In cryosurgery operations, tumoural cells are killed by means of a freezing procedure realised with the 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 optimisation (ACO) to cryosurgery planning, whereby the ACO cost function is computed by numerically solving several direct Stefan problems in biological tissues. The method is validated in the case of a 2D phantom of a prostate cross section. © 2013 Taylor & Francis.
An optimisation approach to multiprobe cryosurgery planning
Piana M;
2013
Abstract
In cryosurgery operations, tumoural cells are killed by means of a freezing procedure realised with the 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 optimisation (ACO) to cryosurgery planning, whereby the ACO cost function is computed by numerically solving several direct Stefan problems in biological tissues. The method is validated in the case of a 2D phantom of a prostate cross section. © 2013 Taylor & Francis.File in questo prodotto:
Non ci sono file associati a questo prodotto.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
