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Characterization of mesothelioma and tissues present in contrast-enhanced thoracic CT scans.

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Title: Characterization of mesothelioma and tissues present in contrast-enhanced thoracic CT scans.
Authors: Corson, Neal1, Sensakovic, William F.1, Straus, Christopher1, Starkey, Adam1, Armato, Samuel G.1
Source: Medical Physics. Feb2011, Vol. 38 Issue 2, p942-947. 6p.
Subjects: Mesothelioma, Tissue analysis, Contrast media, Tomography, Scanning systems, Liver, Image reconstruction
Abstract: Purpose: The purpose of this study was to characterize the Hounsfield unit (HU) distributions of mesothelioma and other tissues present in contrast-enhanced thoracic CT scans, to compare the HU distributions of mesothelioma, muscle, and liver by scanner and reconstruction filter/kernel combination, and to assess interpatient HU distribution variability. Methods: The database consisted of 28 contrast-enhanced thoracic CT scans from different patients. For each scan, regions of interest were manually outlined within each of 13 tissues, including mesothelioma. For each tissue, the empirical percentiles in HU values were calculated along with the interpatient variability. The HU distributions of select tissues were compared across three different scanner and reconstruction filter/kernel combinations. Results: The HU distributions of blood-containing tissues demonstrated substantial overlap, as did the HU distributions of pleural effusion, mesothelioma, muscle, and liver. The HU distribution of fat had the least overlap with the other tissues. Fat and muscle had the lowest interpatient HU variability and the narrowest HU distributions, while blood-containing tissues had the highest interpatient HU variability. A soft-tissue reconstruction filter/kernel yielded the narrowest HU distribution, and fat with artifact had the widest HU distribution. Conclusions: Characterization of tissues in CT scans enhances the understanding of those tissues' HU distributions. Due to their overlapping HU distributions and close spatial proximity to one another, separating pleural effusion, mesothelioma, muscle, and liver from one another is a difficult task based on HU value thresholding alone. The results illustrate the wide distributions and large variability that exist for tissues present in clinical thoracic CT scans. [ABSTRACT FROM AUTHOR]
Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Characterization of mesothelioma and tissues present in contrast-enhanced thoracic CT scans.
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  Data: <searchLink fieldCode="JN" term="%22Medical+Physics%22">Medical Physics</searchLink>. Feb2011, Vol. 38 Issue 2, p942-947. 6p.
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  Data: <searchLink fieldCode="DE" term="%22Mesothelioma%22">Mesothelioma</searchLink><br /><searchLink fieldCode="DE" term="%22Tissue+analysis%22">Tissue analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Contrast+media%22">Contrast media</searchLink><br /><searchLink fieldCode="DE" term="%22Tomography%22">Tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Scanning+systems%22">Scanning systems</searchLink><br /><searchLink fieldCode="DE" term="%22Liver%22">Liver</searchLink><br /><searchLink fieldCode="DE" term="%22Image+reconstruction%22">Image reconstruction</searchLink>
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  Data: Purpose: The purpose of this study was to characterize the Hounsfield unit (HU) distributions of mesothelioma and other tissues present in contrast-enhanced thoracic CT scans, to compare the HU distributions of mesothelioma, muscle, and liver by scanner and reconstruction filter/kernel combination, and to assess interpatient HU distribution variability. Methods: The database consisted of 28 contrast-enhanced thoracic CT scans from different patients. For each scan, regions of interest were manually outlined within each of 13 tissues, including mesothelioma. For each tissue, the empirical percentiles in HU values were calculated along with the interpatient variability. The HU distributions of select tissues were compared across three different scanner and reconstruction filter/kernel combinations. Results: The HU distributions of blood-containing tissues demonstrated substantial overlap, as did the HU distributions of pleural effusion, mesothelioma, muscle, and liver. The HU distribution of fat had the least overlap with the other tissues. Fat and muscle had the lowest interpatient HU variability and the narrowest HU distributions, while blood-containing tissues had the highest interpatient HU variability. A soft-tissue reconstruction filter/kernel yielded the narrowest HU distribution, and fat with artifact had the widest HU distribution. Conclusions: Characterization of tissues in CT scans enhances the understanding of those tissues' HU distributions. Due to their overlapping HU distributions and close spatial proximity to one another, separating pleural effusion, mesothelioma, muscle, and liver from one another is a difficult task based on HU value thresholding alone. The results illustrate the wide distributions and large variability that exist for tissues present in clinical thoracic CT scans. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Medical Physics is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Contrast media
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              Text: Feb2011
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