Boron Neutron Capture Therapy (BNCT) is an innovative binary form of radiation therapy with high selectivity towards cancer tissue based on the neutron capture reaction 10B(n,$\alpha$)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 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. 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 artifacts reduction in few-iterations reconstructed images, leading to promising results in terms of reconstruction accuracy and processing time.

翻译：硼中子俘获疗法（BNCT）是一种创新的二元放射治疗方式，基于10B(n,$\alpha$)7Li中子俘获反应，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治疗。该反应产生的高线性能量转移产物在细胞水平沉积能量，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模式的兴趣，但治疗过程中的体内剂量监测仍不可行，目前有多种方法正在研究中。虽然康普顿成像相较于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治疗时长相当。本研究旨在利用BNCT康普顿相机图像的模拟数据集，开发深度神经网络模型来估计剂量分布。这些模型致力于避免最大似然期望最大化算法（MLEM）相关的迭代时间，从而实现治疗过程中剂量的快速重建。研究采用U-Net架构及两种基于深度卷积框架的变体，用于减少低迭代次数重建图像中的噪声和伪影，在重建精度和处理时间方面均取得了有前景的结果。