论文标题
朝着机器学习帮助的实时范围成像
Towards machine learning aided real-time range imaging in proton therapy
论文作者
论文摘要
在这项工作中,我们报告了I-TED Compton Imager的有利方面,用于质子范围监测,这是基于首次对其对该领域的适用性的蒙特卡洛研究的结果。 I-TED是一系列康普顿摄像机,它是为中子捕获核物理实验设计的,其特征是$γ$ - 雷的能量高达5-6 MEV,相当低的$γ$ ray排放率和强烈的中子中子诱发的$γ$ ray-ray-ray-ray-ray-ray-ray-ray-ray背景。我们以应对这三个方面的发展与强子治疗领域所需的发展相伴随,尤其是在实时监测的高效率方面,对中子背景的低灵敏度以及在高$γ$ ray ray-yergies时的可靠绩效。我们发现,与基于Lyso,cdznte或labr $ _ {3} $的其他类似系统相比,由于其轻巧的设计以及其LAC $ _ {3} $晶体的低中子捕获横截面,信噪比可以通过I-TED明显提高。它的高时间分辨率(CRT $ \ sim $ 500 PS)代表了在脉冲HT模式下操作时背景抑制的附加优势。每个I-TED模块都有两个非常大的LACL $ _ {3} $整体晶体的检测平面,从而在5 cm距离处达到了点状的1mev $γ$ ray源的高效率为0.2%。这导致了可靠图像重建的足够统计数据,其中四个I-TED检测器的阵列假设每个治疗点的临床强度为10 $^{8} $质子。由于双重时间的可达到的效率高于三倍事件,因此首选使用两平面设计而不是三平面。高能$γ$ - 砂的全能事件的损失是通过机器学习算法来补偿的,这使人们可以提高信噪比高达2倍。
In this work, we report on the advantageous aspects of the i-TED Compton imager for proton-range monitoring, based on the results of the first Monte Carlo study of its applicability to this field. i-TED is an array of Compton cameras, that have been designed for neutron-capture nuclear physics experiments, which are characterized by $γ$-ray energies spanning up to 5-6 MeV, rather low $γ$-ray emission yields and intense neutron induced $γ$-ray backgrounds. Our developments to cope with these three aspects are concomitant with those required in the field of hadron therapy, especially in terms of high efficiency for real-time monitoring, low sensitivity to neutron backgrounds and reliable performance at the high $γ$-ray energies. We find that signal-to-background ratios can be appreciably improved with i-TED thanks to its light-weight design and the low neutron-capture cross sections of its LaCl$_{3}$ crystals, when compared to other similar systems based on LYSO, CdZnTe or LaBr$_{3}$. Its high time-resolution (CRT$\sim$500 ps) represents an additional advantage for background suppression when operated in pulsed HT mode. Each i-TED module features two detection planes of very large LaCl$_{3}$ monolithic crystals, thereby achieving a high efficiency in coincidence of 0.2% for a point-like 1MeV $γ$-ray source at 5 cm distance. This leads to sufficient statistics for reliable image reconstruction with an array of four i-TED detectors assuming clinical intensities of 10$^{8}$ protons per treatment point. The use of a two-plane design instead of three-planes has been preferred owing to the higher attainable efficiency for double time-coincidences than for threefold events. The loss of full-energy events for high energy $γ$-rays is compensated by means of Machine-Learning algorithms, which allow one to enhance the signal-to-total ratio up to a factor of 2.