ArticleEJNMMI physics2023
A machine learning-based model for a dose point kernel calculation.
Article in EJNMMI physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Design and validation of a deep learning GAN for predictingJournal of applied clinical medical physics · 2026Article
- AI-enhanced patient-specific dosimetry in I-131 planar imaging with a single oblique view.Scientific reports · 2025Article
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Abstract
purposeAbsorbed 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.
methodsDPK 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 [Formula: see text]Y.
resultsThe 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 [Formula: see text] in the mean average percentage error (MAPE) as compared with previous studies. Furthermore, differences lower than [Formula: see text] were obtained for the absorbed dose in patient-specific dosimetry comparing against full stochastic MC calculations.
conclusionAn 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.
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