Non-invasive time-spatial temperature simulation using neural networks - Teixeira, C. A. (2013)
In this paper the performance of radial basis functions neural networks is accessed for non-invasive time-spatial temperature simulation in a gel-based phantom. The medium was heated at different intensities with a physiotherapeutic ultrasound device. In order to find an appropriate neural network structure the multi-objective genetic algorithm was applied. After the structure selection phase a set of preferable individuals was obtained, and the best one presents a maximum absolute error less than 0.5 oC, as desired in hyperthermia. In addition this model has low computational complexity, a fundamental point for a real-time application.