TY - JOUR
T1 - Real-time predictive surgical control for cancer treatment using laser ablation
AU - Feng, Yusheng
AU - Fuentes, David
N1 - Funding Information:
The authors would like to thank the whole Dynamic Data Driven Application System (DDDAS) team, led by J. Tinsley Oden, Kenneth Diller, and John Hazle, which consisted of other faculty and participating graduate students (reference the list of authors in [5]). Support of this project, by NSF (CNS 0540033, CREST 0932339) and NIH (K25CA116291, 5T32CA119930, 1R21EB010196) is greatly acknowledged.
PY - 2011/5
Y1 - 2011/5
N2 - This article presents an overview on real-time predictive control for laser surgery based on the computational framework that consists of components for numerical implementation of the nonlinear heterogeneous Pennes equation of bioheat transfer including model calibration, remote data transfer, model coregistration, finite element meshing and parallel solution algorithms, cellular damage prediction, and optimal laser control. The goal of this research is to develop a predictive computational tool that may be used by surgeons during a minimally invasive hyper/hypothermia procedure to destroy cancerous tumors. The tool includes various components of computer models in the computational framework that controls the thermal source and makes a prediction of the treatment outcomes. Simultaneously, model parameters are updated to increase the accuracy based on the real-time intraoperative imaging data from in vivo temperature measurement. Current results show that it is important to consider the heterogeneity in the patient-specific cancerous region and the surrounding domain in order to the accuracy of prediction. By solving the corresponding inverse problem, predicted results can be improved by the experimental data, and capture well-known behavior of decreased perfusion in the damage region and hyperperfusion surrounding the damage region. Several canine in vivo experiments demonstrated the feasibility of using this computational framework to control laser induced thermal therapy, in terms of both efficiency and accuracy.
AB - This article presents an overview on real-time predictive control for laser surgery based on the computational framework that consists of components for numerical implementation of the nonlinear heterogeneous Pennes equation of bioheat transfer including model calibration, remote data transfer, model coregistration, finite element meshing and parallel solution algorithms, cellular damage prediction, and optimal laser control. The goal of this research is to develop a predictive computational tool that may be used by surgeons during a minimally invasive hyper/hypothermia procedure to destroy cancerous tumors. The tool includes various components of computer models in the computational framework that controls the thermal source and makes a prediction of the treatment outcomes. Simultaneously, model parameters are updated to increase the accuracy based on the real-time intraoperative imaging data from in vivo temperature measurement. Current results show that it is important to consider the heterogeneity in the patient-specific cancerous region and the surrounding domain in order to the accuracy of prediction. By solving the corresponding inverse problem, predicted results can be improved by the experimental data, and capture well-known behavior of decreased perfusion in the damage region and hyperperfusion surrounding the damage region. Several canine in vivo experiments demonstrated the feasibility of using this computational framework to control laser induced thermal therapy, in terms of both efficiency and accuracy.
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U2 - 10.1109/MSP.2011.940419
DO - 10.1109/MSP.2011.940419
M3 - Article
AN - SCOPUS:85032756230
SN - 1053-5888
VL - 28
SP - 134
EP - 138
JO - IEEE Signal Processing Magazine
JF - IEEE Signal Processing Magazine
IS - 3
M1 - 5753095
ER -