A Comparative Evaluation of Unconstrained Optimization Methods Applied to the Thermal Tomography Problem

[+] Author and Article Information
S. T. Clegg

Aerospace and Mechanical Engineering Department, University of Arizona, Tucson, Ariz. 85721

R. B. Roemer

Radiation Oncology Division, Arizona Health Sciences Center, Tucson, Ariz. 85724

J Biomech Eng 107(3), 228-233 (Aug 01, 1985) (6 pages) doi:10.1115/1.3138547 History: Received August 01, 1983; Revised April 29, 1985; Online June 15, 2009


In cancer hyperthermia treatments, it is important to be able to predict complete tissue temperature fields from sampled temperatures taken at the limited number of locations allowed by clinical constraints. An initial attempt to do this automatically using unconstrained optimization techniques to minimize the differences between experimental temperatures and temperatures predicted from treatment simulations has been previously reported [1]. This paper reports on a comparative study which applies a range of different optimization techniques (relaxation, steepest descent, conjugate gradient, Gauss, Box-Kanemasu, and Modified Box-Kanemasu) to this problem. The results show that the Gauss method converges more rapidly than the others, and that it converges to the correct solution regardless of the initial guess for the unknown blood perfusion vector. A sensitivity study of the error space is also performed, and the relationships between the error space characteristics and the comparative speeds of the optimization techniques are discussed.

Copyright © 1985 by ASME
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