Didonna Angelo, Ramos Lopez Dayron, Iaselli Giuseppe, Amoroso Nicola, Ferrara Nicola, Pugliese Gabriella Maria Incoronata
Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy.
Scuola di Specializzazione in Fisica Medica, Università degli Studi di Milano, 20133 Milan, Italy.
Cancers (Basel). 2025 Jan 3;17(1):130. doi: 10.3390/cancers17010130.
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 B(n,α)Li, 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.
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.
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.
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.
硼中子俘获疗法（BNCT）是一种创新的二元放射疗法，基于中子俘获反应B（n，α）Li对癌组织具有高选择性，其过程是在给予一种在癌细胞中优先蓄积的硼化合物后，让患者接受中子束照射。后续反应产生的高线性能量传递产物在细胞水平沉积能量，从而使正常组织免受损伤。尽管基于加速器的BNCT取得的进展引发了人们对这种癌症治疗方式的新兴趣，但治疗期间的体内剂量监测仍然不可行，目前正在研究多种方法。虽然康普顿成像相对于其他成像方法具有多种优势，但它通常需要较长的重建时间，这与BNCT的治疗持续时间相当。
本研究旨在开发深度神经网络模型，通过使用BNCT康普顿相机图像的模拟数据集来估计剂量分布。这些模型力求避免与最大似然期望最大化算法（MLEM）相关的迭代时间，从而能够在治疗期间迅速进行剂量重建。U-Net架构以及基于深度卷积小波框架的两种变体已被用于减少少迭代重建图像中的噪声和伪影。
就重建精度和处理时间而言，该方法已取得了有前景的结果，与传统迭代算法相比，处理时间减少了约6倍。
考虑到典型的BNCT治疗时间，这可被视为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，可能会实现进一步的改进。
