Improvement of Sievert Integration Model in brachytherapy via inverse problems and artificial neural networks
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2017
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INTERNATIONAL TOPICAL MEETING ON INDUSTRIAL RADIATION AND RADIOISOTOPE MEASUREMENT APPLICATIONS, 10th
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Increasing the radial distance, the accuracy of the Sievert Integration Model (SIM)
decreases in a non-linear manner, adding errors up of 10% into the dose rate
calculations; a similar fact occurs to the 2D anisotropy function where the errors may
achieve 30% as already related. For that reason, this paper sought an innovative
approach to optimize the error variance and its biases of dose rate calculations around
a Nucletron brachytherapy source of 192Ir from 0 to 10 cm taken in the radial distance,
using an improved SIM through a hybrid coupling of Artificial Neural Networks (ANN)
and Inverse Problem Theory (IPT). Since the traditional approach relies into the use of a
small data set of dose rate, the ANN generalized these doses, making possible to
search more broadly optimum parameters to SIM using the IPT. The results showed
excellent accuracy evaluated with the Root Mean Square Percentage Error (RMSPE).
In conclusion, the low RMSPE values indicate that the methodology is a consistent
methodology, showing an excellent agreement with the state of art of dosimetric
measurement techniques.
Como referenciar
NASCIMENTO, ERIBERTO O.; OLIVEIRA, LUCAS N.; CALDAS, LINDA V.E. Improvement of Sievert Integration Model in brachytherapy via inverse problems and artificial neural networks. In: INTERNATIONAL TOPICAL MEETING ON INDUSTRIAL RADIATION AND RADIOISOTOPE MEASUREMENT APPLICATIONS, 10th, July 09-13, 2017, Chicago, IL, USA. Abstract... p. 75-76. Disponível em: http://repositorio.ipen.br/handle/123456789/27952. Acesso em: 30 Dec 2025.
Esta referência é gerada automaticamente de acordo com as normas do estilo IPEN/SP (ABNT NBR 6023) e recomenda-se uma verificação final e ajustes caso necessário.