SynthesisInternational journal of computer assisted radiology and surgery2023
Applications of deep learning to reduce the need for iodinated contrast media for CT imaging: a systematic review.
Synthesis in International journal of computer assisted radiology and surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Shortage of iodinated contrast media: Status and possible chances - A systematic review.European journal of radiology · 2023Pooled it
- Deep learning for synthetic contrast-enhanced CT and MRI: a scoping review.European radiology · 2026Article
- Abbreviated non-contrast magnetic resonance enterography for Crohn's disease: a reliability study.La Radiologia medica · 2026Article
- AI-based denoising improves image quality in HCC volume perfusion CT without affecting Milan classification.BMC medical imaging · 2026Article
- Generating Synthetic Data for Medical Imaging.Radiology · 2024Review
- Global research hotspots and trends of iodinated contrast agents in medical imaging: a bibliometric and visualization analysis.Frontiers in medicine · 2024Article
- Clinical validation of enhanced CT imaging for distal radius fractures through conditional Generative Adversarial Networks (cGAN).PloS one · 2024Article
- Why mild contrast medium-induced reactions are sometimes over-treated and moderate/severe reactions of internal organs are undertreated: a summary based on RadioComics.Insights into imaging · 2023Article
- The role of iodinated contrast media in computed tomography structured Reporting and Data Systems (RADS): a narrative review.Quantitative imaging in medicine and surgery · 2023Review
- 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
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe usage of iodinated contrast media (ICM) can improve the sensitivity and specificity of computed tomography (CT) for many clinical indications. However, the adverse effects of ICM administration can include renal injury, life-threatening allergic-like reactions, and environmental contamination. Deep learning (DL) models can generate full-dose ICM CT images from non-contrast or low-dose ICM administration or generate non-contrast CT from full-dose ICM CT. Eliminating the need for both contrast-enhanced and non-enhanced imaging or reducing the amount of required contrast while maintaining diagnostic capability may reduce overall patient risk, improve efficiency and minimize costs. We reviewed the current capabilities of DL to reduce the need for contrast administration in CT.
methodsWe conducted a systematic review of articles utilizing DL to reduce the amount of ICM required in CT, searching MEDLINE, Embase, Compendex, Inspec, and Scopus to identify papers published from 2016 to 2022. We classified the articles based on the DL model and ICM reduction.
resultsEighteen papers met the inclusion criteria for analysis. Of these, ten generated synthetic full-dose (100%) ICM from real non-contrast CT, while four augmented low-dose to full-dose ICM CT. Three used DL to create synthetic non-contrast CT from real 100% ICM CT, while one paper used DL to translate the 100% ICM to non-contrast CT and vice versa. DL models commonly used generative adversarial networks trained and tested by paired contrast-enhanced and non-contrast or low ICM CTs. Image quality metrics such as peak signal-to-noise ratio and structural similarity index were frequently used for comparing synthetic versus real CT image quality.
conclusionDL-generated contrast-enhanced or non-contrast CT may assist in diagnosis and radiation therapy planning; however, further work to optimize protocols to reduce or eliminate ICM for specific pathology is still needed along with a dedicated assessment of the clinical utility of these synthetic images.
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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 Socratic graph.