Dynamic data‐driven finite element models for laser treatment of cancer

Abstract

Elevating the temperature of cancerous cells is known to increase their susceptibility to subsequent radiation or chemotherapy treatments, and in the case in which a tumor exists as a well-defined region, higher intensity heat sources may be used to ablate the tissue. These facts are the basis for hyperthermia based cancer treatments. Of the many available modalities for delivering the heat source, the application of a laser heat source under the guidance of real-time treatment data has the potential to provide unprecedented control over the outcome of the treatment process (McNichols et al., Lasers Surg Med 34 (2004), 48-55; Salomir et al., Magn Reson Med 43 (2000), 342-347). The goals of this work are to provide a precise mathematical framework for the real-time finite element solution of the problems of calibration, optimal heat source control, and goal-oriented error estimation applied to the equations of bioheat transfer and demonstrate that current finite element technology, parallel computer architecture, data transfer infrastructure, and thermal imaging modalities are capable of inducing a precise computer controlled temperature field within the biological domain.

MRTI-Based Optimization and Real-Time Laser Surgical Control for Cancer Treatment Using Fast Inverse Analysis Techniques

“…The computational results presented in this section is similar to the thermal imaging data studied in [4] and [6]. The calibration algorithm is applied to these data sets to invert for the constitutive equation nonlinearites as well as the heterogeneity of the biological domain.…”

“…Image guidance facilitates real time treatment monitoring through temperature feedback during treatment delivery [8,10]. Moreover, high performance numerical implementations of mathematical bioheat transfer models can be used to process in vivo thermal imaging data to assist treatment planning as well as to predict the outcome of the treatment for real-time surgical control [6]. This system is able to provide surgeons in a operating room with real-time feedback and prediction of optimal temperature and damage fields which will permit patient-specific optimized therapy (see figure 1).…”

Modelling of tissue thermal injury formation process with application of direct sensitivity method

“…The damage of a biological tissue resulting from temperature elevation is usually modeled by the so-called Arrhenius injury integral defined as (Abraham and Sparrow, 2007;Oden et al, 2007) θ…”

“…The interval 0.1 to 1 reflects the blood flow decrease as the vasculature going to shut down. On the basic on the injury integral in form (2.5), the damage fraction F D is calculated as (Glenn et al, 1996;Oden et al, 2007)…”

Using Cyber-Infrastructure for Dynamic Data Driven Laser Treatment of Cancer

“…P is the laser power, μ a , μ s are laser coefficients related to laser wavelength and give probability of absorption of photons by tissue, γ is the anisotropy factor, and x 0 is the position of laser photon source. Constitutive model data and details of the optimization process are given in [8,11].…”