Body composition determinants of radiation dose during abdominopelvic CT

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dc.contributor.author McLaughlin, Patrick D.
dc.contributor.author Chawke, Liam
dc.contributor.author Twomey, Maria
dc.contributor.author Murphy, Kevin P.
dc.contributor.author O'Neill, Siobhán B.
dc.contributor.author McWilliams, Sebastian R.
dc.contributor.author James, Karl
dc.contributor.author Kavanagh, Richard G.
dc.contributor.author Sullivan, Charles
dc.contributor.author Chan, Faimee E.
dc.contributor.author Moore, Niamh
dc.contributor.author O'Connor, Owen J.
dc.contributor.author Eustace, Joseph A.
dc.contributor.author Maher, Michael M.
dc.date.accessioned 2018-02-06T13:36:28Z
dc.date.available 2018-02-06T13:36:28Z
dc.date.issued 2017
dc.identifier.citation McLaughlin, P. D., Chawke, L., Twomey, M., Murphy, K. P., O’Neill, S. B., McWilliams, S. R., James, K., Kavanagh, R. G., Sullivan, C., Chan, F. E., Moore, N., O’Connor, O. J., Eustace, J. A. and Maher, M. M. (2017) 'Body composition determinants of radiation dose during abdominopelvic CT', Insights into Imaging. pp.1-8. doi: 10.1007/s13244-017-0577-y en
dc.identifier.startpage 1
dc.identifier.endpage 8
dc.identifier.issn 1869-4101
dc.identifier.uri http://hdl.handle.net/10468/5386
dc.identifier.doi 10.1007/s13244-017-0577-y
dc.description.abstract Objectives: We designed a prospective study to investigate the in-vivo relationship between abdominal body composition and radiation exposure to determine the strongest body composition predictor of dose length product (DLP) at CT. Methods: Following institutional review board approval, quantitative analysis was performed prospectively on 239 consecutive patients who underwent abdominopelvic CT. DLP, BMI, volumes of abdominal adipose tissue, muscle, bone and solid organs were recorded. Results: All measured body composition parameters correlated positively with DLP. Linear regression (R2 = 0.77) revealed that total adipose volume was the strongest predictor of radiation exposure [B (95% CI) = 0.027(0.024–0.030), t=23.068, p < 0.001]. Stepwise linear regression using DLP as the dependent and BMI and total adipose tissue as independent variables demonstrated that total adipose tissue is more predictive of DLP than BMI [B (95% CI) = 16.045 (11.337-20.752), t=6.681, p < 0.001]. Conclusions: The volume of adipose tissue was the strongest predictor of radiation exposure in our cohort. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Springer International Publishing AG en
dc.relation.uri https://link.springer.com/article/10.1007%2Fs13244-017-0577-y
dc.rights © 2017, the Authors. This article is an open access publication. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. en
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.subject Tomography en
dc.subject X-ray computed en
dc.subject Radiation dosage en
dc.subject Intra-abdominal fat en
dc.subject Muscle en
dc.subject Skeletal en
dc.subject Body mass index en
dc.title Body composition determinants of radiation dose during abdominopelvic CT en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother richard.kavanagh@ucc.ie en
dc.internal.availability Full text available en
dc.description.version Published Version en
dc.description.status Peer reviewed en
dc.identifier.journaltitle Insights into Imaging en
dc.internal.IRISemailaddress Richard Kavanagh, Anatomy and Neuroscience, University College Cork, Cork, Ireland. +353-21-490-3000 Email: richard.kavanagh@ucc.ie en


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©  2017, the Authors. This article is an open access publication. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Except where otherwise noted, this item's license is described as © 2017, the Authors. This article is an open access publication. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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