Scarinci Ignacio, Valente Mauro, Pérez Pedro
Instituto de Física Enrique Gaviola (IFEG), CONICET, Av. Medina Allende s/n, Córdoba, 5000, Córdoba, Argentina.
Laboratorio de Investigación e Instrumentación en Física Aplicada a la Medicina e Imágenes de Rayos X (LIIFAMIRx), Facultad de Matemática, Astronomía, Física y Computación, Universidad Nacional de Córdoba, Av. Medina Allende s/n,, Córdoba, 5000, Córdoba, Argentina.
Res Sq. 2023 Jan 9:rs.3.rs-2419706. doi: 10.21203/rs.3.rs-2419706/v1.
Absorbed dose calculation by kernel convolution requires the prior determination of dose point kernels (DPK). This study shows applications of machine learning to generate the DPKs for monoenergetic sources and a model to obtain DPKs for beta emitters.
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. Three machine learning (ML) algorithms were trained using the MC DPKs. 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 ML sDPK approach was applied to a patient-specific case calculating the dose voxel kernels (DVK) for a hepatic radioembolization treatment with (^{90})Y.
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.
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 remarkable short computation times.
通过核卷积进行吸收剂量计算需要预先确定剂量点核（DPK）。本研究展示了机器学习在生成单能源DPK方面的应用，以及一种获取β发射体DPK的模型。
使用FLUKA蒙特卡罗（MC）代码针对多种具有临床意义的材料以及10至3000 keV的初始能量，计算单能电子源的DPK。使用MC DPK对三种机器学习（ML）算法进行训练。电子单能缩放DPK（sDPK）用于评估核医学中常用β发射体的相应sDPK，并与已发表的参考数据进行比较。最后，将ML sDPK方法应用于一个针对患者的案例，计算用(^{90})Y进行肝动脉放射性栓塞治疗的剂量体素核（DVK）。
三个经过训练的机器学习模型显示出有前景的能力，能够预测单能发射以及具有临床意义的β发射体的sDPK，与先前研究相比，平均平均百分比误差（MAPE）差异低于10%。此外，在针对患者的剂量测定中，与完全随机的MC计算相比，吸收剂量差异低于7%。
开发了一种ML模型来评估核医学中的剂量计算。所实施的方法已显示出能够在不同材料的广泛能量范围内准确预测单能β源的sDPK。用于计算β发射放射性核素sDPK的ML模型能够获得VDK，有助于实现可靠的针对患者的吸收剂量分布，且计算时间显著缩短。
