ArticleMedical physics2017
Standardizing CT lung density measure across scanner manufacturers.
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.
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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.
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Who cites it
43 citing papers in PubMed, 1 synthesis or guideline pooled it, 66 citations in OpenAlex.
- The Use of Computed Tomography Densitometry for the Assessment of Emphysema in Clinical Trials: A Position Paper from the Fleischner Society.American journal of respiratory and critical care medicine · 2025Pooled it
- Segmentation-based deep learning emphysema quantification using chest CT: improved accuracy and robustness vs LAA-950.European radiology · 2026Article
- Intra-vendor inter-scanner agreement of negative-HU measurements on fast kV-switching dual-energy CT virtual monoenergetic imaging: a phantom study.Radiological physics and technology · 2026Article
- Precise Lung Density Quantification with a Physics-based CT Harmonizer.Radiology. Cardiothoracic imaging · 2026Observational
- 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 · 2026Article
- The Impact of CT Reconstruction Parameters on Emphysema Index Quantification, HU-Based Measurements, and Goddard Score in COPD Assessment: A Prospective Study.International journal of biomedical imaging · 2026Article
- Harmonized low-dose computed tomographic protocols for quantitative lung imaging using dose modulation and advanced reconstructions.Medical physics · 2025Article
- Comparison of quantitative lung measures in low dose energy-integrating detector and photon-counting detector chest CT with an anthropomorphic phantom.Biomedical physics & engineering express · 2025Article
- Standardization of Lung CT Number Using COPD Gene2 Phantom Under Various Scanning Protocols.Sensors (Basel, Switzerland) · 2025Article
- Novel Quantification Protocol for Cardiovascular Calcification Progression Using Longitudinal MicroPET/MicroCT Images.Journal of visualized experiments : JoVE · 2024Article
- Correlation between CT-based phenotypes and serum biomarker in interstitial lung diseases.BMC pulmonary medicine · 2024Observational
- Quantitative accuracy of lung function measurement using parametric response mapping: A virtual imaging study.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Quantification of Emphysema Progression at CT Using Simultaneous Volume, Noise, and Bias Lung Density Correction.Radiology · 2024Article
- Quantitative CT Scan Imaging of the Airways for Diagnosis and Management of Lung Disease.Chest · 2023Review
- Observational
- Quantification of airway wall contrast enhancement on virtual monoenergetic images from spectral computed tomography.European radiology · 2023Article
- Generative Adversarial Network-Based Image Conversion Among Different Computed Tomography Protocols and Vendors: Effects on Accuracy and Variability in Quantifying Regional Disease Patterns of Interstitial Lung Disease.Korean journal of radiology · 2023Article
- Area-Detector Computed Tomography for Pulmonary Functional Imaging.Diagnostics (Basel, Switzerland) · 2023Review
- Robustness of radiomics features of virtual unenhanced and virtual monoenergetic images in dual-energy CT among different imaging platforms and potential role of CT number variability.Insights into imaging · 2023Article
- Article
Corrections and comments
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Authors and funding
9 authors at 9 institutions in 2 countries.
Funding
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.
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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.