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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

Estimation of glandular dose in mammography based on artificial neural networks

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Autor(es):
Trevisan Massera, Rodrigo [1] ; Tomal, Alessandra [1]
Número total de Autores: 2
Afiliação do(s) autor(es):
[1] Univ Estadual Campinas, Inst Fis Gleb Wataghin, BR-13083859 Campinas - Brazil
Número total de Afiliações: 1
Tipo de documento: Artigo Científico
Fonte: Physics in Medicine and Biology; v. 65, n. 9 MAY 7 2020.
Citações Web of Science: 0
Resumo

This work proposes using artificial neural networks (ANNs) for the regression of the dosimetric quantities employed in mammography. The data were generated by Monte Carlo (MC) simulations using a modified and validated version of the PENELOPE (v. 2014) + penEasy (v. 2015) code. A breast model of a homogeneous mixture of adipose and glandular tissue was adopted. The ANNs were constructed using the Keras and scikit-learn libraries for mean glandular dose (MGD) and air kerma (K-air) regressions, respectively. In total, seven parameters were considered, including the incident photon energies (from 8.25 to 48.75 keV), breast geometry, breast glandularity and K-air acquisition geometry. Two ensembles of five ANNs each were formed to calculate MGD and K-air. The normalized glandular dose coefficients (DgN) were calculated using the ratio of the ensemble outputs for MGD and K-air. Polyenergetic DgN values were calculated by weighting monoenergetic values by the spectrum bin probabilities. The results indicate a very good ANN prediction performance when compared to the validation data, with median errors on the order of the average simulation uncertainties (\& x224d; 0.2%). Moreover, the predicted DgN values are in good agreement compared with previously published works, with mean (maximum) differences up to 2.2% (9.4%). Therefore, it is shown that ANNs could be a complementary or alternative technique to tables, parametric equations and polynomial fits to estimate DgN values obtained via MC simulations. (AU)

Processo FAPESP: 16/15366-9 - Otimização dos parâmetros de exposição em mamografia digital: estudos experimentais e por Simulação Monte Carlo
Beneficiário:Rodrigo Trevisan Massera
Linha de fomento: Bolsas no Brasil - Mestrado
Processo FAPESP: 15/21873-8 - Desenvolvimento e implementação de metodologias para otimização de técnicas de imagens em radiologia digital
Beneficiário:Alessandra Tomal
Linha de fomento: Auxílio à Pesquisa - Regular