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Proceedings Paper

Patient-informed modelling of hepatic contrast dynamics in contrast-enhanced CT imaging
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Paper Abstract

Iodinated contrast agent is frequently used in computed tomography (CT) imaging to enhance organ contrast enhancement and improve diagnostic sensitivity. Despite this importance, there currently is a lack of standardization in contrast administration protocol across institutions, leading to many safety and clinical diagnostic risks. To solve this, we built three liver contrast enhancement/perfusion models: two using simple linear regression and another by combining a pre-existing pharmacokinetics mathematical model with clinical data with the eventual goal of individualizing contrast administration protocol to optimize contrast-enhanced CT imaging for each patient. These models primarily use patient attributes, such as height, weight, sex, age and contrast administration information, and bolus tracking information to make such predictions. 418 Chest/Abdomen/Pelvis CT scans were used in this study. 75% of cases were used to train these models and the rest were used to test the prediction accuracy. Pearson’s correlation coefficient test was used to find the correlations between the patient attributes and contrast enhancement in liver parenchyma. Weight, height, BMI, and lean body mass were found to be statistically significant predictors for contrast enhancement (P<0.05), with weight as the strongest predictor. Of the predictive models, we found that including bolus tracking information increases predictive accuracy (r2=0.75 v. 0.42) and that in the absence of bolus tracking information, combining clinical data with pre-existing pharmacokinetics model may provide the needed enhancement curve.

Paper Details

Date Published: 16 March 2020
PDF: 10 pages
Proc. SPIE 11312, Medical Imaging 2020: Physics of Medical Imaging, 1131214 (16 March 2020); doi: 10.1117/12.2548879
Show Author Affiliations
Hananiel Setiawan, Duke Univ. (United States)
Ehsan Abadi, Duke Univ. (United States)
Francesco Ria, Duke Univ. (United States)
Wanyi Fu, Duke Univ. (United States)
Taylor B. Smith, Duke Univ. (United States)
Ehsan Samei, Duke Univ. (United States)

Published in SPIE Proceedings Vol. 11312:
Medical Imaging 2020: Physics of Medical Imaging
Guang-Hong Chen; Hilde Bosmans, Editor(s)

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