In cryosurgery operations tumoral cells are killed by means of a freezing procedure realized with insertion of cryoprobes in the diseased tissue. Cryosurgery planning aims at establishing the best values for operation parameters like number and position of the probes or temperature and duration of the freezing process. Here we present an application of Ant Colony Optimization (ACO) to cryosurgery planning, whereby the ACO cost function is computed by numerically solving several direct Stefan problems for biological tissues. The method is validated in the case of a 2D phantom of a prostate cross-section.

An optimization approach to multiprobe cryosurgery planning

BRIGNONE, MASSIMO;PIANA, MICHELE;CAVIGLIA, GIACOMO
2013-01-01

Abstract

In cryosurgery operations tumoral cells are killed by means of a freezing procedure realized with insertion of cryoprobes in the diseased tissue. Cryosurgery planning aims at establishing the best values for operation parameters like number and position of the probes or temperature and duration of the freezing process. Here we present an application of Ant Colony Optimization (ACO) to cryosurgery planning, whereby the ACO cost function is computed by numerically solving several direct Stefan problems for biological tissues. The method is validated in the case of a 2D phantom of a prostate cross-section.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/661572
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