Evidence map›Paper›PMID 37672849›Full record

ArticleClinical imaging2023

Virtual monoenergetic imaging in photon-counting CT of the head and neck.

Faraz Farhadi, Pooyan Sahbaee, Jayasai R Rajagopal, Moozhan Nikpanah, Babak Saboury, Ralf Gutjahr, Nadia M Biassou, Ritu Shah, Thomas G Flohr, Ehsan Samei and 4 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.2field-weighted citation impact, top 23% 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

6 citing papers in PubMed, 11 citations in OpenAlex.

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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

14 authors at 4 institutions in 2 countries.

Faraz FarhadiRadiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA.
Pooyan SahbaeeSiemens Medical Solutions USA, Malvern, PA, USA.
Jayasai R RajagopalRadiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA; Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, NC, USA.
Moozhan NikpanahRadiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA.
Babak SabouryRadiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA.
Ralf GutjahrSiemens Healthineers, Forchheim, Germany.
Nadia M BiassouRadiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA.
Ritu ShahRadiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA.
Thomas G FlohrSiemens Healthineers, Forchheim, Germany.
Ehsan SameiCarl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, Durham, NC, USA.
William F PritchardRadiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA; Center for Interventional Oncology, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA.
Ashkan A MalayeriRadiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA.
David A BluemkeDepartment of Radiology, University of Wisconsin, Madison, WI, USA.
Elizabeth C JonesRadiology and Imaging Sciences, Clinical Center, National Institutes of Health, Bethesda, MD, USA. Electronic address: ejones@cc.nih.gov.
National Institutes of Health Clinical Center · USDuke Medical Center · USSiemens Healthcare (Germany) · DEUniversity of Wisconsin–Madison · US

Funding

Translational Development of Photon-Counting CT ImagingZIACL040017 · CLC · CLINICAL CENTER · PI JONES, ELIZABETH C. · 2020 to 2022
$0k
Intramural NIH HHS ZIA CL040017
6 · The paper itself

Abstract

purposeAdvantages of virtual monoenergetic images (VMI) have been reported for dual energy CT of the head and neck, and more recently VMIs derived from photon-counting (PCCT) angiography of the head and neck. We report image quality metrics of VMI in a PCCT angiography dataset, expanding the anatomical regions evaluated and extending observer-based qualitative methods further than previously reported.

methodsIn a prospective study, asymptomatic subjects underwent contrast enhanced PCCT of the head and neck using an investigational scanner. Image sets of low, high, and full spectrum (Threshold-1) energies; linear mix of low and high energies (Mix); and 23 VMIs (40-150 keV, 5 keV increments) were generated. In 8 anatomical locations, SNR and radiologists' preferences for VMI energy levels were measured using a forced-choice rank method (4 observers) and ratings of image quality using visual grading characteristic (VGC) analysis (2 observers) comparing VMI to Mix and Threshold-1 images.

resultsFifteen subjects were included (7 men, 8 women, mean 57 years, range 46-75). Among all VMIs, SNRs varied by anatomic location. The highest SNRs were observed in VMIs. Radiologists preferred 50-60 keV VMIs for vascular structures and 75-85 keV for all other structures. Cumulative ratings of image quality averaged across all locations were higher for VMIs with areas under the curve of VMI vs Mix and VMI vs Threshold-1 of 0.67 and 0.68 for the first reader and 0.72 and 0.76 for the second, respectively.

conclusionPreferred keV level and quality ratings of VMI compared to mixed and Threshold-1 images varied by anatomical location.

Indexed as

HeadNeckAngiographyFemaleHumansMaleProspective StudiesTomography, X-Ray ComputedHead and neck CTPhoton counting CTVirtual monoenergetic imagingVisual grading characteristic

Identifiers

PMID37672849
PMCPMC10838526
OpenAlexW4386087409

What OpenQuestion holds

Textmetadata
LicenceTDM
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.