ArticleThe British journal of radiology2020
Potential for dose reduction in CT emphysema densitometry with post-scan noise reduction: a phantom study.
Article in The British journal of radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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
7 citing papers in PubMed.
- Deep Learning Image Reconstruction Algorithm for Quantitative Assessment of Low-Dose Biphasic Chest CT in Chronic Obstructive Pulmonary Disease.International journal of chronic obstructive pulmonary disease · 2026Article
- Deep learning in CT image reconstruction and processing: techniques, performance evaluation, radiation dose, and future perspective.The British journal of radiology · 2025Review
- Impact of AI-Based Post-Processing on Image Quality of Non-Contrast Computed Tomography of the Chest and Abdomen.Diagnostics (Basel, Switzerland) · 2024Article
- Determination of lower radiation dose limit for automatic measurement of adipose tissue.Journal of applied clinical medical physics · 2023Article
- Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting.European radiology · 2022Article
- Improved precision of noise estimation in CT with a volume-based approach.European radiology experimental · 2021Article
- Influence of a novel deep-learning based reconstruction software on the objective and subjective image quality in low-dose abdominal computed tomography.The British journal of radiology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
objectiveThe aim of this phantom study was to investigate the effect of scan parameters and noise suppression techniques on the minimum radiation dose for acceptable image quality for CT emphysema densitometry.
methodsThe COPDGene phantom was scanned on a third generation dual-source CT system with 16 scan setups (CTDI
resultsIn total, 384 scan reconstructions were analyzed. Decreasing radiation dose resulted in an exponential increase of the overlap in normalized histograms of CT density. The overlap was 11-91% for the lowest dose setting (CTDI
conclusionCT density histogram overlap can quantify the degree of discernibility of emphysema and healthy lung tissue. Noise suppression software, IR, and soft reconstruction kernels substantially decrease the dose required for acceptable image quality. ADVANCES IN KNOWLEDGE: Noise suppression software, IR, and soft reconstruction kernels allow radiation dose reduction by 85% while still allowing differentiation between emphysema and normal lung tissue.
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.