Deep residual fully connected neural network classification of Compton camera based prompt gamma imaging for proton radiotherapy
Deep residual fully connected neural network classification of Compton camera based prompt gamma imaging for proton radiotherapy
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DOI：
10.3389/fphy.2023.903929
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发表时间：
2023-02-16
影响因子：
3.1
通讯作者：
Gobbert, Matthias K. K.
中科院分区：
文献类型：
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作者：
Barajas, Carlos A. A.;Polf, Jerimy C. C.;Gobbert, Matthias K. K.
质子束放射治疗是一种癌症治疗方法，它使用质子束照射癌组织，同时最大限度地减少对健康组织的剂量。为了保证规定的辐射剂量被递送到肿瘤并确保健康组织不受影响，许多研究人员已经建议通过使用实时成像来验证治疗递送，所述实时成像使用能够对沿着穿过患者的射束路径沿着发射的瞬发伽马射线进行成像的方法，诸如康普顿照相机（CC）。然而，由于CC的限制，它们的图像是嘈杂的，并且不能用于验证质子治疗递送。我们提供了一个深度残差全连接神经网络的详细描述，该网络能够分类和改善测量的CC数据，使可用数据的比例增加高达72%，并允许在全范围的临床治疗条件下改善图像重建。
Proton beam radiotherapy is a method of cancer treatment that uses proton beams to irradiate cancerous tissue, while minimizing doses to healthy tissue. In order to guarantee that the prescribed radiation dose is delivered to the tumor and ensure that healthy tissue is spared, many researchers have suggested verifying the treatment delivery through the use of real-time imaging using methods which can image prompt gamma rays that are emitted along the beam's path through the patient such as Compton cameras (CC). However, because of limitations of the CC, their images are noisy and unusable for verifying proton treatment delivery. We provide a detailed description of a deep residual fully connected neural network that is capable of classifying and improving measured CC data with an increase in the fraction of usable data by up to 72% and allows for improved image reconstruction across the full range of clinical treatment delivery conditions.