Patient-Specific Prediction of Abdominal Aortic Aneurysm Expansion Using Bayesian Calibration

Translating recent advances in abdominal aortic aneurysm (AAA) growth and remodeling (G&R) knowledge into a predictive, patient-specific clinical treatment tool requires a major paradigm shift in computational modeling. The objectives of this study are to develop a prediction framework that firs...

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Veröffentlicht in:IEEE journal of biomedical and health informatics 2019-11, Vol.23 (6), p.2537-2550
Hauptverfasser: Zhang, Liangliang, Jiang, Zhenxiang, Choi, Jongeun, Lim, Chae Young, Maiti, Tapabrata, Baek, Seungik
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container_issue 6
container_start_page 2537
container_title IEEE journal of biomedical and health informatics
container_volume 23
creator Zhang, Liangliang
Jiang, Zhenxiang
Choi, Jongeun
Lim, Chae Young
Maiti, Tapabrata
Baek, Seungik
description Translating recent advances in abdominal aortic aneurysm (AAA) growth and remodeling (G&R) knowledge into a predictive, patient-specific clinical treatment tool requires a major paradigm shift in computational modeling. The objectives of this study are to develop a prediction framework that first calibrates the physical AAA G&R model using patient-specific serial computed tomography (CT) scan images, predicts the expansion of an AAA in the future, and quantifies the associated uncertainty in the prediction. We adopt a Bayesian calibration method to calibrate parameters in the G&R computational model and predict the magnitude of AAA expansion. The proposed Bayesian approach can take different sources of uncertainty; therefore, it is well suited to achieve our aims in predicting the AAA expansion process as well as in computing the propagated uncertainty. We demonstrate how to achieve the proposed aims by solving the formulated Bayesian calibration problems for cases with the synthetic G&R model output data and real medical patient-specific CT data. We compare and discuss the performance of predictions and computation time under different sampling cases of the model output data and patient data, both of which are simulated by the G&R computation. Furthermore, we apply our Bayesian calibration to real patient-specific serial CT data and validate our prediction. The accuracy and efficiency of the proposed method is promising, which appeals to computational and medical communities.
doi_str_mv 10.1109/JBHI.2019.2896034
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subjects Abdominal aortic aneurysm (AAA)
Aneurysm
Aneurysms
Aorta
Aortic Aneurysm, Abdominal - diagnostic imaging
Aortic Aneurysm, Abdominal - pathology
Aortic aneurysms
Bayes methods
Bayes Theorem
Bayesian analysis
bayesian calibration
Calibration
Computational modeling
Computed tomography
Computer applications
Computer Simulation
CT scan
Data models
Disease Progression
Expansion
growth and remodeling (G&R) computational model
Humans
Image Interpretation, Computer-Assisted - methods
Patient-Specific Modeling
patient-specific predictive modeling
Patients
Predictions
Predictive models
Tomography, X-Ray Computed
Uncertainty
title Patient-Specific Prediction of Abdominal Aortic Aneurysm Expansion Using Bayesian Calibration
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