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Real-time markerless tumour tracking with patient-specific deep learning using a personalised data generation strategy: proof of concept by phantom study.

使用个性化数据生成策略的患者特异性深度学习的实时无标记肿瘤跟踪: 幻影研究的概念证明。

  • 影响因子:2.12
  • DOI:10.1259/bjr.20190420
  • 作者列表:"Takahashi W","Oshikawa S","Mori S
  • 发表时间:2020-05-01
Abstract

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 personalised 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 four-dimensional 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 using the training DRRs with random contrast transformation and random noise addition. RESULTS:We defined adequate tracking accuracy as the percentage frames satisfying <1 mm tracking error of the isocentre. In the simulation study, we achieved 100% tracking accuracy in 3 cm spherical and 1.5×2.25×3 cm ovoid masses. In the phantom study, we achieved 100 and 94.7% tracking accuracy in 3 cm 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 personalised data generation with digital phantom and epoxy phantom studies. ADVANCES IN KNOWLEDGE:Using DL with personalised data generation is an efficient strategy for real-time lung tumour tracking.

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,该方法使用患者特异性深度学习 (DL),使用个性化数据生成策略,避免需要收集大型患者数据集。我们通过数字体模模拟和环氧体体模研究验证了我们的策略。 方法: 我们开发了用于放疗的肺部肿瘤跟踪,使用卷积神经网络,通过使用多个数字重建的射线照片 (DRRs) 对每个体模病变进行训练。从每个体模的治疗计划生成的四维CT。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。我们解决了使用随机对比度变换和随机噪声添加的训练DRRs进行训练和x射线图像跟踪的问题。 结果: 我们将足够的跟踪精度定义为满足等中心 <1  mm跟踪误差的百分比帧。在仿真研究中,我们在 3  cm球形和 100% × 1.5 × 3  cm卵圆形质量中实现了 2.25 的跟踪精度。在体模研究中,我们分别在 100 和 2  cm球形质量中实现了 94.7% 和 3厘米的跟踪精度。这需要 32.5 ms/帧 (30.8 fps) 实时处理。 结论: 我们证明了基于患者特异性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,并通过数字体模和环氧体模研究进行个性化数据生成。 知识进展: 使用具有个性化数据生成的DL是实时肺部肿瘤跟踪的有效策略。

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翻译标题与摘要 下载文献
影响因子:6.93
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DOI:10.1002/ijc.32530
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肺肿瘤方向

肺肿瘤,又叫支气管肺癌,是常见的恶性肿瘤之一。肺肿瘤的治疗为包括手术、中药、放疗、化疗及免疫等多学科的综合治疗。

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