ArticleJournal of applied clinical medical physics2020
Dose and blending fraction quantification for adaptive statistical iterative reconstruction based on low-contrast detectability in abdomen CT.
Article in Journal of applied clinical medical physics, 2020. 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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1 citing paper in PubMed, 7 citations in OpenAlex.
- Automated development of the contrast-detail curve based on statistical low-contrast detectability in CT images.Journal of applied clinical medical physics · 2022Article
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1 author at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
purposeThe utilization of iterative reconstruction makes it difficult to identify the dose-noise relationship, resulting in empirical design of scan protocols and inconsistent conclusions on dose reduction for consistent image quality. This study was to quantitatively determine the dose and the blending fraction of adaptive statistical iterative reconstruction (ASIR) based on the specified low-contrast detectability (LCD).
methodsA tissue equivalent abdomen phantom and a GE discovery 750 HD computed tomography (CT) were utilized. The normality of the noise distribution was tested at various spatial scales (2.1-9.8 mm) in the presence of ASIR (10-100%) with a wide range of doses (2.24-38 mGy). The statically defined minimum detectable contrast (MDC) was used as the image quality metric. The parametric model decomposed the MDC into two terms: one with and the other without ASIR, each was related to the dose in the form of power law with factors and indices dependent on spatial scales. The parameters were identified by least-square fitting to the experimental data. By considering the constraint of the blending fraction in the range of [0, 1], the dose and ASIR blending fraction were determined for any specified low-contrast detectability (LCD), quantified by the MDC at the concerned lesion size.
resultsIt was verified that noise distribution is normal in the presence of ASIR. It was also found that the noises obtained from the subtractions of adjacent slices had an underestimate of 20% as compared to the ground truth noises, regardless of the spatial scale, pitch, or ASIR blending fraction. The least-square fitting for the parametric model resulted in correlation coefficients from 0.905 to 0.996. The root-mean-square errors ranged from 1.27% to 7.15%.
conclusionThe parametric model can be used to form a look-up-table for dose and ASIR blending fraction. The dose choices may be substantially limited in some cases depending on the required LCD.
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