ReviewPharmaceutics2022
Synthetic Post-Contrast Imaging through Artificial Intelligence: Clinical Applications of Virtual and Augmented Contrast Media.
Review in Pharmaceutics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Deep learning for synthetic PET imaging: a systematic mapping review of techniques, metrics, and clinical relevance.European radiology experimental · 2026Pooled it
- Applications of deep learning to reduce the need for iodinated contrast media for CT imaging: a systematic review.International journal of computer assisted radiology and surgery · 2023Pooled it
- Abbreviated non-contrast magnetic resonance enterography for Crohn's disease: a reliability study.La Radiologia medica · 2026Article
- Artificial intelligence in paediatric neuroradiology: current landscape, challenges, and future directions.Pediatric radiology · 2026Review
- BrainGAN Framework for Generating Synthetic Contrast-Enhanced Multisequence MRI in Brain Metastases.Journal of imaging informatics in medicine · 2026Article
- [Fe-EOB-tCDTA] generates strong contrast in the blood but not in the liver, despite inhibiting cellular [Gd-EOB-DTPA]European radiology experimental · 2026Article
- Simulating dynamic tumor contrast enhancement in breast MRI using conditional generative adversarial networks.Journal of medical imaging (Bellingham, Wash.) · 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
- Clinical feasibility of deep learning based synthetic contrast enhanced abdominal CT in patients undergoing non enhanced CT scans.Scientific reports · 2024Article
- Evolving and Novel Applications of Artificial Intelligence in Thoracic Imaging.Diagnostics (Basel, Switzerland) · 2024Review
- Revolutionising paediatric radiology: the future impact of artificial intelligence.European radiology · 2024Article
- Review of strategies to reduce the contamination of the water environment by gadolinium-based contrast agents.Insights into imaging · 2024Review
- Review
- Artificial Intelligence Applications in Cardiovascular Magnetic Resonance Imaging: Are We on the Path to Avoiding the Administration of Contrast Media?Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Contrast media are widely diffused in biomedical imaging, due to their relevance in the diagnosis of numerous disorders. However, the risk of adverse reactions, the concern of potential damage to sensitive organs, and the recently described brain deposition of gadolinium salts, limit the use of contrast media in clinical practice. In recent years, the application of artificial intelligence (AI) techniques to biomedical imaging has led to the development of 'virtual' and 'augmented' contrasts. The idea behind these applications is to generate synthetic post-contrast images through AI computational modeling starting from the information available on other images acquired during the same scan. In these AI models, non-contrast images (virtual contrast) or low-dose post-contrast images (augmented contrast) are used as input data to generate synthetic post-contrast images, which are often undistinguishable from the native ones. In this review, we discuss the most recent advances of AI applications to biomedical imaging relative to synthetic contrast media.
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Identifiers
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.