使用个性化数据生成策略通过患者特定深度学习进行实时无标记肿瘤跟踪：通过体模研究进行概念验证
Real-time markerless tumour tracking with patient-specific deep learning using a personalized data generation strategy: Proof of concept by phantom study
摘要：
目的：对于立体定向肺放疗中的实时无标记肿瘤跟踪，我们提出了一种不同的方法，该方法使用个性化数据生成策略，使用患者特定的深度学习（DL），避免了收集大量患者数据集的需要。我们用数字体模模拟和环氧树脂体模研究验证了我们的策略。研究方法：我们使用卷积神经网络开发了用于放射治疗的肺肿瘤跟踪，该卷积神经网络通过使用从每个体模的治疗计划4D-CT生成的多个数字重建射线照片（DRR）为每个体模的病变进行训练。我们使用各种投影几何形状生成的大量训练DRR来训练肿瘤-骨分化，以模拟肿瘤运动。我们解决了使用DRR进行训练和X射线图像进行跟踪的问题，通过使用训练DRR与随机对比度变换和随机噪声添加。结果：我们将足够的跟踪精度定义为满足等中心跟踪误差<1 mm的帧百分比。在模拟研究中，我们在3 cm球形和1.5 x 2.25 x 3 cm卵形质量中实现了100%的跟踪精度。在体模研究中，我们分别在3 cm和2 cm球形肿块中实现了100%和94.7%的跟踪准确度。这需要32.5 ms/帧（30.8 fps）的实时处理。结论：我们证明了基于患者特定DL的立体定向肺放疗的实时无标记肿瘤跟踪框架的潜在可行性，该框架具有数字体模和环氧树脂体模研究的个性化数据生成。知识进展：使用DL和个性化数据生成是实时肺部肿瘤跟踪的有效策略。
英文摘要：
Objective: For real-time markerless tumour tracking in stereotactic lung radiotherapy, we propose a different approach which uses patient-specific deep learning (DL) using a personalized data generation strategy, avoiding the need for collection of a large patient data set. We validated our strategy with digital phantom simulation and epoxy phantom studies. Methods: We developed lung tumour tracking for radiotherapy using a convolutional neural network trained for each phantom's lesion by using multiple digitally reconstructed radiographs (DRRs) generated from each phantom's treatment planning 4D-CT. We trained tumour-bone differentiation using large numbers of training DRRs generated with various projection geometries to simulate tumour motion. We solved the problem of using DRRs for training and X-ray images for tracking by using the training DRRs with random contrast transformation and random noise addition. Results: We defined adequate tracking accuracy as the % frames satisfying < 1 mm tracking error of the isocentre. In the simulation study, we achieved 100% tracking accuracy in 3-cm spherical and 1.5 x 2.25 x 3-cm ovoid masses. In the phantom study, we achieved 100% and 94.7% tracking accuracy in 3- and 2-cm spherical masses, respectively. This required 32.5 ms/frame (30.8 fps) real-time processing. Conclusions: We proved the potential feasibility of a real-time markerless tumour tracking framework for stereotactic lung radiotherapy based on patient-specific DL with personalized data generation with digital phantom and epoxy phantom studies. Advances in Knowledge: Using DL with personalized data generation is an efficient strategy for real-time lung tumour tracking.
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