Artificial Contrast: Deep Learning for Reducing Gadolinium-Based Contrast Agents in Neuroradiology.
Journal
Investigative radiology
ISSN: 1536-0210
Titre abrégé: Invest Radiol
Pays: United States
ID NLM: 0045377
Informations de publication
Date de publication:
01 08 2023
01 08 2023
Historique:
medline:
7
7
2023
pubmed:
24
2
2023
entrez:
23
2
2023
Statut:
ppublish
Résumé
Deep learning approaches are playing an ever-increasing role throughout diagnostic medicine, especially in neuroradiology, to solve a wide range of problems such as segmentation, synthesis of missing sequences, and image quality improvement. Of particular interest is their application in the reduction of gadolinium-based contrast agents, the administration of which has been under cautious reevaluation in recent years because of concerns about gadolinium deposition and its unclear long-term consequences. A growing number of studies are investigating the reduction (low-dose approach) or even complete substitution (zero-dose approach) of gadolinium-based contrast agents in diverse patient populations using a variety of deep learning methods. This work aims to highlight selected research and discusses the advantages and limitations of recent deep learning approaches, the challenges of assessing its output, and the progress toward clinical applicability distinguishing between the low-dose and zero-dose approach.
Identifiants
pubmed: 36822654
doi: 10.1097/RLI.0000000000000963
pii: 00004424-202308000-00003
doi:
Substances chimiques
Contrast Media
0
Gadolinium
AU0V1LM3JT
Radiopharmaceuticals
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
539-547Informations de copyright
Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.
Déclaration de conflit d'intérêts
Conflicts of interest and sources of funding: R.H. is funded by a research grant (BONFOR; O-194.0002.1) of the Medical Faculty of the University of Bonn. A.E. and T.P. are funded by the German Research Foundation under Germany's Excellence Strategy (EXC-2047/1, 390685813 and EXC-2151, 390873048). All authors are cofounders of relios.vision.
Références
Runge VM, Ai T, Hao D, et al. The developmental history of the gadolinium chelates as intravenous contrast media for magnetic resonance. Invest Radiol . 2011;46:807–816.
Fraum TJ, Ludwig DR, Bashir MR, et al. Gadolinium-based contrast agents: a comprehensive risk assessment. J Magn Reson Imaging . 2017;46:338–353.
Runge VM. Safety of the gadolinium-based contrast agents for magnetic resonance imaging, focusing in part on their accumulation in the brain and especially the dentate nucleus. Invest Radiol . 2016;51:273–279.
Idée JM, Port M, Raynal I, et al. Clinical and biological consequences of transmetallation induced by contrast agents for magnetic resonance imaging: a review. Fundam Clin Pharmacol . 2006;20:563–576.
Frenzel T, Lengsfeld P, Schirmer H, et al. Stability of gadolinium-based magnetic resonance imaging contrast agents in human serum at 37 degrees C. Invest Radiol . 2008;43:817–828.
Port M, Idée JM, Medina C, et al. Efficiency, thermodynamic and kinetic stability of marketed gadolinium chelates and their possible clinical consequences: a critical review. Biometals . 2008;21:469–490.
European Medicines Agency. Assessment Report for Gadolinium-Containing Contrast Agents . London: European Medicines Agency; 2010. https://www.ema.europa.eu/en/documents/referral/gadolinium-h-31-1097-assessment-report_en.pdf . Accessed October 27, 2022.
Idée JM, Fretellier N, Robic C, et al. The role of gadolinium chelates in the mechanism of nephrogenic systemic fibrosis: a critical update. Crit Rev Toxicol . 2014;44:895–913.
Grobner T. Gadolinium—a specific trigger for the development of nephrogenic fibrosing dermopathy and nephrogenic systemic fibrosis? Nephrol Dial Transplant . 2006;21:1104–1108.
Marckmann P, Skov L, Rossen K, et al. Nephrogenic systemic fibrosis: suspected causative role of gadodiamide used for contrast-enhanced magnetic resonance imaging. J Am Soc Nephrol . 2006;17:2359–2362.
Martin DR, Krishnamoorthy SK, Kalb B, et al. Decreased incidence of NSF in patients on dialysis after changing gadolinium contrast-enhanced MRI protocols. J Magn Reson Imaging . 2010;31:440–446.
White GW, Gibby WA, Tweedle MF. Comparison of Gd(DTPA-BMA) (Omniscan) versus Gd(HP-DO3A) (ProHance) relative to gadolinium retention in human bone tissue by inductively coupled plasma mass spectroscopy. Invest Radiol . 2006;41:272–278.
Kanda T, Ishii K, Kawaguchi H, et al. High signal intensity in the dentate nucleus and globus pallidus on unenhanced T1-weighted MR images: relationship with increasing cumulative dose of a gadolinium-based contrast material. Radiology . 2013;270:834–841.
Errante Y, Cirimele V, Mallio CA, et al. Progressive increase of T1 signal intensity of the dentate nucleus on unenhanced magnetic resonance images is associated with cumulative doses of intravenously administered gadodiamide in patients with normal renal function, suggesting dechelation. Invest Radiol . 2014;49:685–690.
McDonald RJ, McDonald JS, Dai D, et al. Comparison of gadolinium concentrations within multiple rat organs after intravenous administration of linear versus macrocyclic gadolinium chelates. Radiology . 2017;285:536–545.
Kanda T, Osawa M, Oba H, et al. High signal intensity in dentate nucleus on unenhanced T1-weighted MR images: association with linear versus macrocyclic gadolinium chelate administration. Radiology . 2015;275:803–809.
Radbruch A, Weberling LD, Kieslich PJ, et al. Gadolinium retention in the dentate nucleus and globus pallidus is dependent on the class of contrast agent. Radiology . 2015;275:783–791.
Radbruch A, Haase R, Kieslich PJ, et al. No signal intensity increase in the dentate nucleus on unenhanced T1-weighted MR images after more than 20 serial injections of macrocyclic gadolinium-based contrast agents. Radiology . 2016;282:699–707.
European Medicines Agency. EMA's final Opinion Confirms Restrictions on Use of Linear Gadolinium Agents in Body Scans . London: European Medicines Agency; 2017. https://www.ema.europa.eu/en/documents/referral/gadolinium-article-31-referral-emas-final-opinion-confirms-restrictions-use-linear-gadolinium-agents_en.pdf . Accessed October 26, 2022.
Shahid I, Joseph A, Lancelot E. Use of real-life safety data from international pharmacovigilance databases to assess the importance of symptoms associated with gadolinium exposure. Invest Radiol . 2022;57:664–673.
Grand View Research. MRI contrast media agents market size, share & trends analysis report by product (paramagnetic agents, superparamagnetic agents), by type, by application, by end-use, by region, and segment forecasts, 2022–2030. 2022. https://www.grandviewresearch.com/industry-analysis/mri-contrast-media-agents-market-report . Accessed November 2, 2022.
Trapasso G, Chiesa S, Freitas R, et al. What do we know about the ecotoxicological implications of the rare earth element gadolinium in aquatic ecosystems? Sci Total Environ . 2021;781:146273.
Wattjes MP, Ciccarelli O, Reich DS, et al. 2021 MAGNIMS-CMSC-NAIMS consensus recommendations on the use of MRI in patients with multiple sclerosis. Lancet Neurol . 2021;20:653–670.
Robic C, Port M, Rousseaux O, et al. Physicochemical and pharmacokinetic profiles of gadopiclenol: a new macrocyclic gadolinium chelate with high T1 relaxivity. Invest Radiol . 2019;54:475.
Lohrke J, Berger M, Frenzel T, et al. Preclinical profile of gadoquatrane: a novel tetrameric, macrocyclic high relaxivity gadolinium-based contrast agent. Invest Radiol . 2022;57:629–638.
Jurkiewicz E, Tsvetkova S, Grinberg A, et al. Pharmacokinetics, safety, and efficacy of gadopiclenol in pediatric patients aged 2 to 17 years. Invest Radiol . 2022;57:510–516.
Tweedle MF. Alternatives to gadolinium-based contrast agents. Invest Radiol . 2021;56:35–41.
Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. Med Image Comput Comput Assist Interv . 2015;9351:234–241.
Havaei M, Davy A, Warde-Farley D, et al. Brain tumor segmentation with deep neural networks. Med Image Anal . 2017;35:18–31.
Gabr RE, Coronado I, Robinson M, et al. Brain and lesion segmentation in multiple sclerosis using fully convolutional neural networks: a large-scale study. Mult Scler . 2020;26:1217–1226.
Sharma A, Member S, Hamarneh G, et al. Missing MRI pulse sequence synthesis using multi-modal generative adversarial network. IEEE Trans Med Imaging . 2019;39:1170–1183.
Bône A, Ammari S, Menu Y, et al. From dose reduction to contrast maximization: can deep learning amplify the impact of contrast media on brain magnetic resonance image quality? A reader study. Invest Radiol . 2022;57:527–535.
Chen Y, Shi F, Christodoulou AG, et al. Efficient and accurate MRI super-resolution using a generative adversarial network and 3D multi-level densely connected network. Med Image Comput Comput Assist Interv . 2018;11070 LNCS:91–99.
Pham CH, Ducournau A, Fablet R, et al. Brain MRI super-resolution using deep 3D convolutional networks. Proc IEEE Int Symp Biomed Imaging . 2017;197–200.
Hammernik K, Klatzer T, Kobler E, et al. Learning a variational network for reconstruction of accelerated MRI data. Magn Reson Med . 2018;79:3055–3071.
Narnhofer D, Effland A, Kobler E, et al. Bayesian uncertainty estimation of learned variational MRI reconstruction. IEEE Trans Med Imaging . 2022;41:279–291.
Lundervold AS, Lundervold A. An overview of deep learning in medical imaging focusing on MRI. Z Med Phys . 2019;29:102–127.
Liu Z, Lin Y, Cao Y, et al. SWIN transformer: hierarchical vision transformer using shifted windows. Proc IEEE Int Conf Comput Vis . 2021;10012–10022.
Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16×16 words: transformers for image recognition at scale. In: International Conference on Learning Representations (ICLR) . 2021:1–21. https://openreview.net/forum?id=YicbFdNTTy . Accessed November 28, 2022.
Han K, Wang Y, Chen H, et al. A survey on vision transformer . IEEE Trans Pattern Anal Mach Intell; 2022. https://ieeexplore.ieee.org/document/9716741 .
Song Y, Ermon S. Generative modeling by estimating gradients of the data distribution. Adv Neural Inf Process Syst . 2019;32.
Ho J, Jain A, Abbeel P. Denoising diffusion probabilistic models. Adv Neural Inf Process Syst . 2020;33:6840–6851.
Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. Adv Neural Inf Process Syst . 2017;30.
Brown TB, Mann B, Ryder N, et al. Language models are few-shot learners. Adv Neural Inf Process Syst . 2020;33:1877–1901.
Valanarasu JMJ, Oza P, Hacihaliloglu I, et al. Medical transformer: gated axial-attention for medical image segmentation. Med Image Comput Comput Assist Interv . 2021;12901 LNCS:36–46.
Chen J, He Y, Frey EC, et al. ViT-V-net: vision transformer for unsupervised volumetric medical image registration. Med Imaging With Deep Learn . 2021.
Hatamizadeh A, Tang Y, Nath V, et al. UNETR: transformers for 3D medical image segmentation. IEEE Winter Conf Appl Comput Vis . 2022;574–584.
Xie H, Lei Y, Wang T, et al. Magnetic resonance imaging contrast enhancement synthesis using cascade networks with local supervision. Med Phys . 2022;49:3278–3287.
Chen C, Raymond C, Speier W, et al. Synthesizing MR image contrast enhancement using 3D high-resolution ConvNets. IEEE Trans Biomed Eng . 2021.
Jayachandran Preetha C, Meredig H, Brugnara G, et al. Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study. Lancet Digit Health . 2021;3:e784–e794.
Liu J, Pasumarthi S, Duffy B, et al. One model to synthesize them all: multi-contrast multi-scale transformer for missing data imputation. ArXiv . 2022. https://arxiv.org/abs/2204.13738 . Accessed on November 28, 2022.
Wang Z, Bovik AC, Sheikh HR, et al. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process . 2004;13:600–612.
Zhang R, Isola P, Efros AA, et al. The unreasonable effectiveness of deep features as a perceptual metric. In: Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit . IEEE Computer Society; 2018:586–595.
Baid U, Ghodasara S, Mohan S, et al. The RSNA-ASNR-MICCAI BraTS 2021 benchmark on brain tumor segmentation and radiogenomic classification. ArXiv . 2021. abs/2107.02314.
Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern . 1979;SMC-9:62–66.
Ammari S, Bône A, Balleyguier C, et al. Can deep learning replace gadolinium in neuro-oncology?: a reader study. Invest Radiol . 2022;57:99–107.
Kleesiek J, Morshuis JN, Isensee F, et al. Can virtual contrast enhancement in brain MRI replace gadolinium?: a feasibility study. Invest Radiol . 2019;54:653–660.
Johnson J, Alahi A, Fei-Fei L. Perceptual losses for real-time style transfer and super-resolution. Comput Vis ECCV . 2016;9906:694–711.
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (ICLR) . 2015:1–14.
Deng J, Dong W, Socher R, et al. Imagenet: a large-scale hierarchical image database. In: Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit . IEEE; 2009:248–255.
Gong E, Pauly JM, Wintermark M, et al. Deep learning enables reduced gadolinium dose for contrast-enhanced brain MRI. J Magn Reson Imaging . 2018;48:330–340.
Luo H, Zhang T, Gong NJ, et al. Deep learning–based methods may minimize GBCA dosage in brain MRI. Eur Radiol . 2021;31:6419–6428.
Pasumarthi S, Tamir JI, Christensen S, et al. A generic deep learning model for reduced gadolinium dose in contrast-enhanced brain MRI. Magn Reson Med . 2021;86:1687–1700.
Liu BP, Rosenberg M, Saverio P, et al. Clinical efficacy of reduced-dose gadobutrol versus standard-dose gadoterate for contrast-enhanced MRI of the CNS: an international multicenter prospective crossover trial (LEADER-75). AJR Am J Roentgenol . 2021;217:1195–1205.
Nyúl LG, Udupa JK, Zhang X. New variants of a method of MRI scale standardization. IEEE Trans Med Imaging . 2000;19:143–150.
Isola P, Zhu J-Y, Zhou T, et al. Image-to-image translation with conditional adversarial networks. Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit (CVPR) . 2017;5967–5976.
Kingma DP, Ba J. Adam: A Method for Stochastic Optimization. In: International Conference on Learning Representations (ICLR) . 2015:1–15.
Boxerman JL, Prah DE, Paulson ES, et al. The role of preload and leakage correction in gadolinium-based cerebral blood volume estimation determined by comparison with MION as a criterion standard. AJNR Am J Neuroradiol . 2012;33:1081.
Calabrese E, Rudie JD, Rauschecker AM, et al. Feasibility of simulated postcontrast MRI of glioblastomas and lower-grade gliomas by using three-dimensional fully convolutional neural networks. Radiol Artif Intell . 2021;3:e200276.
Reddi SJ, Kale S, Kumar S. On the convergence of Adam and beyond. In: International Conference on Learning Representations (ICLR) . 2018:1–23.
Wang J, Sun K, Cheng T, et al. Deep high-resolution representation learning for visual recognition. IEEE Trans Pattern Anal Mach Intell . 2020;43:3349–3364.
Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial networks. Commun ACM . 2020;63:139–144.
Goodfellow IJ, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets. Adv Neural Inf Process Syst . 2014;27.
Armanious K, Jiang C, Fischer M, et al. MedGAN: medical image translation using GANs. Comput Med Imaging Graph . 2020;79:101684.
Chartsias A, Joyce T, Giuffrida MV, et al. Multimodal MR synthesis via modality-invariant latent representation. IEEE Trans Med Imaging . 2018;37:803–814.
Park N, Anand A, Moniz JRA, et al. MMGAN: manifold-matching generative adversarial networks. Int Conf Pattern Recognit . 2018;1343–1348.
Gui J, Sun Z, Wen Y, et al. A review on generative adversarial networks: algorithms, theory, and applications. IEEE Trans Knowl Data Eng . 2021.
Kazeminia S, Baur C, Kuijper A, et al. GANs for medical image analysis. Artif Intell Med . 2020;109:101938.
Hyndman RJ, Athanasopoulos G. Forecasting: Principles and Practice . Melbourne, Australia: OTexts; 2018:79.
Narayana PA, Coronado I, Sujit SJ, et al. Deep learning for predicting enhancing lesions in multiple sclerosis from noncontrast MRI. Radiology . 2020;294:398.