论文标题
使用个性化数据生成策略：幻影研究证明
Real-time markerless tumour tracking with patient-specific deep learning using a personalized data generation strategy: Proof of concept by phantom study
论文作者
论文摘要
目的：对于立体定向性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），避免需要收集大型患者数据集。我们通过数字幻影模拟和环氧幻影研究验证了我们的策略。 方法：我们使用为每个幻影病变训练的卷积神经网络开发了放射疗法的肺肿瘤跟踪，该卷积神经网络通过使用每个幻影计划4D-CT产生的多个数字重建放射线照片（DRR）。我们使用具有各种投影几何形状的大量训练DRR训练了肿瘤 - 骨头分化，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，并使用具有随机对比度转换和随机噪声的训练DRR。 结果：我们将足够的跟踪精度定义为满足<1 mm跟踪误差的iS中心误差。在模拟研究中，我们在3厘米球形和1.5 x 2.25 x 3厘米卵形质量中实现了100％跟踪的准确性。在幻影研究中，我们分别在3和2-CM球形质量中达到了100％和94.7％的跟踪精度。这需要32.5毫秒/帧（30.8 fps）实时处理。 结论：我们证明了实时无标记的肿瘤跟踪框架对基于患者特异性DL的立体定向性肺放疗的潜在可行性，并具有数字幻影和环氧幻影研究的个性化数据生成。 知识的进步：将DL与个性化数据生成是实时肺肿瘤跟踪的有效策略。
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.
