Ramos López Dayron, Pugliese Gabriella Maria Incoronata, Iaselli Giuseppe, Amoroso Nicola, Gong Chunhui, Pascali Valeria, Altieri Saverio, Protti Nicoletta
Dipartimento Interateneo di Fisica, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy.
Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy.
Cancers (Basel). 2023 Jul 12;15(14):3582. doi: 10.3390/cancers15143582.
Boron Neutron Capture Therapy (BNCT) is an innovative and highly selective treatment against cancer. Nowadays, in vivo boron dosimetry is an important method to carry out such therapy in clinical environments. In this work, different imaging methods were tested for dosimetry and tumor monitoring in BNCT based on a Compton camera detector. A dedicated dataset was generated through Monte Carlo tools to study the imaging capabilities. We first applied the Maximum Likelihood Expectation Maximization (MLEM) iterative method to study dosimetry tomography. As well, two methods based on morphological filtering and deep learning techniques with Convolutional Neural Networks (CNN), respectively, were studied for tumor monitoring. Furthermore, clinical aspects such as the dependence on the boron concentration ratio in image reconstruction and the stretching effect along the detector position axis were analyzed. A simulated spherical gamma source was studied in several conditions (different detector distances and boron concentration ratios) using MLEM. This approach proved the possibility of monitoring the boron dose. Tumor monitoring using the CNN method shows promising results that could be enhanced by increasing the training dataset.
硼中子俘获疗法（BNCT）是一种创新的、高选择性的癌症治疗方法。如今，体内硼剂量测定是在临床环境中开展此类治疗的重要方法。在这项工作中，基于康普顿相机探测器，对不同的成像方法进行了测试，用于BNCT中的剂量测定和肿瘤监测。通过蒙特卡罗工具生成了一个专用数据集，以研究成像能力。我们首先应用最大似然期望最大化（MLEM）迭代方法来研究剂量测定断层扫描。此外，还分别研究了基于形态学滤波和带有卷积神经网络（CNN）的深度学习技术的两种方法用于肿瘤监测。此外，还分析了诸如图像重建中对硼浓度比的依赖性以及沿探测器位置轴的拉伸效应等临床方面的问题。使用MLEM在几种条件下（不同的探测器距离和硼浓度比）研究了一个模拟球形伽马源。这种方法证明了监测硼剂量的可能性。使用CNN方法进行肿瘤监测显示出有前景的结果，通过增加训练数据集可以进一步增强效果。
