Repositorio
Institucional

Valente, Mauro

Scarinci, Ignacio

Perez, Pedro

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

SPRINGER

EJNMMI PHYSICS

en

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.

2023

artículo original

10.1186/s40658-023-00560-9

PDF

Beta emitters

Dose point kernel

Internal dosimetry

Machine learning

Radiología, Medicina Nuclear e Imágenes Médicas

Emisores beta

Núcleo de punto de dosis

Dosimetría interna

Machine learning

artículo original

versión publicada

CC BY 4.0

CC BY 4.0

acceso abierto

acceso abierto

WOS:001020596300001

2197-7364

verde# dorada

Radiología, Medicina Nuclear e Imágenes Médicas

SeCyT 38111-425102001-2451

UFRO DI 21-0068

UFRO DI 21-1005