Evidence map›Paper›PMID 38386967›Full record

ArticleJournal of applied clinical medical physics2024

Spectral performance evaluation of a second-generation spectral detector CT.

Leening P Liu, Nadav Shapira, Sandra S Halliburton, Sebastian Meyer, Amy Perkins, Harold I Litt, Hans Ulrich Kauczor, Tim Leiner, Wolfram Stiller, Peter B Noël

Abstract read
In one paragraph

Article in Journal of applied clinical medical physics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
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

10 citing papers in PubMed.

  1. Article
  2. Theoretical Prediction of Bias in Model-Based Material Decomposition.Proceedings of SPIE--the International Society for Optical Engineering · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Spectral performance evaluation of a second-generation spectral detector CT.Journal of applied clinical medical physics · 2024
    Article
  10. Article
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

10 authors.

Leening P LiuDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Nadav ShapiraDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Sandra S HalliburtonPhilips Healthcare, Orange Village, Ohio, USA.
Sebastian MeyerDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Amy PerkinsPhilips Healthcare, Orange Village, Ohio, USA.
Harold I LittDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Hans Ulrich KauczorDiagnostic and Interventional Radiology (DIR), Heidelberg University Hospital, Heidelberg, Germany.
Tim LeinerDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.
Wolfram StillerDiagnostic and Interventional Radiology (DIR), Heidelberg University Hospital, Heidelberg, Germany.
Peter B NoëlDepartment of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Funding

Patient-specific, high-sensitivity spectral CT for assessment of pancreatic cancerR01EB030494 · NIBIB · UNIVERSITY OF PENNSYLVANIA · PI GANG, JIANAN GRACE, NOËL, PETER B · 2021 to 2024
$2.4M
NIBIB NIH HHS R01 EB030494NIH HHS R01EB030494
6 · The paper itself

Abstract

purposeThe aim of this study was to characterize a second-generation wide-detector dual-layer spectral computed tomography (CT) system for material quantification accuracy, acquisition parameter and patient size dependencies, and tissue characterization capabilities.

methodsA phantom with multiple tissue-mimicking and material-specific inserts was scanned with a dual-layer spectral detector CT using different tube voltages, collimation widths, radiation dose levels, and size configurations. Accuracy of iodine density maps and virtual monoenergetic images (MonoE) were investigated. Additionally, differences between conventional and MonoE 70 keV images were calculated to evaluate acquisition parameter and patient size dependencies. To demonstrate material quantification and differentiation, liver-mimicking inserts with adipose and iron were analyzed with a two-base decomposition utilizing MonoE 50 and 150 keV, and root mean square error (RMSE) for adipose and iron content was reported.

resultsMeasured inserts exhibited quantitative accuracy across a wide range of MonoE levels. MonoE 70 keV images demonstrated reduced dependence compared to conventional images for phantom size (1 vs. 27 HU) and acquisition parameters, particularly tube voltage (4 vs. 37 HU). Iodine density quantification was successful with errors ranging from -0.58 to 0.44 mg/mL. Similarly, inserts with different amounts of adipose and iron were differentiated, and the small deviation in values within inserts corresponded to a RMSE of 3.49 ± 1.76% and 1.67 ± 0.84 mg/mL for adipose and iron content, respectively.

conclusionThe second-generation dual-layer CT enables acquisition of quantitatively accurate spectral data without compromises from differences in patient size and acquisition parameters.

Indexed as

IodineTomography, X-Ray ComputedHumansIronObesityPhantoms, ImagingSignal-To-Noise RatioIodineIrondual energy CTspectral CT

Identifiers

PMID38386967
PMCPMC11005977

What OpenQuestion holds

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LicenceCC BY
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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.