A feasibility study of enhanced prompt gamma imaging for range verification in proton therapy using deep learning.
A feasibility study of enhanced prompt gamma imaging for range verification in proton therapy using deep learning.
复制标题
使用深度学习进行质子治疗范围验证的增强瞬发伽马成像的可行性研究。
DOI：
10.1088/1361-6560/acbf9a
复制
发表时间：
2023
影响因子：
3.5
通讯作者：
Ren,Lei
中科院分区：
文献类型：
--
作者：
Jiang,Zhuoran;Polf,JerimyC;Barajas,CarlosA;Gobbert,MatthiasK;Ren,Lei
背景与目的射程不确定性是影响质子治疗精度的主要因素。基于康普顿照相机（CC）的γ射线（PG）成像是提供3D体内范围验证的有前途的技术。然而，传统的背投影PG图像由于CC的有限视野而遭受严重的失真，显著地限制了其临床实用性。深度学习已证明可以有效地增强有限视角测量的医学图像。但与其他具有丰富解剖结构的医学图像不同，沿质子笔形束的路径沿着发射的PG占据3D图像空间的极低部分，这为深度学习带来了注意力和不平衡的挑战。为了解决这些问题，我们提出了一种基于双层深度学习的方法，该方法具有新颖的加权轴投影损失，以生成精确的3D PG图像，从而实现精确的质子范围验证。材料与方法：所提出的方法由两个模型组成：首先，训练定位模型以在包含质子笔形射束的失真后向投影PG图像中定义感兴趣区域（ROI）;其次，训练增强模型以恢复真实的PG发射，并额外关注ROI。在这项研究中，我们使用蒙特-卡罗（MC）模拟了在组织等效体模中以临床剂量率（20 kMU min− 1和180 kMU min− 1）输送的54个质子笔形束（能量范围：75-125 MeV，剂量水平：1× 109质子/束和3× 108质子/束）。使用MC-Plus-Detector-Effects模型模拟使用CC的PG检测。图像重建使用核加权反投影算法，然后增强所提出的method.ResultsThe方法有效地恢复了三维形状的PG图像的质子铅笔束范围清晰可见，在所有的测试情况下。在大多数情况下，在较高剂量水平下，所有方向的范围误差均在2像素（4 mm）以内。所提出的方法是全自动的，并且增强仅需要10.26 s。总体而言，这项初步研究证明了所提出的方法使用深度学习框架生成精确的3D PG图像的可行性，为质子治疗的高精度体内范围验证提供了强大的工具。
Background and objectiveRange uncertainty is a major concern affecting the delivery precision in proton therapy. The Compton camera (CC)-based prompt-gamma (PG) imaging is a promising technique to provide 3D in vivo range verification. However, the conventional back-projected PG images suffer from severe distortions due to the limited view of the CC, significantly limiting its clinical utility. Deep learning has demonstrated effectiveness in enhancing medical images from limited-view measurements. But different from other medical images with abundant anatomical structures, the PGs emitted along the path of a proton pencil beam take up an extremely low portion of the 3D image space, presenting both the attention and the imbalance challenge for deep learning. To solve these issues, we proposed a two-tier deep learning-based method with a novel weighted axis-projection loss to generate precise 3D PG images to achieve accurate proton range verification. Materials and methods: the proposed method consists of two models: first, a localization model is trained to define a region-of-interest (ROI) in the distorted back-projected PG image that contains the proton pencil beam; second, an enhancement model is trained to restore the true PG emissions with additional attention on the ROI. In this study, we simulated 54 proton pencil beams (energy range: 75–125 MeV, dose level: 1× 10 9 protons/beam and 3× 10 8 protons/beam) delivered at clinical dose rates (20 kMU min− 1 and 180 kMU min− 1) in a tissue-equivalent phantom using Monte-Carlo (MC). PG detection with a CC was simulated using the MC-Plus-Detector-Effects model. Images were reconstructed using the kernel-weighted-back-projection algorithm, and were then enhanced by the proposed method.ResultsThe method effectively restored the 3D shape of the PG images with the proton pencil beam range clearly visible in all testing cases. Range errors were within 2 pixels (4 mm) in all directions in most cases at a higher dose level. The proposed method is fully automatic, and the enhancement takes only∼ 0.26 s.SignificanceOverall, this preliminary study demonstrated the feasibility of the proposed method to generate accurate 3D PG images using a deep learning framework, providing a powerful tool for high-precision in vivo range verification of proton therapy.