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.
Bibliographical noteFunding Information:
This work was supported in part by the National Heart, Lung, and Blood Institute of the National Institutes of Health under Grants R01HL115185 and R21HL113857, and in part by the National Science Foundation under Grant CMMI-1150376. The work of J. Choi was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIT) under Grant 2018R1A4A1025986.
Manuscript received July 25, 2018; revised November 23, 2018; accepted January 21, 2019. Date of publication January 30, 2019; date of current version November 6, 2019.This work was supported in part by the National Heart, Lung, and Blood Institute of the National Institutes of Health under Grants R01HL115185 and R21HL113857, and in part by the National Science Foundation under Grant CMMI-1150376. The work of J. Choi was supported by the National Research Foundation of Korea (NRF) Grant funded by the Korea government (MSIT) under Grant 2018R1A4A1025986. (Corresponding author: Jongeun Choi.) L. Zhang and T. Maiti are with the Department of the Statistics and Probability, Michigan State University, East Lansing, MI 48824-1027 USA (e-mail:,firstname.lastname@example.org; email@example.com).
© 2013 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Electrical and Electronic Engineering
- Health Information Management