ArticleThe international journal of cardiovascular imaging2015
Predicting the image noise level of prospective ECG-triggered coronary computed tomography angiography: quantitative measurement of thoracic component versus body mass index.
Article in The international journal of cardiovascular imaging, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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1 citing paper in PubMed, 3 citations in OpenAlex.
- NoiseNet, a fully automatic noise assessment tool that can identify non-diagnostic CCTA examinations.The international journal of cardiovascular imaging · 2024Article
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We evaluated the feasibility of using quantitatively measured thoracic components, as compared to body mass index (BMI), for predicting the image noise of coronary computed tomography angiography (CCTA). One hundred subjects (M:F = 64:36; mean age, 55 ± 8.8 years) who underwent prospective electrocardiography-gated CCTA and low-dose chest computed tomography (CT) were analyzed retrospectively. The image noise of the CCTA was determined by the standard deviation of the attenuation value in a region of interest on the aortic root level. On the low-dose chest CT, the areas of the thoracic components were measured at the aortic root level. An auto-segmentation technique with the following threshold levels was used: quantitatively measured area of total thorax [QMAtotal: -910 to 1000 Hounsfield units (HU)], lung (QMAlung: -910 to -200 HU), fat (QMAfat: -200 to 0 HU), muscle (QMAmuscle: 0-300 HU), soft tissue (fat + muscle, QMAsoft tissue: -200 to 300 HU), bone (QMAbone: 300-1000 HU) and solid tissue (fat + muscle + bone, QMAsolid tissue: -200 to 1000 HU). The relationship between image noise and variable biometric parameters including QMA was analyzed, and the linear correlation coefficients were used as indicators of the strength of association. Among the variable biometric parameters, including BMI, QMAsolid tissue showed the highest correlation coefficient with image noise in all subjects (r = 0.804), males (r = 0.716), females (r = 0.889), the overweight (r = 0.556), and the non-overweight subgroups (r = 0.783). QMAsolid tissue can be used as a potential surrogate predictor of the image noise level in low tube voltage CCTA.
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