ArticleChest2023
Emphysema Quantifications With CT Scan: Assessing the Effects of Acquisition Protocols and Imaging Parameters Using Virtual Imaging Trials.
Article in Chest, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- An integrated PET/CT simulation framework for virtual imaging trials and quantitative performance evaluation.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2026Article
- Precise Lung Density Quantification with a Physics-based CT Harmonizer.Radiology. Cardiothoracic imaging · 2026Observational
- Virtual Imaging-Guided Harmonization of Chest X-rays Using a Generative Adversarial Neural Network.IEEE transactions on bio-medical engineering · 2026Article
- Airway quantifications of bronchitis patients with photon-counting and energy-integrating computed tomography.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Development of a customizable model for spectral photon-counting detector CT.Medical physics · 2025Article
- Reproducibility of lung density quantification across photon-counting and conventional energy-integrating CT: a comparative study.The British journal of radiology · 2025Article
- Evaluation of unified harmonization of CT images across multiple tasks: A step towards AI generalizability.Medical physics · 2025Article
- An end-to-end CT simulation framework with graphical user interface and sample scanner models.Medical physics · 2025Article
- Effects of Intra-patient Lung Volume Variability on CT-Based Emphysema Quantification: A Virtual Imaging Study.Academic radiology · 2025Article
- XCAT 3.0: A comprehensive library of personalized digital twins derived from CT scans.Medical image analysis · 2025Article
- Quantitative Accuracy of CT Protocols for Cross-sectional and Longitudinal Assessment of COPD: A Virtual Imaging Study.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- Diagnosis and Severity Assessment of COPD Based on Machine Learning of Chest CT Images.International journal of chronic obstructive pulmonary disease · 2025Article
- Toward widespread use of virtual trials in medical imaging innovation and regulatory science.Medical physics · 2024Review
- A systematic assessment and optimization of photon-counting CT for lung density quantifications.Medical physics · 2024Article
- Quantitative accuracy of lung function measurement using parametric response mapping: A virtual imaging study.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Development and Application of a Virtual Imaging Trial Framework for Longitudinal Quantification of Emphysema in CT.Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Quantitative Analysis for Lung Disease on Thin-Section CT.Diagnostics (Basel, Switzerland) · 2023Review
- Development and Application of a Virtual Imaging Trial framework for Airway Quantifications via CT.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
Corrections and comments
- Commented on by
Authors and funding
5 authors.
Funding
Abstract
backgroundCT scan has notable potential to quantify the severity and progression of emphysema in patients. Such quantification should ideally reflect the true attributes and pathologic conditions of subjects, not scanner parameters. To achieve such an objective, the effects of the scanner conditions need to be understood so the influence can be mitigated. RESEARCH QUESTION: How do CT scan imaging parameters affect the accuracy of emphysema-based quantifications and biomarkers? STUDY DESIGN AND
methodsTwenty anthropomorphic digital phantoms were developed with diverse anatomic attributes and emphysema abnormalities informed by a real COPD cohort. The phantoms were input to a validated CT scan simulator (DukeSim), modeling a commercial scanner (Siemens Flash). Virtual images were acquired under various clinical conditions of dose levels, tube current modulations (TCM), and reconstruction techniques and kernels. The images were analyzed to evaluate the effects of imaging parameters on the accuracy of density-based quantifications (percent of lung voxels with HU < -950 [LAA-950] and 15
resultsThe most accurate imaging condition corresponded to the highest acquired dose (100 mAs) and iterative reconstruction (SAFIRE) with the smooth kernel of I31, where the measurement errors (difference between measurement and ground truth) were 35 ± 3 Hounsfield Units (HU), -4% ± 5%, and 26 ± 10 HU (average ± SD), for the mean lung HU, LAA-950, and Perc15, respectively. Without TCM and at the I31 kernel, increase of dose (20 to 100 mAs) improved the lung mean absolute error (MAE) by 4.2 ± 2.3 HU (average ± SD). TCM did not contribute to a systematic improvement of lung MAE.
interpretationThe results highlight that although CT scan quantification is possible, its reliability is impacted by the choice of imaging parameters. The developed virtual imaging trial platform in this study enables comprehensive evaluation of CT scan methods in reliable quantifications, an effort that cannot be readily made with patient images or simplistic physical phantoms.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.