ArticleEuropean radiology2021
Deep learning-based methods may minimize GBCA dosage in brain MRI.
Article in European radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT05754476 (Multi-center and Prospective Cohort Study of Artificial Intelligence Model for Gadolinium-based Contrast Agent Reduction in Brain MRI), which is not on this map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Multi-center and Prospective Cohort Study of Artificial Intelligence Model for Gadolinium-based Contrast Agent Reduction in Brain MRI (MAGNET)
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- Rationales for Non-standard GBCA Dosing-Low?-High?-When? and Why?: A Literature-based Study.Investigative radiology · 2026Pooled it
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- Deep learning for contrast-enhanced MRI in pediatric brain imaging.Neuroradiology · 2026Article
- Predicting brain tumour enhancement from non-contrast MRI with artificial intelligence: a multicohort, retrospective, diagnostic accuracy study.The Lancet medical imaging & theranostics · 2026Article
- Artificial intelligence-amplified contrast enhancement in brain magnetic resonance imaging for improving image quality and lesion visualization: a prospective pilot study.Quantitative imaging in medicine and surgery · 2026Article
- BrainGAN Framework for Generating Synthetic Contrast-Enhanced Multisequence MRI in Brain Metastases.Journal of imaging informatics in medicine · 2026Article
- Multi-Sequence Guided Generation of Contrast-Enhanced Magnetic Resonance Imaging Using Diffusion Models.Bioengineering (Basel, Switzerland) · 2026Article
- A deep learning model to reduce agent dose for contrast-enhanced MRI of the cerebellopontine angle cistern.European radiology · 2026Article
- Smart Nanomedicines for Neurodegenerative Diseases: Empowering New Therapies with Molecular Imaging and Artificial Intelligence.Molecular diagnosis & therapy · 2026Review
- AI for image quality and patient safety in CT and MRI.European radiology experimental · 2025Review
- Diffusion probabilistic versus generative adversarial models to reduce contrast agent dose in breast MRI.European radiology experimental · 2024Article
- Review of strategies to reduce the contamination of the water environment by gadolinium-based contrast agents.Insights into imaging · 2024Review
- Contrast-enhanced MRI synthesis using dense-dilated residual convolutions based 3D network toward elimination of gadolinium in neuro-oncology.Journal of applied clinical medical physics · 2023Article
- The Quest to Reduce the Use of Gadolinium-based Contrast Agents: AI May Provide a Solution.Radiology · 2023Article
- Magnetic Resonance Imaging of Primary Adult Brain Tumors: State of the Art and Future Perspectives.Biomedicines · 2023Review
- Toward deep learning replacement of gadolinium in neuro-oncology: A review of contrast-enhanced synthetic MRI.Frontiers in neuroimaging · 2023Review
- Synthetic Post-Contrast Imaging through Artificial Intelligence: Clinical Applications of Virtual and Augmented Contrast Media.Pharmaceutics · 2022Review
- Magnetic resonance imaging contrast enhancement synthesis using cascade networks with local supervision.Medical physics · 2022Article
- Segmentation Algorithm of Magnetic Resonance Imaging Glioma under Fully Convolutional Densely Connected Convolutional Networks.Stem cells international · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
12 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo evaluate the clinical performance of a deep learning (DL)-based method for brain MRI exams with reduced gadolinium-based contrast agent (GBCA) dose to provide better understanding of the readiness and limitations of this method.
methodsEighty-three consecutive patients (from March 2019 to August 2019) who underwent brain contrast-enhanced (CE) MRI were included. Three 3D T1-weighted images with zero-dose, low-dose (10%), and full-dose (100%) GBCA were collected. The first 30 cases were used to train a DL model to synthesize the full-dose GBCA images from the zero-dose and low-dose image pairs. The remaining 53 cases were used for testing. The enhancement pattern, number, and location of enhancing lesions were recorded. Overall image quality, image signal noise ratio (SNR), lesion conspicuity, and lesion enhancement were assessed.
resultsLesion detection from the DL-synthesized CE-MRI image accurately matched those from the true full-dose CE-MRI images in 48 of 53 cases (90.6%). The DL method identified the lesions in 34 of 36 cases (94.4%) with a single enhanced lesion and all lesions in 3 of 6 cases (50.0%) in cases with multiple enhancing lesions. The agreement between synthesized and true full-dose CE-MRI images were 0.73, 0.63, 0.89, and 0.87 for image quality, image SNR, lesion conspicuity, and lesion enhancement, respectively.
conclusionsThe proposed DL method is a feasible way to minimize the dosage of GBCAs in brain MRI without sacrificing the diagnostic information. Missing enhancement of small lesions in patients with multiple lesions was observed, requiring improvements in algorithms or dosage design. KEY POINTS: • This study evaluated the clinical performance of a DL-based reconstruction method for significant dose reduction in GBCA contrast-enhanced MRI exams. • The proposed DL method has the potential to satisfy the routine radiological diagnosis needs in certain clinical applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.