ArticleIEEE transactions on bio-medical engineering2023
Synthesizing MR Image Contrast Enhancement Using 3D High-Resolution ConvNets.
Article in IEEE transactions on bio-medical engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Deep learning for synthetic contrast-enhanced CT and MRI: a scoping review.European radiology · 2026Article
- Deep learning for contrast-enhanced MRI in pediatric brain imaging.Neuroradiology · 2026Article
- Multi-Sequence Guided Generation of Contrast-Enhanced Magnetic Resonance Imaging Using Diffusion Models.Bioengineering (Basel, Switzerland) · 2026Article
- Contrast-free identification of glioma blood-brain barrier status via generative diffusion AI and non-contrast MRI.Nature communications · 2026Article
- Generative Models for Medical Image Creation and Translation: A Scoping Review.Sensors (Basel, Switzerland) · 2026Article
- Article
- Downstream evaluation of synthetic AI-generated TFrontiers in radiology · 2026Article
- Diagnostically competitive performance of a physiology-informed generative multi-task network for contrast-free CT perfusion.Frontiers in human neuroscience · 2026Article
- Recommendations on the use of gadolinium-based contrast agents in the diagnosis and monitoring of common adult intracranial tumours.European radiology · 2025Review
- Simulating dynamic tumor contrast enhancement in breast MRI using conditional generative adversarial networks.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Automated synthetic contrast-enhanced MRI improves choroid plexus segmentation in Parkinsonian syndromes.Radiology advances · 2025Article
- Enhanced brain tumor classification framework using deep learning.Scientific reports · 2025Article
- Article
- Feasibility of virtual T2-weighted fat-saturated breast MRI images by convolutional neural networks.European radiology experimental · 2025Article
- Deep learning empowered gadolinium-free contrast-enhanced abbreviated MRI for diagnosing hepatocellular carcinoma.JHEP reports : innovation in hepatology · 2025Article
- Robust deep MRI contrast synthesis using a prior-based and task-oriented 3D network.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Enhancing Ultrasound Image Quality Across Disease Domains: Application of Cycle-Consistent Generative Adversarial Network and Perceptual Loss.JMIR biomedical engineering · 2024Article
- Special Issue: Artificial Intelligence in Advanced Medical Imaging.Bioengineering (Basel, Switzerland) · 2024Article
- Multi-stage cascade GAN for synthesis of contrast enhancement CT aorta images from non-contrast CT.Scientific reports · 2024Article
- Turning brain MRI into diagnostic PET:Medical image analysis · 2024Article
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8 authors.
Funding
Abstract
objectiveGadolinium-based contrast agents (GBCAs) have been widely used to better visualize disease in brain magnetic resonance imaging (MRI). However, gadolinium deposition within the brain and body has raised safety concerns about the use of GBCAs. Therefore, the development of novel approaches that can decrease or even eliminate GBCA exposure while providing similar contrast information would be of significant use clinically.
methodsIn this work, we present a deep learning based approach for contrast-enhanced T1 synthesis on brain tumor patients. A 3D high-resolution fully convolutional network (FCN), which maintains high resolution information through processing and aggregates multi-scale information in parallel, is designed to map pre-contrast MRI sequences to contrast-enhanced MRI sequences. Specifically, three pre-contrast MRI sequences, T1, T2 and apparent diffusion coefficient map (ADC), are utilized as inputs and the post-contrast T1 sequences are utilized as target output. To alleviate the data imbalance problem between normal tissues and the tumor regions, we introduce a local loss to improve the contribution of the tumor regions, which leads to better enhancement results on tumors.
resultsExtensive quantitative and visual assessments are performed, with our proposed model achieving a PSNR of 28.24 dB in the brain and 21.2 dB in tumor regions. CONCLUSION AND SIGNIFICANCE: Our results suggest the potential of substituting GBCAs with synthetic contrast images generated via deep learning.
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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.