Paper Info
Title
Real-Time Predictive Surgical Control for Cancer Treatment Using Laser Ablation [Life Science]
Abstract
This article presents an over view 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 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.
Year
DOI
Venue
2011
10.1109/MSP.2011.940419
IEEE Signal Process. Mag.
Keywords
DocType
Volume
optimal laser control,bioheat transfer,laser surgery,minimally invasive hypothermia procedure,nonlinear heterogeneous pennes equation,cancerous tumors,cellular damage prediction,cancer,minimally invasive hyperthermia procedure,laser ablation,nonlinear equations,realtime predictive control,surgery,finite element meshing,patient treatment,predictive control,cancer treatment,data model,computer model,real time,data models,computational modeling,real time systems,temperature measurement
Journal
28
Issue
ISSN
Citations 
3
1053-5888
0
PageRank 
References 
Authors
0.34
3
2
Name
Order
Citations
PageRank
Yusheng Feng194.46
David Fuentes2164.42