Enhancement Using 3D High-Resolution ConvNets
Chen, C., Raymond, C., Speier, W. F., Jin, X., Cloughesy, T. F., Enzmann, D. R., Ellingson, B. M., & Arnold, C. W. (2022) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2022.3192309 · Cited by 75 · see citing works
“[6] proposed a 2D U-Net-like model to synthesize the full-dose postcontrast images from precontrast and low dose images.”
BackgroundMethodInfluentialRole of artificial intelligence in MS clinical practice
Bonacchi, R., Filippi, M., & Rocca, M. A. (2022) · NeuroImage Clinical · doi:10.1016/j.nicl.2022.103065 · Cited by 74 · see citing works
“, 2020), thus reducing or avoiding gadolinium injection (Gong et al., 2018).”
Koçak, B. (2022) · Diagnostic and Interventional Radiology · doi:10.5152/dir.2022.211297 · Cited by 71 · see citing works
Using Machine Learning to Reduce the Need for Contrast Agents in Breast MRI through Synthetic Images
Müller‐Franzes, G. A., Huck, L. C., Arasteh, S. T., et al. (2023) · Radiology · doi:10.1148/radiol.222211 · Cited by 71 · see citing works
Kiryu, S., Yasaka, K., Akai, H., Nakata, Y., Sugomori, Y., Hara, S., Seo, M., Abe, O., & Ohtomo, K. (2019) · European Radiology · doi:10.1007/s00330-019-06327-0 · Cited by 67 · see citing works
Chen, Y., Jakary, A., Avadiappan, S., Hess, C. P., & Lupo, J. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116389 · Cited by 68 · see citing works
The emerging role of artificial intelligence in multiple sclerosis imaging
Afzal, H. M. R., Luo, S., Ramadan, S. A., & Lechner‐Scott, J. (2020) · Multiple Sclerosis Journal · doi:10.1177/1352458520966298 · Cited by 69 · see citing works
“9 Besides the associated health risks, using Gd both doubles the cost of a scan and increases the scanning time.”
BackgroundMethodEnhancing head and neck tumor management with artificial intelligence: Integration and perspectives
Zhong, N., Wang, H., Huang, X., Li, Z., Cao, L., Huo, F., Liu, B., & Bu, L. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.07.002 · Cited by 71 · 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
Workflow Applications of Artificial Intelligence in Radiology and an Overview of Available Tools
Kapoor, N., Lacson, R. C., & Khorasani, R. (2020) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2020.08.016 · Cited by 66 · see citing works
A generic deep learning model for reduced gadolinium dose in contrast‐enhanced brain MRI
Pasumarthi, S., Tamir, J. I., Christensen, S., et al. (2021) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28808 · Cited by 65 · see citing works
SUSAN: segment unannotated image structure using adversarial network
Liu, F. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27627 · Cited by 63 · see citing works
Bash, S., Johnson, B., Gibbs, W. N., Zhang, T., Shankaranarayanan, A., & Tanenbaum, L. N. (2021) · Clinical Neuroradiology · doi:10.1007/s00062-021-01121-2 · Cited by 60 · see citing works
“Diagnostic imaging modalities are particularly suited to benefit with opportunities such as reduced radiation and/or contrast dose for PET [3, 4], MR [5] and CT [6].”
BackgroundChaudhari, 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
Can Deep Learning Replace Gadolinium in Neuro-Oncology?
Ammari, S., Bône, A., Balleyguier, C., Moulton, E. J., Chouzenoux, É., Volk, A., Menu, Y., Bidault, F., Nicolas, F., Robert, P., et al. (2021) · Investigative Radiology · doi:10.1097/rli.0000000000000811 · Cited by 58 · see citing works
AI for image quality and patient safety in CT and MRI
Melazzini, L., Bortolotto, C., Brizzi, L., Achilli, M. F., Basla, N., Meo, A. D. D., Gerbasi, A., Bottinelli, O. M., Bellazzi, R., & Preda, L. (2025) · European Radiology Experimental · doi:10.1186/s41747-025-00562-5 · Cited by 58 · see citing works
“2) [23].”
InfluentialWeakly-Supervised teacher-Student network for liver tumor segmentation from non-enhanced images
Zhang, D., Chen, B., Chong, J., & Li, S. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102005 · Cited by 57 · see citing works
“For instance, 2D/3DUNet-197 based methods for contrast image synthesizing or hippocampus segmentation (Gong et al., 2018; Porter et al., 2020).”
MethodWang, G., Gong, E., Banerjee, S., Martin, D., Tong, E., Choi, J., Chen, H., Wintermark, M., Pauly, J. M., & Zaharchuk, G. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2987026 · Cited by 57 · see citing works
“Concerning the network design, the generator is likely to be improved by introducing a more densely connected neural network, which has been shown useful in biomedical imaging reconstruction [58], [59].”
BackgroundLi, W., Xiao, H., Li, T., Ren, G., Lam, S., Teng, X., Liu, C., Zhang, J., Lee, F. K.-H., Au, K., et al. (2021) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2021.11.007 · Cited by 56 · see citing works
Neuroimaging in the Era of Artificial Intelligence: Current Applications
Monsour, R. (2022) · Federal Practitioner · doi:10.12788/fp.0231 · Cited by 54 · see citing works
Neuroimaging of Traumatic Brain Injury
Douglas, D., Ro, T., Toffoli, T., Krawchuk, B., Muldermans, J., Gullo, J., Dulberger, A. R., Anderson, A., Douglas, P. K., & Wintermark, M. (2018) · Medical Sciences · doi:10.3390/medsci7010002 · Cited by 54 · 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
Wong, L. M., King, A. D., Ai, Q. Y. H., Lam, W. K., Poon, D. M. C., Ma, B. B. Y., Chan, K. C. A., & Mo, F. K. F. (2020) · European Radiology · doi:10.1007/s00330-020-07451-y · Cited by 52 · see citing works
Artificial intelligence applications for pediatric oncology imaging
Daldrup-Link, H. E. (2019) · Pediatric Radiology · doi:10.1007/s00247-019-04360-1 · Cited by 51 · see citing works
“Gong and coworkers [20] used a CNN to reduce the dose of gadoliniumbased contrast agents for brain MRI scans to 10% of the original dose by reconstructing low-dose images to standard-dose images.”
MethodArtificial intelligence in multiparametric magnetic resonance imaging: A review
Li, C., Li, W., Liu, C., Zheng, H., Cai, J., & Wang, S. (2022) · Medical Physics · doi:10.1002/mp.15936 · Cited by 50 · 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
“(97) showed that full dose gadolinium-enhanced MR images can be synthesized with low dose MR images.”
BackgroundTadavarthi, Y., Makeeva, V., Wagstaff, W., Zhan, H. T., Podlasek, A., Bhatia, N. K., Heilbrun, M. E., Krupinski, E. A., Safdar, N. M., Banerjee, I., et al. (2022) · Radiology Artificial Intelligence · doi:10.1148/ryai.210114 · Cited by 50 · see citing works
“MRI does not produce ionizing radiation, but researchers have explored machine learning techniques to reduce gadolinium-based intravenous contrast agent dosage (33).”
BackgroundRadiology AI and sustainability paradox: environmental, economic, and social dimensions
Koçak, B., Ponsiglione, A., Romeo, V., Ugga, L., Huisman, M., & Cuocolo, R. (2025) · Insights into Imaging · doi:10.1186/s13244-025-01962-2 · Cited by 58 · see citing works
“AI-powered imaging sequences have demonstrated the ability to lower the required contrast dosage without compromising diagnostic accuracy, offering both clinical and environmental bene fi ts [75].”
Distortion correction of single-shot EPI enabled by deep-learning
Hu, Z., Wang, Y., Zhang, Z., Zhang, J., Zhang, H., Guo, C., Sun, Y., & Guo, H. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117170 · Cited by 48 · see citing works
“Recently, deep-learning algorithm has been applied across various MR imaging fields, such as image reconstruction, denoising, and contrast synthesis (Gong et al., 2018; Yoon et al., 2018; Zhu et al., 2018).”
Choi, J. W., Cho, Y. J., Ha, J. Y., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-00058-3 · Cited by 46 · see citing works
Diverse Applications of Artificial Intelligence in Neuroradiology
Duong, M. T., Rauschecker, A. M., & Mohan, S. (2020) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2020.07.003 · Cited by 45 · see citing works
“47 Accordingly, CNNs may reduce the gadolinium dose required to interpret enhancing lesions on brain MRI 48,49 and even radiation exposure on CT. 50,51 For nuclear medicine studies, DL algorithms are being developed to create “synthetic” amyloid positron emission tomography (PET) to reduce…”
MethodInfluentialDeep learning–based methods may minimize GBCA dosage in brain MRI
Luo, H., Zhang, T., Gong, N., Tamir, J., Pasumarthi, S., Xu, C., Duan, Y., Zhou, T., Zhou, F., Zaharchuk, G., et al. (2021) · European Radiology · doi:10.1007/s00330-021-07848-3 · Cited by 45 · see citing works
Preetha, C. J., Meredig, H., Brugnara, G., Mahmutoglu, M. A., Foltyn‐Dumitru, M., al, E., & Weller, M. (2021) · Zurich Open Repository and Archive (University of Zurich) · doi:10.5167/uzh-209425 · Cited by 45 · see citing works
Stephen, O., Maduh, U. J., & Sain, M. (2021) · Electronics · doi:10.3390/electronics11010055 · Cited by 44 · see citing works
Applications of artificial intelligence multiomics in precision oncology
Srivastava, R. (2022) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-022-04161-4 · Cited by 44 · see citing works
“These autoencoders reduce the radiation dose and use of intravenous contrast (Haubold et al. 2021; Gong et al. 2018; Katsari et al. 2021) which improve the safety of the patients.”
BackgroundHu, H. (2020) · Frontiers in Chemistry · doi:10.3389/fchem.2020.00203 · Cited by 43 · see citing works
“…imaging, which includes the following aspects: image acquisition, removing the unwanted artifacts, improving the image quality, reducing the contrast agent dose, and shortening the diagnose period (Gong et al., 2018; Shan et al., 2018; Zaharchuk et al., 2018; Codari et al., 2019; Zhu et al., 2019).”
BackgroundArtificial Intelligence in Neuroradiology: Current Status and Future Directions.
Lui, Y. W., Chang, P., Zaharchuk, G., Barboriak, D. P., Flanders, A. E., Wintermark, M., Hess, C. P., & Filippi, C. G. (2020) · PubMed Central · doi:10.3174/ajnr.a6681 · Cited by 47 · see citing works
Mallio, C. A., Radbruch, A., Deike‐Hofmann, K., van der Molen, A. J., Dekkers, I. A., Zaharchuk, G., Parizel, P. M., Zobel, B. B., & Quattrocchi, C. C. (2023) · Investigative Radiology · doi:10.1097/rli.0000000000000983 · Cited by 44 · see citing works
Cabitza, F., Campagner, A., & Sconfienza, L. M. (2021) · Health Information Science and Systems · doi:10.1007/s13755-021-00138-8 · Cited by 44 · see citing works
Artificial intelligence in oncologic imaging
Chen, M., Terzic, A., Becker, A. S., Johnson, J. M., Wu, C. C., Wintermark, M., Wald, C., & Wu, J. (2022) · European Journal of Radiology Open · doi:10.1016/j.ejro.2022.100441 · Cited by 41 · see citing works
“Gong et al. reported up to 90% contrast media reduction with preserved image quality using deep learning models [12].”
Sherwani, M. K., & Gopalakrishnan, S. (2024) · Frontiers in Radiology · doi:10.3389/fradi.2024.1385742 · Cited by 47 · see citing works
Kaye, E., Aherne, E. A., Düzgöl, C., Häggström, I., Kobler, E., Mazaheri, Y., Fung, M., Zhang, Z., Otazo, R., Vargas, H. A., et al. (2020) · Radiology Artificial Intelligence · doi:10.1148/ryai.2020200007 · Cited by 39 · see citing works
“9 %)** Age(y)* 64 [11] 68 [12] 64 [12] Weight (kg)* 81 [16] 79 [28] 85 [17] Indication for MRI exam Prostate cancer 75 11 30 Elevated PSA 24 4 6 Other 4 0 1”
Deep learning model for predicting gestational age after the first trimester using fetal MRI
Kojita, Y., Matsuo, H., Kanda, T., Nishio, M., Sofue, K., Nogami, M., Kono, A. K., Hori, M., & Murakami, T. (2021) · European Radiology · doi:10.1007/s00330-021-07915-9 · Cited by 39 · see citing works
Xie, Y., & Li, Q. (2022) · Electronics · doi:10.3390/electronics11040586 · Cited by 38 · see citing works
Accelerating GluCEST imaging using deep learning for B0 correction
Li, Y., Xie, D., Cember, A., Nanga, R. P. R., Yang, H., Kumar, D., Hariharan, H., Li, B., Detre, J. A., Reddy, R. D., et al. (2020) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28289 · Cited by 38 · see citing works
Xie, H., Lei, Y., Wang, T., Roper, J. R., Axente, M., Bradley, J. D., Liu, T., & Yang, X. (2022) · Medical Physics · doi:10.1002/mp.15578 · Cited by 37 · see citing works
Gadolinium Retention and Breast MRI Screening: More Harm Than Good?
Sardanelli, F., Cozzi, A., Trimboli, R. M., et al. (2019) · American Journal of Roentgenology · doi:10.2214/ajr.19.21988 · 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
“Becauseneuralnetworksusedinmedicalimagingcancontainmanylayers Becauseneuralnetworksusedinmedicalimagingcancontainmanylayers,adeepnetworkmaycontainmillionsoflearnedparameters.”
Current applications of deep-learning in neuro-oncological MRI
Zegers, C. M. L., Posch, J., Traverso, A., Eekers, D. B. P., Postma, A. A., Backes, W. H., Dekker, A. L. A. J., & van Elmpt, W. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.03.003 · Cited by 35 · see citing works
“This ranged from tasks regarding the reduction of contrast agents in the image acquisition [7], the filtering of artefacts and spectral fitting in MRSI [8,9], the separation of brain from non-brain tissue [10], the generation of synthetic Computed Tomography (CT) images for radiotherapy treatment…”
MethodResultXiong, X., Ding, Y., Sun, C., et al. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3190293 · Cited by 34 · see citing works
“To reduce the use of gadolinium-based contrast agents, Gong et al. [20] developed an encoder–decoder model to synthesize full-contrast MRI from pre-contrast MRI and low contrast-enhanced MRI.”
MethodPasquini, L., Napolitano, A., Pignatelli, M., Tagliente, E., Parrillo, C., Nasta, F., Romano, A., Bozzao, A., & Napoli, A. D. (2022) · Pharmaceutics · doi:10.3390/pharmaceutics14112378 · Cited by 34 · see citing works
“A CNN can be considered as a simplified version of the neocognitron model introduced by Fukushima [26] in 1980 to simulate the human visual system.”
BackgroundMethodInfluentialArtificial Intelligence for Neuroimaging in Pediatric Cancer
da Rocha, J. L. D., Lai, J., Pandey, P., Myat, P. S. M., Loschinskey, Z., Bag, A. K., & Sitaram, R. (2025) · Cancers · doi:10.3390/cancers17040622 · Cited by 35 · see citing works
“For example, deep learning based on an encoder–decoder CNN has been applied to generate high-quality post-contrast MRI from pre-contrast MRI and low-dose post-contrast MRI [79].”
Reducing Contrast Agent Dose in Cardiovascular MR Angiography with Deep Learning
Montalt‐Tordera, J., Quail, M. A., Steeden, J. A., et al. (2021) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.27573 · Cited by 33 · see citing works
“It has been shown that a deep learning postprocessing step can recover high-dose (HD) CE-MRA characteristics from LD brain MRA images.(11) This study aims to investigate whether a similar approach can be used to regain HD features from LD-MRA of the great vessels in CHD.”
BackgroundMethodResultInfluentialHaase, R., Pinetz, T., Bendella, Z., et al. (2023) · Investigative Radiology · doi:10.1097/rli.0000000000000955 · Cited by 33 · see citing works
Rovira, À., Doniselli, F. M., Auger, C., Haider, L., Hodel, J., Severino, M., Wattjes, M. P., van der Molen, A. J., Jasperse, B., Mallio, C. A., et al. (2023) · European Radiology · doi:10.1007/s00330-023-10151-y · Cited by 34 · see citing works
“Recent studies have also suggested that synthesized images that approximate full contrast dose images from pre-contrast and low-dose images (generated with deep learning methods) could help to further reduce GBCA doses, without significant image quality degradation [56]).”
The role of AI in prostate MRI quality and interpretation: Opportunities and challenges
Kim, H., Kang, S. W., Kim, J., Nagar, H., Sabuncu, M. R., Margolis, D., & Kim, C. K. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.110887 · Cited by 32 · see citing works
Pharmacokinetics, Safety, and Efficacy of Gadopiclenol in Pediatric Patients Aged 2 to 17 Years
Jurkiewicz, E., Tsvetkova, S., Grinberg, A., et al. (2022) · Investigative Radiology · doi:10.1097/rli.0000000000000865 · Cited by 32 · see citing works
“In this regard, a deep learning–based technology that could reduce the GBCA dose while maintaining image quality and contrast information of full-dose contrast images has been proposed.(4) Furthermore, developing newGBCAs allowing the reduction of injected Gd dose while keeping the same efficacy provided by the curren…”
BackgroundGadolinium Deposition Safety: Seeking the Patient’s Perspective
Mallio, C. A., Quattrocchi, C. C., Rovira, À., & Parizel, P. M. (2020) · American Journal of Neuroradiology · doi:10.3174/ajnr.a6586 · Cited by 31 · see citing works
“A6586 944 Mallio Jun 2020 www.ajnr.org diffusion-weighted imaging,28 showed the absence of tissue changes in the visually hyperintense dentate nuclei on unenhanced T1weighted images after cumulative doses of GBCA.”
BackgroundInfluentialDeveloping and deploying deep learning models in brain magnetic resonance imaging: A review
Aggarwal, K., Jimeno, M. M., Ravi, K. S., González, R. G., & Geethanath, S. (2023) · NMR in Biomedicine · doi:10.1002/nbm.5014 · Cited by 31 · see citing works
“110,111 115 incorporate a conditional Generative Adversarial Network (cGAN) 116 into the compressed sensing MRI framework (CS-MRI).”
BackgroundArtificial intelligence-powered nanomedicine
Luo, G., 蒋显超, Hu, C., Li, L., Yan, L., Xiao, G., Duo, Y., & Zhang, X. (2026) · Chemical Society Reviews · doi:10.1039/d5cs01406a · Cited by 33 · see citing works
Bône, A., Ammari, S., Lamarque, J.-P., et al. (2021) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi48211.2021.9434029 · Cited by 30 · see citing works
“Four performance metrics are considered: in addition to the “global” L2 loss which is directly optimized, we also compute the tumoraveraged L2 (t-L2) based on segmentation maps of enhancing lesions, as well as the peak signal-to-noise ratio (PSNR) and the structural similarity index (SSIM, see [14]) as in [8, 9].”
BackgroundMethodInfluentialReducing Gadolinium Contrast With Artificial Intelligence
Tsui, B. Q., Calabrese, E., Zaharchuk, G., & Rauschecker, A. M. (2023) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.29095 · Cited by 29 · see citing works
“24,25 Similarly, bias in training data is an important consideration for any machine learning model.”
Data‐driven synthetic MRI FLAIR artifact correction via deep neural network
Ryu, K., Nam, Y., Gho, S., Jang, J., Lee, H., Cha, J., Baek, H. J., Park, J., & Kim, D. (2019) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26712 · Cited by 29 · see citing works
“21 Considering the current outcomes of deep neural networks on these applications, the approach may be applicable for correcting the artifacts of synthetic FLAIR images.”
Method
- Authors
- Gong, E., Pauly, J. M., Wintermark, M., & Zaharchuk, G.
- Affiliations
- Stanford Medicine (US)
- Stanford University (US)
- Corresponding author
- Gong, Enhao
- Venue
- Journal of Magnetic Resonance Imaging
- Publisher
- Wiley
- Published
- 2018-02-13
- Volume
- 48
- Issue
- 2
- Pages
- 330-340
- ISSN
- 1053-1807 (ISSN-L)
- 1522-2586
- OpenAlex
- W2789565832
- Cited by
- 351
- References
- 37
- Citations by year
- 2026: 27 · 2025: 42 · 2024: 52 · 2023: 42 · 2022: 53 · 2021: 53 · 2020: 47 · 2019: 26 · 2018: 9
- FWCI
- 23.32
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Magnetic Resonance Imaging (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Materials Science › Materials Chemistry › Lanthanide and Transition Metal Complexes
- Keywords
- gadolinium-based contrast agents, gadobenate dimeglumine, deep learning image synthesis, gadolinium deposition, glioma, image quality assessment, motion artifact reduction, non-inferiority
- MeSH terms
- Machine Learning, Deep Learning, Adult, Aged, Brain, Brain Neoplasms, Contrast Media, Female, Gadolinium, Glioma, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Motion, Artifacts
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
