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

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.

Department

Description

Provenance

Subjects

Citation

Published Version (Please cite this version)

10.1117/12.2548879

Scholars@Duke

Setiawan

Hananiel Setiawan

Affiliate

Doctor of Philosophy (PhD) in Medical Physics
For my Medical Physics PhD dissertation research, I studied 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 10]>

<![endif]-->Prior to joining Duke, the majority of my undergraduate research projects were in nuclear physics at the National Superconducting Cyclotron Laboratory. I also participated in an astrophysics NSF-REU at Northwestern University, a US DOE-SULI in accelerator physics at SLAC National Laboratory (Stanford University), and a particle physics research abroad at Universität Zürich in 2017.

Educational Background
Doctor of Philosophy
Medical Physics, Diagnostic Imaging
Duke University (Durham, NC, USA) 2017-2023

Bachelor of Science
Physics, Honors
Michigan State University (East Lansing, MI, USA) 2014-2017

Associate of Science
Mathematics and Engineering Physics
Lansing Community College (Lansing, MI, USA) 2012-2014

Service to Duke
Vice President of Advocacy, Graduate and Professional Student Government 2021-2022
Director of Operations, Graduate and Professional Student Council 2019-2020
Student Member, External Engagement Committee of the Duke University Board of Trustees 2022-2023
Member, the Advisory Committee on Investment Responsibility 2022-2023
Board Member, Duke Chapel National Advisory Board 2018-Present
<!--[if gte mso 10]>

<![endif]-->

Ria

Francesco Ria

Assistant Professor of Radiology

Dr. Francesco Ria is a medical physicist and he serves as an Assistant Professor in the Department of Radiology. Francesco has an extensive expertise in the assessment of procedure performances in radiology. In particular, his research activities focus on the simultaneous evaluation of radiation dose and image quality in vivo in computed tomography providing a comprehensive evaluation of radiological exams. Moreover, Francesco is developing and investigating novel mathematical models that, uniquely in the radiology field, can incorporate a comprehensive and quantitative risk-to-benefit assessment of the procedures; he is continuing to apply his expertise towards the definition of new patient specific risk metrics, and in the assessment of image quality in vivo also using state-of-the-art imaging technology, such as photon counting computed tomography scanners, and machine learning reconstruction algorithms.

Dr. Ria is a member of the American Association of Physicists in Medicine task group 392 (Investigation and Quality Control of Automatic Exposure Control System in CT), of the American Association of Physicists in Medicine Public Education working group (WGATE), and of the Italian Association of Medical Physics task group Dose Monitoring in Diagnostic Imaging.

Abadi

Ehsan Abadi

Associate Professor in Radiology

Ehsan Abadi, PhD is an imaging scientist at Duke University. He serves as an Associate 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, computational human modeling, medical imaging simulation, and CT imaging in cardiothoracic and musculoskeletal applications. He is actively involved in developing computational anthropomorphic models with various diseases such as COPD, and scanner-specific simulation platforms (e.g., DukeSim) for imaging systems. Currently, his work is centered on identifying and optimizing imaging systems to ensure accurate and precise quantifications of lung and bone diseases.


Material is made available in this collection at the direction of authors according to their understanding of their rights in that material. You may download and use these materials in any manner not prohibited by copyright or other applicable law.