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利用合成目标特定数字重建射线照片增强目标可见度,用于分次内运动监测:概念验证研究。

Enhancing the target visibility with synthetic target specific digitally reconstructed radiograph for intrafraction motion monitoring: A proof-of-concept study.

机构信息

Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.

Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, New York, USA.

出版信息

Med Phys. 2023 Dec;50(12):7791-7805. doi: 10.1002/mp.16580. Epub 2023 Jul 3.

DOI:10.1002/mp.16580
PMID:37399367
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11313213/
Abstract

BACKGROUND

Intrafraction motion monitoring in External Beam Radiation Therapy (EBRT) is usually accomplished by establishing a correlation between the tumor and the surrogates such as an external infrared reflector, implanted fiducial markers, or patient skin surface. These techniques either have unstable surrogate-tumor correlation or are invasive. Markerless real-time onboard imaging is a noninvasive alternative that directly images the target motion. However, the low target visibility due to overlapping tissues along the X-ray projection path makes tumor tracking challenging.

PURPOSE

To enhance the target visibility in projection images, a patient-specific model was trained to synthesize the Target Specific Digitally Reconstructed Radiograph (TS-DRR).

METHODS

Patient-specific models were built using a conditional Generative Adversarial Network (cGAN) to map the onboard projection images to TS-DRR. The standard Pix2Pix network was adopted as our cGAN model. We synthesized the TS-DRR based on the onboard projection images using phantom and patient studies for spine tumors and lung tumors. Using previously acquired CT images, we generated DRR and its corresponding TS-DRR to train the network. For data augmentation, random translations were applied to the CT volume when generating the training images. For the spine, separate models were trained for an anthropomorphic phantom and a patient treated with paraspinal stereotactic body radiation therapy (SBRT). For lung, separate models were trained for a phantom with a spherical tumor insert and a patient treated with free-breathing SBRT. The models were tested using Intrafraction Review Images (IMR) for the spine and CBCT projection images for the lung. The performance of the models was validated using phantom studies with known couch shifts for the spine and known tumor deformation for the lung.

RESULTS

Both the patient and phantom studies showed that the proposed method can effectively enhance the target visibility of the projection images by mapping them into synthetic TS-DRR (sTS-DRR). For the spine phantom with known shifts of 1 mm, 2 mm, 3 mm, and 4 mm, the absolute mean errors for tumor tracking were 0.11 ± 0.05 mm in the x direction and 0.25 ± 0.08 mm in the y direction. For the lung phantom with known tumor motion of 1.8 mm, 5.8 mm, and 9 mm superiorly, the absolute mean errors for the registration between the sTS-DRR and ground truth are 0.1 ± 0.3 mm in both the x and y directions. Compared to the projection images, the sTS-DRR has increased the image correlation with the ground truth by around 83% and increased the structural similarity index measure with the ground truth by around 75% for the lung phantom.

CONCLUSIONS

The sTS-DRR can greatly enhance the target visibility in the onboard projection images for both the spine and lung tumors. The proposed method could be used to improve the markerless tumor tracking accuracy for EBRT.

摘要

背景

在外部射束放射治疗(EBRT)中,分次内运动监测通常通过在肿瘤和替代物(如外部红外反射器、植入的基准标记物或患者皮肤表面)之间建立相关性来实现。这些技术要么替代物-肿瘤相关性不稳定,要么具有侵入性。无标记实时在线成像则是一种直接对目标运动进行成像的非侵入性替代方法。然而,由于沿 X 射线投影路径重叠的组织,目标的可见度较低,使得肿瘤跟踪具有挑战性。

目的

为了增强投影图像中的目标可见度,我们训练了一个患者特异性模型来合成靶标特异性数字重建射线照片(TS-DRR)。

方法

使用条件生成对抗网络(cGAN)为每个患者构建模型,将在线投影图像映射到 TS-DRR。采用标准的 Pix2Pix 网络作为我们的 cGAN 模型。我们使用脊柱肿瘤和肺肿瘤的体模和患者研究,基于在线投影图像合成 TS-DRR。使用先前获得的 CT 图像,我们生成 DRR 及其相应的 TS-DRR 来训练网络。为了数据扩充,在生成训练图像时,对 CT 体数据应用随机平移。对于脊柱,我们分别为体模和接受脊柱旁立体定向体部放射治疗(SBRT)的患者训练模型。对于肺部,我们分别为带有球形肿瘤插入物的体模和接受自由呼吸 SBRT 的患者训练模型。我们使用脊柱的分次内审查图像(IMR)和肺部的 CBCT 投影图像来测试模型。我们使用脊柱已知的治疗床移动和肺部已知的肿瘤变形的体模研究来验证模型的性能。

结果

在患者和体模研究中,我们发现,通过将在线投影图像映射到合成的 TS-DRR(sTS-DRR),该方法可以有效地增强投影图像中的目标可见度。对于已知移动为 1mm、2mm、3mm 和 4mm 的脊柱体模,在 x 方向上肿瘤跟踪的绝对平均误差为 0.11±0.05mm,在 y 方向上为 0.25±0.08mm。对于已知肿瘤向上移动 1.8mm、5.8mm 和 9mm 的肺部体模,sTS-DRR 与地面实况之间的注册的绝对平均误差在 x 和 y 方向上均为 0.1±0.3mm。与投影图像相比,sTS-DRR 使图像与地面实况的相关性增加了约 83%,与地面实况的结构相似性指数测量值增加了约 75%。

结论

sTS-DRR 可以大大增强脊柱和肺部肿瘤在线投影图像中的目标可见度。该方法可用于提高 EBRT 中无标记肿瘤跟踪的准确性。

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本文引用的文献

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J Appl Clin Med Phys. 2022 Jun;23(6):e13594. doi: 10.1002/acm2.13594. Epub 2022 Mar 26.
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The markerless lung target tracking AAPM Grand Challenge (MATCH) results.无标记肺靶区跟踪 AAPM 大挑战赛(MATCH)结果。
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