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

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Date
2020-03-16
Authors
Setiawan, Hananiel
Ria, Francesco
Abadi, Ehsan
Fu, Wanyi
Smith, Taylor
Samei, Ehsan
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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.
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Conference
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https://hdl.handle.net/10161/20271
Published Version (Please cite this version)
10.1117/12.2548879
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Scholars@Duke

Abadi

Ehsan Abadi

Assistant Professor in Radiology
Ehsan Abadi, PhD is an imaging scientist at Duke University. He serves as an Assistant Professor in the departments of Radiology and Electrical & Computer Engineering, a faculty member in the Medical Physics Graduate Program and Carl E. Ravin Advanced Imaging Laboratories, and a co-Lead in the Center for Virtual Imaging Trials. Ehsan’s research focuses on quantitative imaging and optimization, CT imaging, lung diseases, computational human modeling, and medical imag
Ria

Francesco Ria

Research Associate, Senior
Samei

Ehsan Samei

Reed and Martha Rice Distinguished Professor of Radiology
Dr. Ehsan Samei, PhD, DABR, FAAPM, FSPIE, FAIMBE, FIOMP, FACR is a Persian-American medical physicist. He is a tenured Professor of Radiology, Medical Physics, Biomedical Engineering, Physics, and Electrical and Computer Engineering at Duke University, where he also serves as the Chief Imaging Physicist for Duke University Health System, the director of the Carl E Ravin Advanced Imaging Laboratories, and the director of Center for Virtual Imaging Trials. He is certi
Setiawan

Hananiel Setiawan

Affiliate
Doctor of Philosophy (PhD) Student in Medical Physics, 2017-PresentAs an early Medical Physics PhD student in the "Imaging" track, I currently study contrast-enhanced Computed Tomography (CT) imaging, with the goal of quantifying, managing, mitigating, monitoring, and optimizing variability of the protocol. I am looking forward to be applying physics knowledge and technique to the field of medicine.<!--[if gte mso 9
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