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Insights into Imaging
Published in: Insights into Imaging 1/2018

Open Access 01-02-2018 | Original Article

Body composition determinants of radiation dose during abdominopelvic CT

Authors: Patrick D. McLaughlin, Liam Chawke, Maria Twomey, Kevin P. Murphy, Siobhán B. O’Neill, Sebastian R. McWilliams, Karl James, Richard G. Kavanagh, Charles Sullivan, Faimee E. Chan, Niamh Moore, Owen J. O’Connor, Joseph A. Eustace, Michael M. Maher

Published in: Insights into Imaging | Issue 1/2018

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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.

Main message

• Individual body composition variables correlate with DLP at abdominopelvic CT.
• Total abdominal adipose tissue is the strongest predictor of radiation exposure.
• Muscle volume is also a significant but weaker predictor of DLP.
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Metadata
Title
Body composition determinants of radiation dose during abdominopelvic CT
Authors
Patrick D. McLaughlin
Liam Chawke
Maria Twomey
Kevin P. Murphy
Siobhán B. O’Neill
Sebastian R. McWilliams
Karl James
Richard G. Kavanagh
Charles Sullivan
Faimee E. Chan
Niamh Moore
Owen J. O’Connor
Joseph A. Eustace
Michael M. Maher
Publication date
01-02-2018
Publisher
Springer Berlin Heidelberg
Published in
Insights into Imaging / Issue 1/2018
Electronic ISSN: 1869-4101
DOI
https://doi.org/10.1007/s13244-017-0577-y

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