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Accuracy of lung nodule density on HRCT: analysis by PSF‐based image simulation

A computed tomography (CT) image simulation technique based on the point spread function (PSF) was applied to analyze the accuracy of CT‐based clinical evaluations of lung nodule density. The PSF of the CT system was measured and used to perform the lung nodule image simulation. Then, the simulated...

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Autores principales: Ohno, Ken, Ohkubo, Masaki, Marasinghe, Janaka C, Murao, Kohei, Matsumoto, Toru, Wada, Shinichi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5718548/
https://www.ncbi.nlm.nih.gov/pubmed/23149779
http://dx.doi.org/10.1120/jacmp.v13i6.3868
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author Ohno, Ken
Ohkubo, Masaki
Marasinghe, Janaka C
Murao, Kohei
Matsumoto, Toru
Wada, Shinichi
author_facet Ohno, Ken
Ohkubo, Masaki
Marasinghe, Janaka C
Murao, Kohei
Matsumoto, Toru
Wada, Shinichi
author_sort Ohno, Ken
collection PubMed
description A computed tomography (CT) image simulation technique based on the point spread function (PSF) was applied to analyze the accuracy of CT‐based clinical evaluations of lung nodule density. The PSF of the CT system was measured and used to perform the lung nodule image simulation. Then, the simulated image was resampled at intervals equal to the pixel size and the slice interval found in clinical high‐resolution CT (HRCT) images. On those images, the nodule density was measured by placing a region of interest (ROI) commonly used for routine clinical practice, and comparing the measured value with the true value (a known density of object function used in the image simulation). It was quantitatively determined that the measured nodule density depended on the nodule diameter and the image reconstruction parameters (kernel and slice thickness). In addition, the measured density fluctuated, depending on the offset between the nodule center and the image voxel center. This fluctuation was reduced by decreasing the slice interval (i.e., with the use of overlapping reconstruction), leading to a stable density evaluation. Our proposed method of PSF‐based image simulation accompanied with resampling enables a quantitative analysis of the accuracy of CT‐based evaluations of lung nodule density. These results could potentially reveal clinical misreadings in diagnosis, and lead to more accurate and precise density evaluations. They would also be of value for determining the optimum scan and reconstruction parameters, such as image reconstruction kernels and slice thicknesses/intervals. PACS numbers: 87.57.‐s, 87.57.cf, 87.57.Q‐
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spelling pubmed-57185482018-04-02 Accuracy of lung nodule density on HRCT: analysis by PSF‐based image simulation Ohno, Ken Ohkubo, Masaki Marasinghe, Janaka C Murao, Kohei Matsumoto, Toru Wada, Shinichi J Appl Clin Med Phys Medical Imaging A computed tomography (CT) image simulation technique based on the point spread function (PSF) was applied to analyze the accuracy of CT‐based clinical evaluations of lung nodule density. The PSF of the CT system was measured and used to perform the lung nodule image simulation. Then, the simulated image was resampled at intervals equal to the pixel size and the slice interval found in clinical high‐resolution CT (HRCT) images. On those images, the nodule density was measured by placing a region of interest (ROI) commonly used for routine clinical practice, and comparing the measured value with the true value (a known density of object function used in the image simulation). It was quantitatively determined that the measured nodule density depended on the nodule diameter and the image reconstruction parameters (kernel and slice thickness). In addition, the measured density fluctuated, depending on the offset between the nodule center and the image voxel center. This fluctuation was reduced by decreasing the slice interval (i.e., with the use of overlapping reconstruction), leading to a stable density evaluation. Our proposed method of PSF‐based image simulation accompanied with resampling enables a quantitative analysis of the accuracy of CT‐based evaluations of lung nodule density. These results could potentially reveal clinical misreadings in diagnosis, and lead to more accurate and precise density evaluations. They would also be of value for determining the optimum scan and reconstruction parameters, such as image reconstruction kernels and slice thicknesses/intervals. PACS numbers: 87.57.‐s, 87.57.cf, 87.57.Q‐ John Wiley and Sons Inc. 2012-11-08 /pmc/articles/PMC5718548/ /pubmed/23149779 http://dx.doi.org/10.1120/jacmp.v13i6.3868 Text en © 2012 The Authors. This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/3.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Medical Imaging
Ohno, Ken
Ohkubo, Masaki
Marasinghe, Janaka C
Murao, Kohei
Matsumoto, Toru
Wada, Shinichi
Accuracy of lung nodule density on HRCT: analysis by PSF‐based image simulation
title Accuracy of lung nodule density on HRCT: analysis by PSF‐based image simulation
title_full Accuracy of lung nodule density on HRCT: analysis by PSF‐based image simulation
title_fullStr Accuracy of lung nodule density on HRCT: analysis by PSF‐based image simulation
title_full_unstemmed Accuracy of lung nodule density on HRCT: analysis by PSF‐based image simulation
title_short Accuracy of lung nodule density on HRCT: analysis by PSF‐based image simulation
title_sort accuracy of lung nodule density on hrct: analysis by psf‐based image simulation
topic Medical Imaging
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5718548/
https://www.ncbi.nlm.nih.gov/pubmed/23149779
http://dx.doi.org/10.1120/jacmp.v13i6.3868
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