Evidence map›Paper›PMID 30276672›Full record

ArticleEuropean radiology2019

How accurate and precise are CT based measurements of iodine concentration? A comparison of the minimum detectable concentration difference among single source and dual source dual energy CT in a phantom study.

André Euler, Justin Solomon, Maciej A Mazurowski, Ehsan Samei, Rendon C Nelson

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Article in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
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21citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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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.

2 · The registry

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3 · Its place in the literature

Who cites it

21 citing papers in PubMed.

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  13. Quantitative dual-energy CT techniques in the abdomen.Abdominal radiology (New York) · 2022
    Review
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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

André EulerDepartment of Radiology, Duke University Medical Center, 2301 Erwin Road, Box 3808, Durham, NC, 27710, USA. and.euler@gmail.com.ORCID http://orcid.org/0000-0001-5019-4585
Justin SolomonCarl E. Ravin Advanced Imaging Laboratories, Durham, NC, USA.
Maciej A MazurowskiDepartment of Radiology, Duke University Medical Center, 2301 Erwin Road, Box 3808, Durham, NC, 27710, USA.
Ehsan SameiCarl E. Ravin Advanced Imaging Laboratories, Durham, NC, USA.
Rendon C NelsonDepartment of Radiology, Duke University Medical Center, 2301 Erwin Road, Box 3808, Durham, NC, 27710, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo assess the impact of scan- and patient-related factors on the error and the minimum detectable difference in iodine concentration among different generations of single-source (SS) fast kV-switching and dual-source (DS) dual-energy CT (DECT).

methodsLesions having eight different iodine concentrations (0.2-4 mgI/mL) were emulated in a 3D-printed phantom of medium and large size. Each combination of concentration and size was scanned in dual-energy mode on four different SS and DS DECTs. Radiation doses were 7 and 10 mGy (medium size) and 10, 13, and 16 mGy (large size). Iodine maps were reconstructed with filtered back projection (FBP) and vendor-specific iterative reconstruction algorithms (IRs). Absolute error of iodine quantification (E) was measured. Multivariate regression models determined the influence of CT scanner, iodine concentration, phantom size, radiation dose, and reconstruction algorithm on E. The minimum detectable difference in iodine concentration (IC

resultsThe error was significantly lower in current than in previous DECT generations (p < 0.001). For all CT scanner conditions, the error was significantly higher with increasing phantom size and decreasing radiation dose (p < 0.001). Iodine concentration only significantly affected the error for SS DECT (p < 0.001). IC

conclusionsPatient- and scan-related factors have a significant impact on the error and minimum detectable difference in iodine concentration within and among SS fast kV-switching and DS DECT. KEY POINTS: • Patient- and scan-related factors have a significant impact on the error and minimum detectable difference in dual-energy CT-based iodine quantification. • Third-generation DECTs outperformed second-generation scanners for both single-source and dual-source dual-energy CT. • The minimum intra- and inter-conditional detectable difference in iodine concentration ranged from 0.4 to 1.5 mg iodine/mL.

Indexed as

AlgorithmsPhantoms, ImagingHumansIodineMultidetector Computed TomographyReproducibility of ResultsIodineIodineMultidetector computed tomographyPhantom imaging

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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.