ArticleRadiology2023
Using Machine Learning to Reduce the Need for Contrast Agents in Breast MRI through Synthetic Images.
Article in Radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed, 64 citations in OpenAlex.
- Deep learning for synthetic contrast-enhanced CT and MRI: a scoping review.European radiology · 2026Article
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- Deep learning-based generation of synthetic multiphasic MRI in hepatocellular carcinoma and cirrhosis.JHEP reports : innovation in hepatology · 2026Article
- How to optimise breast cancer staging with contrast-enhanced mammography: current evidence and clinical implications.Insights into imaging · 2026Review
- A deep learning model to reduce agent dose for contrast-enhanced MRI of the cerebellopontine angle cistern.European radiology · 2026Article
- XVertNet: Unsupervised Contrast Enhancement of Vertebral Structures with Dynamic Self-Tuning Guidance and Multi-Stage Analysis.Journal of imaging informatics in medicine · 2026Article
- Contrast-free identification of glioma blood-brain barrier status via generative diffusion AI and non-contrast MRI.Nature communications · 2026Article
- Dataset of artificial breast cancer MRIs produced from unpaired mammograms.Data in brief · 2026Article
- Acquisition of KQuantitative imaging in medicine and surgery · 2026Article
- Simulating dynamic tumor contrast enhancement in breast MRI using conditional generative adversarial networks.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Virtual contrast-enhanced maximum intensity projections from high-b-value diffusion-weighted breast MRI: a feasibility study.European radiology experimental · 2025Article
- Detection of cerebral cavernous malformation associated developmental venous anomalies in gradient-echo and susceptibility-weighted magnetic resonance imaging: can we skip the contrast?Neuroradiology · 2025Article
- Feasibility of virtual T2-weighted fat-saturated breast MRI images by convolutional neural networks.European radiology experimental · 2025Article
- Impact of non-contrast-enhanced imaging input sequences on the generation of virtual contrast-enhanced breast MRI scans using neural network.European radiology · 2025Article
- A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations.Scientific data · 2025Article
- Clinical Application of Artificial Intelligence in Breast MRI.Journal of the Korean Society of Radiology · 2025Review
- Accelerating breast MRI acquisition with generative AI models.European radiology · 2025Article
- Article
- AI Applications to Breast MRI: Today and Tomorrow.Journal of magnetic resonance imaging : JMRI · 2024Review
- Conceptual review of outcome metrics and measures used in clinical evaluation of artificial intelligence in radiology.La Radiologia medica · 2024Review
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Authors and funding
12 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Reducing the amount of contrast agent needed for contrast-enhanced breast MRI is desirable. Purpose To investigate if generative adversarial networks (GANs) can recover contrast-enhanced breast MRI scans from unenhanced images and virtual low-contrast-enhanced images. Materials and Methods In this retrospective study of breast MRI performed from January 2010 to December 2019, simulated low-contrast images were produced by adding virtual noise to the existing contrast-enhanced images. GANs were then trained to recover the contrast-enhanced images from the simulated low-contrast images (approach A) or from the unenhanced T1- and T2-weighted images (approach B). Two experienced radiologists were tasked with distinguishing between real and synthesized contrast-enhanced images using both approaches. Image appearance and conspicuity of enhancing lesions on the real versus synthesized contrast-enhanced images were independently compared and rated on a five-point Likert scale.
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What OpenQuestion holds
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.