Proton imaging with machine learning
Proton imaging with machine learning
复制标题
质子成像与机器学习
DOI：
10.1117/12.2580618
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Akgun, Ugur
中科院分区：
文献类型：
--
作者：
Finneman, Grant;Meskell, Nathan R.;Caplice, Timothy W.;Eichhorn, Owen H.;Abu-Halawa, Ammar S.;Stobb, Michael;Akgun, Ugur
本研究的目的是介绍一种紧凑的热量计，可以提供一个额外的成像工具的质子治疗中心。为此目的设计的钨，钆和镧系元素基高密度增透玻璃具有阻止厚度小于60 mm的200 MeV质子的能力，这使我们能够模拟可以连接到机架上的紧凑型探测器。玻璃的发展和初步的成像工作与此探测器的细节先前报道。这项研究总结了基于人工神经网络的成像工作与这种新型的质子成像仪探测器。通过GATE模拟创建了800个肿瘤的质子锥形束CT（CBCT）扫描库。该肿瘤库用于两种不同的机器学习工具Flux和PyTorch的训练目的。在这里，报告了概念验证机器学习成像研究。新材料的开发、紧凑的探测器设计和基于机器学习的成像可以使这种方法对临床应用有用。
The purpose of this study is to introduce a compact calorimeter that can offer an additional imaging tool for proton therapy centers. The tungsten, gadolinium, and lanthanide based high-density scintillating glass designed for this purpose has the ability to stop 200 MeV protons with thicknesses less than 60 mm, which allows us to model a compact detector that can be attached to a gantry. The details of the glass development and preliminary imaging efforts with this detector were previously reported. This study summarizes the Artificial Neural Network based imaging efforts with this novel proton imager detector. A library of proton conical beam CT (CBCT) scans of 800 tumors was created via GATE simulations. This tumor library was used for training purposes with two different machine learning tools, Flux and PyTorch. Here, the proof-of-concept machine learning imaging study is reported. The novel material development, compact detector design, and machine learning based imaging can make this approach useful for clinical applications.