Evidence map›Paper›PMID 28060414›Full record

ArticleMedical physics2017

Standardizing CT lung density measure across scanner manufacturers.

Huaiyu Heather Chen-Mayer, Matthew K Fuld, Bernice Hoppel, Philip F Judy, Jered P Sieren, Junfeng Guo, David A Lynch, Antonio Possolo, Sean B Fain

Open access · greenAbstract read
In one paragraph

Article in Medical physics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed, 1 pooled it
4.5field-weighted citation impact, top 5% 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

43 citing papers in PubMed, 1 synthesis or guideline pooled it, 66 citations in OpenAlex.

  1. Pooled it
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  4. Observational
  5. Local fragility index and Radiomics-lite modeling of CT-Derived Hounsfield units for osteoporosis risk assessment in spine surgery patients.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2026
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  12. Quantitative accuracy of lung function measurement using parametric response mapping: A virtual imaging study.Proceedings of SPIE--the International Society for Optical Engineering · 2024
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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

9 authors at 9 institutions in 2 countries.

Huaiyu Heather Chen-MayerRadiation Physics Division, Physical Measurements Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA.
Matthew K FuldSiemens Medical Solutions USA Inc., Malvern, PA, 19355, USA.
Bernice HoppelToshiba Medical Research Institute USA Inc., Vernon Hills, IL, 60061, USA.
Philip F JudyDepartment of Radiology, Brigham & Women's Hospital, Boston, MA, 02115, USA.
Jered P SierenVIDA Diagnostics Inc., Coralville, IA, 52241, USA.
Junfeng GuoDepartments of Radiology and Biomedical Engineering, University of Iowa, Iowa City, IA, 52242, USA.
David A LynchDepartment of Radiology, National Jewish Health, Denver, CO, 80206, USA.
Antonio PossoloStatistical Engineering Division, Information Technology Laboratory, National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA.
Sean B FainDepartment of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, 53705, USA.
Brigham and Women's Hospital · USNational Institute of Standards and Technology · USPhysical Measurement Laboratory · USSiemens Healthcare (United States) · USToshiba (United States) · USUniversity of Colorado Denver · USUniversity of Iowa · USUniversity of Wisconsin–Madison · USVida Diagnostics (United States) · US

Funding

Intramural NIST DOC 9999-NISTNIBIB NIH HHS HHSN268201300071C
6 · The paper itself

Abstract

purposeComputed Tomography (CT) imaging of the lung, reported in Hounsfield Units (HU), can be parameterized as a quantitative image biomarker for the diagnosis and monitoring of lung density changes due to emphysema, a type of chronic obstructive pulmonary disease (COPD). CT lung density metrics are global measurements based on lung CT number histograms, and are typically a quantity specifying either the percentage of voxels with CT numbers below a threshold, or a single CT number below which a fixed relative lung volume, nth percentile, falls. To reduce variability in the density metrics specified by CT attenuation, the Quantitative Imaging Biomarkers Alliance (QIBA) Lung Density Committee has organized efforts to conduct phantom studies in a variety of scanner models to establish a baseline for assessing the variations in patient studies that can be attributed to scanner calibration and measurement uncertainty.

methodsData were obtained from a phantom study on CT scanners from four manufacturers with several protocols at various tube potential voltage (kVp) and exposure settings. Free from biological variation, these phantom studies provide an assessment of the accuracy and precision of the density metrics across platforms solely due to machine calibration and uncertainty of the reference materials. The phantom used in this study has three foam density references in the lung density region, which, after calibration against a suite of Standard Reference Materials (SRM) foams with certified physical density, establishes a HU-electron density relationship for each machine-protocol. We devised a 5-step calibration procedure combined with a simplified physical model that enabled the standardization of the CT numbers reported across a total of 22 scanner-protocol settings to a single energy (chosen at 80 keV). A standard deviation was calculated for overall CT numbers for each density, as well as by scanner and other variables, as a measure of the variability, before and after the standardization. In addition, a linear mixed-effects model was used to assess the heterogeneity across scanners, and the 95% confidence interval of the mean CT number was evaluated before and after the standardization.

resultsWe show that after applying the standardization procedures to the phantom data, the instrumental reproducibility of the CT density measurement of the reference foams improved by more than 65%, as measured by the standard deviation of the overall mean CT number. Using the lung foam that did not participate in the calibration as a test case, a mixed effects model analysis shows that the 95% confidence intervals are [-862.0 HU, -851.3 HU] before standardization, and [-859.0 HU, -853.7 HU] after standardization to 80 keV. This is in general agreement with the expected CT number value at 80 keV of -855.9 HU with 95% CI of [-857.4 HU, -854.5 HU] based on the calibration and the uncertainty in the SRM certified density.

conclusionsThis study provides a quantitative assessment of the variations expected in CT lung density measures attributed to non-biological sources such as scanner calibration and scanner x-ray spectrum and filtration. By removing scanner-protocol dependence from the measured CT numbers, higher accuracy and reproducibility of quantitative CT measures were attainable. The standardization procedures developed in study may be explored for possible application in CT lung density clinical data.

Indexed as

CalibrationHumansLungModels, AnatomicModels, TheoreticalPhantoms, ImagingReproducibility of ResultsTomography Scanners, X-Ray ComputedTomography, X-Ray ComputedUncertaintyCOPDCT scanner calibrationHounsfield Unit correctionlung density CTlung density reference phantomlung density SRMQuantitative Imaging Biomarker

Identifiers

PMID28060414
PMCPMC6276120
OpenAlexW2571384411

What OpenQuestion holds

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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.