Background: Boron neutron capture therapy (BNCT) is an innovative binary form of radiation therapy with high selectivity towards cancer tissue based on the neutron capture reaction10B(n,α)7Li, consisting in the exposition of patients to neutron beams after administration of a boron compound with preferential accumulation in cancer cells. The high linear energy transfer products of the ensuing reaction deposit their energy at the cell level, sparing normal tissue. Although progress in accelerator-based BNCT has led to renewed interest in this cancer treatment modality, in vivo dose monitoring during treatment still remains not feasible and several approaches are under investigation. While Compton imaging presents various advantages over other imaging methods, it typically requires long reconstruction times, comparable with BNCT treatment duration. Methods: This study aims to develop deep neural network models to estimate the dose distribution by using a simulated dataset of BNCT Compton camera images. The models pursue the avoidance of the iteration time associated with the maximum-likelihood expectation-maximization algorithm (MLEM), enabling a prompt dose reconstruction during the treatment. The U-Net architecture and two variants based on the deep convolutional framelets framework have been used for noise and artifact reduction in few-iteration reconstructed images. Results: This approach has led to promising results in terms of reconstruction accuracy and processing time, with a reduction by a factor of about 6 with respect to classical iterative algorithms. Conclusions: This can be considered a good reconstruction time performance, considering typical BNCT treatment times. Further enhancements may be achieved by optimizing the reconstruction of input images with different deep learning techniques.
背景：硼中子俘获疗法（BNCT）是一种创新的二元放射治疗形式，基于中子俘获反应10B(n,α)7Li，通过给予患者优先在癌细胞中积累的硼化合物后暴露于中子束，实现对癌组织的高选择性。该反应产生的高线性能量转移产物在细胞水平释放能量，从而保护正常组织。尽管基于加速器的BNCT进展重新激发了人们对这种癌症治疗方式的兴趣，但治疗过程中的体内剂量监测仍不可行，目前正在研究多种方法。虽然康普顿成像相比其他成像方法具有多种优势，但其通常需要较长的重建时间，与BNCT治疗持续时间相当。 方法：本研究旨在开发深度神经网络模型，利用BNCT康普顿相机图像的模拟数据集来估计剂量分布。这些模型旨在避免与最大似然期望最大化算法（MLEM）相关的迭代时间，从而实现治疗期间快速的剂量重建。研究中采用了U-Net架构以及基于深度卷积框架的两种变体，以减少少量迭代重建图像中的噪声和伪影。 结果：该方法在重建精度和处理时间方面取得了有希望的结果，与经典迭代算法相比，处理时间减少了约6倍。 结论：考虑到典型的BNCT治疗时间，这可以被认为是一种良好的重建时间性能。通过使用不同的深度学习技术优化输入图像的重建，可能实现进一步的改进。
肿瘤(癌症)患者之家