Evidence map›Paper›PMID 37059839›Full record

ArticleScientific reports2023

Non-invasive mass and temperature quantifications with spectral CT.

Leening P Liu, Matthew Hwang, Matthew Hung, Michael C Soulen, Thomas P Schaer, Nadav Shapira, Peter B Noël

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.4field-weighted citation impact, top 21% 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

5 citing papers in PubMed, 15 citations in OpenAlex.

  1. Article
  2. Dual-Layer Spectral CT for in Vivo Thermometry During Thermal Ablation.Cardiovascular and interventional radiology · 2026
    Article
  3. Quantitative metal artifact reduction algorithm for spectral CT thermometry.Conference proceedings. International Conference on Image Formation in X-Ray Computed Tomography · 2024
    Article
  4. Article
  5. 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

7 authors at 2 institutions in 1 country.

Leening P LiuDepartment of Radiology, University of Pennsylvania, Philadelphia, USA. leening.liu@pennmedicine.upenn.edu.
Matthew HwangDepartment of Physics, Reed College, Portland, USA.
Matthew HungDepartment of Radiology, University of Pennsylvania, Philadelphia, USA.
Michael C SoulenDepartment of Radiology, University of Pennsylvania, Philadelphia, USA.
Thomas P SchaerDepartment of Clinical Studies, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, USA.
Nadav ShapiraDepartment of Radiology, University of Pennsylvania, Philadelphia, USA.
Peter B NoëlDepartment of Radiology, University of Pennsylvania, Philadelphia, USA.
University of Pennsylvania · USReed College · US

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
Interventional oncology clinical research specialistR50CA276008 · NCI · UNIVERSITY OF PENNSYLVANIA · PI MICHAEL C SOULEN · 2023 to 2026
$692k
NCI NIH HHS R50 CA276008NIBIB NIH HHS R01 EB030494NIH HHS R01EB030494
6 · The paper itself

Abstract

Spectral CT has been increasingly implemented clinically for its better characterization and quantification of materials through its multi-energy results. It also facilitates calculation of physical density, allowing for non-invasive mass measurements and temperature evaluations by manipulating the definition of physical density and thermal volumetric expansion, respectively. To develop spectral physical density quantifications, original and parametrized Alvarez-Macovski model and electron density-physical density model were validated with a phantom. The best physical density model was then implemented on clinical spectral CT scans of ex vivo bovine muscle to determine the accuracy and effect of acquisition parameters on mass measurements. In addition, the relationship between physical density and changes in temperature was evaluated by scanning and subjecting the tissue to a range of temperatures. The parametrized Alvarez-Macovski model performed best in both model development and validation with errors within ± 0.02 g/mL. No effect from acquisition parameters was observed in mass measurements, which demonstrated accuracy with a maximum percent error of 0.34%. Furthermore, physical density was strongly correlated (R of 0.9781) to temperature changes through thermal volumetric expansion. Accurate and precise spectral physical density quantifications enable non-invasive mass measurements for pathological detection and temperature evaluation for thermal therapy monitoring in interventional oncology.

Indexed as

Tomography, X-Ray ComputedAnimalsCattlePhantoms, ImagingTemperature

Identifiers

PMID37059839
PMCPMC10104802
OpenAlexW4365517138

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.