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|Título:||Non-invasive tissue temperature evaluation during application of therapeutic ultrasound: precise time-spatial non-linear modelling|
|Autor:||Teixeira, C. A.|
Ruano, M. Graca
Ruano, A. E.
Pereira, W. C. A.
|Palavras-chave:||Non-invasive temperature estimation|
Radial basis functions,
Multi-objective genetic algorithms
|Citação:||Teixeira, C. A.; Graça Ruano, M.; Ruano, A. E.; Pereira, W. C. A.Non-invasive tissue temperature evaluation during application of therapeutic ultrasound: precise time-spatial non-linear modelling, In World Congress on Medical Physics and Biomedical Engineering 2006, 69-72, ISBN: 978-3-540-36839-7. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007.|
|Resumo:||The potential of thermal therapy’s applications improve with the development of accurate non-invasive timespatial temperature models. These models should represent the non-linear tissue thermal behaviour and be capable of tracking temperature at both time-instant and spatial point. An in-vitro experiment was developed based on a gel phantom, heated by a therapeutic ultrasound (TUS) device emitting continuously. The heating process was monitored by an imaging ultrasound (IUS) transducer working in pulse-echo mode, placed perpendicularly to the TUS transducer. The IUS RF-lines and temperature values were collected 60 mm distant from the TUS transducer face. Three thermocouples were aligned along the IUS transducer axial direction and across the TUS transducer radial direction (1 cm spaced). Three different TUS intensities were applied. The non-invasive time-spatial evolutionary temperature models were created making use of radial basis functions neural networks (RBFNN). The neural network input information was: the propagation time-delay between RF-line echoes and the past temperature lags from three different medium locations and three different TUS intensities. A total of nine different operating situations were studied. The best RBFNN structures were automatically determined by a multiobjective genetic algorithm, due to the enormous number of possible structures. The RBFNN temperature models were evaluated with data never used in the models, neither at the training or structural selection phases. In order to precisely evaluate the model generalisation performance these data included the nine possible operating situations. The best model presents a maximum absolute error less than 0.5 degrees Celsius (gold-standard value for hyperthermia/diathermia applications). To be mentioned also that the best model presents low computational complexity enabling future real-time implementations. Concluding, a maximum absolute error below the gold-standard value pointed for hyperthermia/diathermia applications was attained. In addition, this methodology does not require a-priori determination of physical constants and mathematical simplifications required for analytical methodologies.|
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