Cargando…

Dose and blending fraction quantification for adaptive statistical iterative reconstruction based on low‐contrast detectability in abdomen CT

PURPOSE: The 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 bl...

Descripción completa

Detalles Bibliográficos
Autor principal: Zhou, Yifang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7021010/
https://www.ncbi.nlm.nih.gov/pubmed/31898865
http://dx.doi.org/10.1002/acm2.12813
_version_ 1783497851703132160
author Zhou, Yifang
author_facet Zhou, Yifang
author_sort Zhou, Yifang
collection PubMed
description PURPOSE: The 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). METHODS: A 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. RESULTS: It 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%. CONCLUSION: The 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.
format Online
Article
Text
id pubmed-7021010
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher John Wiley and Sons Inc.
record_format MEDLINE/PubMed
spelling pubmed-70210102020-03-06 Dose and blending fraction quantification for adaptive statistical iterative reconstruction based on low‐contrast detectability in abdomen CT Zhou, Yifang J Appl Clin Med Phys Medical Imaging PURPOSE: The 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). METHODS: A 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. RESULTS: It 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%. CONCLUSION: The 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. John Wiley and Sons Inc. 2020-01-03 /pmc/articles/PMC7021010/ /pubmed/31898865 http://dx.doi.org/10.1002/acm2.12813 Text en © 2020 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Medical Imaging
Zhou, Yifang
Dose and blending fraction quantification for adaptive statistical iterative reconstruction based on low‐contrast detectability in abdomen CT
title Dose and blending fraction quantification for adaptive statistical iterative reconstruction based on low‐contrast detectability in abdomen CT
title_full Dose and blending fraction quantification for adaptive statistical iterative reconstruction based on low‐contrast detectability in abdomen CT
title_fullStr Dose and blending fraction quantification for adaptive statistical iterative reconstruction based on low‐contrast detectability in abdomen CT
title_full_unstemmed Dose and blending fraction quantification for adaptive statistical iterative reconstruction based on low‐contrast detectability in abdomen CT
title_short Dose and blending fraction quantification for adaptive statistical iterative reconstruction based on low‐contrast detectability in abdomen CT
title_sort dose and blending fraction quantification for adaptive statistical iterative reconstruction based on low‐contrast detectability in abdomen ct
topic Medical Imaging
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7021010/
https://www.ncbi.nlm.nih.gov/pubmed/31898865
http://dx.doi.org/10.1002/acm2.12813
work_keys_str_mv AT zhouyifang doseandblendingfractionquantificationforadaptivestatisticaliterativereconstructionbasedonlowcontrastdetectabilityinabdomenct