Evidence map›Paper›PMID 28986761›Full record

ReviewInsights into imaging2017

Detector-based spectral CT with a novel dual-layer technology: principles and applications.

Negin Rassouli, Maryam Etesami, Amar Dhanantwari, Prabhakar Rajiah

Open access · goldAbstract readReview
In one paragraph

Review in Insights into imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 151 papers.

0numbers the graph read from it
0cells of the map it votes in
151citing papers in PubMed
7.8field-weighted citation impact, top 2% of its field
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

151 citing papers in PubMed, 276 citations in OpenAlex.

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  12. Dual energy CT of pediatric genitourinary disorders.Abdominal radiology (New York) · 2026
    Review
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91 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Negin RassouliDepartment of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA.
Maryam EtesamiDepartment of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA.
Amar DhanantwariPhilips Healthcare, Cleveland, OH, USA.
Prabhakar RajiahDepartment of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA. radprabhakar@gmail.com.
University Hospitals of Cleveland · USPhilips (United States) · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Detector-based spectral computed tomography is a novel dual-energy CT technology that employs two layers of detectors to simultaneously collect low- and high-energy data in all patients using standard CT protocols. In addition to the conventional polyenergetic images created for each patient, projection-space decomposition is used to generate spectral basis images (photoelectric and Compton scatter) for creating multiple spectral images, including material decomposition (iodine-only, virtual non-contrast, effective atomic number) and virtual monoenergetic images, on-demand according to clinical need. These images are useful in multiple clinical applications, including- improving vascular contrast, improving lesion conspicuity, decreasing artefacts, material characterisation and reducing radiation dose. In this article, we discuss the principles of this novel technology and also illustrate the common clinical applications. Teaching points • The top and bottom layers of dual-layer CT absorb low- and high-energy photons, respectively.• Multiple spectral images are generated by projection-space decomposition.• Spectral images can be generated in all patients scanned in this scanner.

Indexed as

CardiacContrastCTDual-energySpectral

Identifiers

PMID28986761
PMCPMC5707218
OpenAlexW2763781088

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.