Repositorio UFRO · Omeka S

A machine learning-based model for a dose point kernel calculation

Primer Autor	Valente, Mauro
Co-autores	Scarinci, Ignacio Perez, Pedro
Título	A machine learning-based model for a dose point kernel calculation
Editorial	SPRINGER
Revista	EJNMMI PHYSICS
Lenguaje	en
Resumen	Purpose: Absorbed dose calculation by kernel convolution requires the prior determination of dose point kernels (DPK). This study reports on the design, implementation, and test of a multi-target regressor approach to generate the DPKs for monoenergetic sources and a model to obtain DPKs for beta emitters. Methods: DPK for monoenergetic electron sources were calculated using the FLUKA Monte Carlo (MC) code for many materials of clinical interest and initial energies ranging from 10 to 3000 keV. Regressor Chains (RC) with three different coefficients regularization/shrinkage models were used as base regressors. Electron monoenergetic scaled DPKs (sDPKs) were used to assess the corresponding sDPKs for beta emitters typically used in nuclear medicine, which were compared against reference published data. Finally, the beta emitters sDPK were applied to a patient-specific case calculating the Voxel Dose Kernel (VDK) for a hepatic radioembolization treatment with Y-90.Results: The three trained machine learning models demonstrated a promising capacity to predict the sDPK for both monoenergetic emissions and beta emitters of clinical interest attaining differences lower than 10% in the mean average percentage error (MAPE) as compared with previous studies. Furthermore, differences lower than 7% were obtained for the absorbed dose in patient-specific dosimetry comparing against full stochastic MC calculations.Conclusion: An ML model was developed to assess dosimetry calculations in nuclear medicine. The implemented approach has shown the capacity to accurately predict the sDPK for monoenergetic beta sources in a wide range of energy in different materials. The ML model to calculate the sDPK for beta-emitting radionuclides allowed to obtain VDK useful to achieve reliable patient-specific absorbed dose distributions required short computation times.
Fecha Publicación	2023
Tipo de Recurso	artículo original
doi	10.1186/s40658-023-00560-9
Formato Recurso	PDF
Palabras Claves	Beta emitters Dose point kernel Internal dosimetry Machine learning
Ubicación del archivo	http://dx.doi.org/10.1186/s40658-023-00560-9
Categoría OCDE	Radiología, Medicina Nuclear e Imágenes Médicas
Materias	Emisores beta Núcleo de punto de dosis Dosimetría interna Machine learning
Identificador del recurso (Mandatado-único)	artículo original
Versión del recurso (Recomendado-único)	versión publicada
License	CC BY 4.0
Condición de la licencia (Recomendado-repetible)	CC BY 4.0
Derechos de acceso	acceso abierto
Access Rights	acceso abierto
Id de Web of Science	WOS:001020596300001
ISSN	2197-7364
Tipo de ruta	verde# dorada
Categoría WOS	Radiología, Medicina Nuclear e Imágenes Médicas
Referencia del Financiador (Mandatado si es aplicable-repetible)	SeCyT 38111-425102001-2451 UFRO DI 21-0068 UFRO DI 21-1005