ArticleRadiological physics and technology2017
Optimizing CT technique to reduce radiation dose: effect of changes in kVp, iterative reconstruction, and noise index on dose and noise in a human cadaver.
Article in Radiological physics and technology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed.
- Reduced environmental impact in body CT imaging with deep learning reconstruction: experience of a high-volume tertiary referral center.Insights into imaging · 2026Article
- Article
- Myelography Using Energy-Integrating Detector CT Versus Photon-Counting Detector CT for Detection of CSF-Venous Fistulas in Patients With Spontaneous Intracranial Hypotension.AJR. American journal of roentgenology · 2024Article
- Direct measurement of radiation exposure dose to individual organs during diagnostic computed tomography examination.Scientific reports · 2021Article
- Body composition determinants of radiation dose during abdominopelvic CT.Insights into imaging · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
For assessment of the effect of varying the peak kilovoltage (kVp), the adaptive statistical iterative reconstruction technique (ASiR), and automatic dose modulation on radiation dose and image noise in a human cadaver, a cadaver torso underwent CT scanning at 80, 100, 120 and 140 kVp, each at ASiR settings of 0, 30 and 50 %, and noise indices (NIs) of 5.5, 11 and 22. The volume CT dose index (CTDI
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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.