Abstract
PURPOSE
Iodinated contrast agents are commonly used in CT imaging to enhance tissue contrast.
Consistency in contrast enhancement (CE) is critical in radiological diagnosis. Contrast
material circulation in individual patients is affected by factors such as patient
body habitus and anatomy leading to significant variability in organ contrast enhancement,
image quality, and dose. Toward the goal of improving CE consistency in clinical populations,
in this work we developed a contrast dynamics model to predict CT HU enhancement of
liver parenchyma in abdominopelvic CE CT scans.
METHOD AND MATERIALS
This study included 700 adult abdominopelvic contrast CT exams performed in 2014-2018
using two scanner models from two vendors. Each CT image was segmented using a deep
learning-based segmentation algorithm and the hepatic parenchyma HU values were acquired
from the segmentations. A two-layer neural network-based algorithm was used to identify
the relationship between patient attributes (height, weight, BMI, age, sex), scan
parameters (slice thickness, scanner model), contrast injection protocols (bolus volume,
injection-to-scan wait time), and the liver HU CE. We randomly selected 60% studies
for training, 10% validation, and 30% for testing the accuracy. The training output
was the extracted HU values. The goodness-of-fit of the model was evaluated in terms
of R^2, Adjusted R^2, Mean Absolute Error (MAE), and Mean Squared Error (MSE) between
the model prediction and ground truth. In addition, the generalizability of the model
was evaluated by comparing the R^2 in the training data (leave-one-out validation)
and the testing data.
RESULTS
This preliminary model has an 0.51 R^2, 0.40 adjusted R^2, 10.0 HU MAE, 159.1 HU MSE,
0.6±12.8 HU Mean Error, and 2.5 HU Median Error on test data. For training data, the
model has 0.59 R^2, 0.56 Adjusted R^2, and 0.5 predicted R^2. The close R^2 between
testing and training data results indicate a reasonable generalizability.
CONCLUSION
Results showed considerable predictability of liver CE from patient attributes, scanning
parameters, and contrast administration protocol. We envision to expand the model
to include other major organs toward a comprehensive predictive model.
CLINICAL RELEVANCE/APPLICATION
A contrast dynamics model can be an essential tool to personalize contrast-enhanced
CT protocol and to improve the consistency of contrast enhancement across different
patients in diagnostics imaging.
Published Version (Please cite this version)
10.1117/12.2548879