PDF(Pubmed)
Abstract：
BACKGROUND: X-ray image quality is critical for accurate intrafraction motion tracking in radiation therapy.
OBJECTIVE: This study aims to develop a deep-learning algorithm to improve kV image contrast by decomposing the image into bony and soft tissue components. In particular, we designed a priori attention mechanism in the neural network framework for optimal decomposition. We show that a patient-specific prior cross-attention (PCAT) mechanism can boost the performance of kV image decomposition. We demonstrate its use in paraspinal SBRT motion tracking with online kV imaging.
METHODS: Online 2D kV projections were acquired during paraspinal SBRT for patient motion monitoring. The patient-specific prior images were generated by randomly shifting and rotating spine-only DRR created from the setup CBCT, simulating potential motions. The latent features of the prior images were incorporated into the PCAT using multi-head cross attention. The neural network aimed to learn to selectively amplify the transmission of the projection image features that correlate with features of the priori. The PCAT network structure consisted of (1) a dual-branch generator that separates the spine and soft tissue component of the kV projection image and (2) a dual-function discriminator (DFD) that provides the realness score of the predicted images. For supervision, we used a loss combining mean absolute error loss, discriminator loss, perceptual loss, total variation, and mean squared error loss for soft tissues. The proposed PCAT approach was benchmarked against previous work using the ResNet generative adversarial network (ResNetGAN) without prior information.
RESULTS: The trained PCAT had improved performance in effectively retaining and preserving the spine structure and texture information while suppressing the soft tissues from the kV projection images. The decomposed spine-only x-ray images had the submillimeter matching accuracy at all beam angles. The decomposed spine-only x-ray significantly reduced the maximum errors to 0.44 mm (<2 pixels) in comparison to 0.92 mm (∼4 pixels) of ResNetGAN. The PCAT decomposed spine images also had higher PSNR and SSIM (p-value < 0.001).
CONCLUSIONS: The PCAT selectively learned the important latent features by incorporating the patient-specific prior knowledge into the deep learning algorithm, significantly improving the robustness of the kV projection image decomposition, and leading to improved motion tracking accuracy in paraspinal SBRT.
OBJECTIVE: This study aims to develop a deep-learning algorithm to improve kV image contrast by decomposing the image into bony and soft tissue components. In particular, we designed a priori attention mechanism in the neural network framework for optimal decomposition. We show that a patient-specific prior cross-attention (PCAT) mechanism can boost the performance of kV image decomposition. We demonstrate its use in paraspinal SBRT motion tracking with online kV imaging.
METHODS: Online 2D kV projections were acquired during paraspinal SBRT for patient motion monitoring. The patient-specific prior images were generated by randomly shifting and rotating spine-only DRR created from the setup CBCT, simulating potential motions. The latent features of the prior images were incorporated into the PCAT using multi-head cross attention. The neural network aimed to learn to selectively amplify the transmission of the projection image features that correlate with features of the priori. The PCAT network structure consisted of (1) a dual-branch generator that separates the spine and soft tissue component of the kV projection image and (2) a dual-function discriminator (DFD) that provides the realness score of the predicted images. For supervision, we used a loss combining mean absolute error loss, discriminator loss, perceptual loss, total variation, and mean squared error loss for soft tissues. The proposed PCAT approach was benchmarked against previous work using the ResNet generative adversarial network (ResNetGAN) without prior information.
RESULTS: The trained PCAT had improved performance in effectively retaining and preserving the spine structure and texture information while suppressing the soft tissues from the kV projection images. The decomposed spine-only x-ray images had the submillimeter matching accuracy at all beam angles. The decomposed spine-only x-ray significantly reduced the maximum errors to 0.44 mm (<2 pixels) in comparison to 0.92 mm (∼4 pixels) of ResNetGAN. The PCAT decomposed spine images also had higher PSNR and SSIM (p-value < 0.001).
CONCLUSIONS: The PCAT selectively learned the important latent features by incorporating the patient-specific prior knowledge into the deep learning algorithm, significantly improving the robustness of the kV projection image decomposition, and leading to improved motion tracking accuracy in paraspinal SBRT.
摘要：
背景：X射线图像质量对于放射治疗中准确的帧内运动跟踪至关重要。
目的：本研究旨在开发一种深度学习算法，通过将图像分解为骨骼和软组织成分来提高kV图像的对比度。特别是,我们在神经网络框架中设计了先验注意力机制进行最优分解。我们表明，特定于患者的先验交叉注意（PCAT）机制可以提高kV图像分解的性能。我们通过在线kV成像演示了其在椎旁SBRT运动跟踪中的应用。
方法：在椎旁SBRT期间获取在线2DkV投影，用于患者运动监测。通过随机移动和旋转从设置CBCT创建的仅脊柱DRR来生成患者特定的先前图像。模拟潜在的运动。使用多头交叉注意将先前图像的潜在特征合并到PCAT中。神经网络旨在学习选择性地放大与先验特征相关的投影图像特征的传输。PCAT网络结构由(1)分离kV投影图像的脊柱和软组织分量的双分支发生器和(2)提供预测图像的真实性分数的双功能鉴别器(DFD)组成。为了监督，我们使用了组合平均绝对误差损失的损失，鉴别器损失，知觉损失，总变异，和软组织的均方误差损失。拟议的PCAT方法是针对使用ResNet生成对抗网络（ResNetGAN）的先前工作进行的，而没有事先信息。
结果：经过训练的PCAT在有效保留和保留脊柱结构和纹理信息方面具有改善的性能，同时抑制了kV投影图像中的软组织。分解的仅脊柱X射线图像在所有光束角度都具有亚毫米匹配精度。与ResNetGAN的0.92mm（~4像素）相比，分解的仅脊柱X射线将最大误差显着降低至0.44mm（<2像素）。PCAT分解的脊柱图像也具有较高的PSNR和SSIM(p值<0.001)。
结论：PCAT通过将患者特定的先验知识纳入深度学习算法，选择性地学习了重要的潜在特征，显著提高了kV投影图像分解的鲁棒性,并提高了椎旁SBRT的运动跟踪精度。
目的：本研究旨在开发一种深度学习算法，通过将图像分解为骨骼和软组织成分来提高kV图像的对比度。特别是,我们在神经网络框架中设计了先验注意力机制进行最优分解。我们表明，特定于患者的先验交叉注意（PCAT）机制可以提高kV图像分解的性能。我们通过在线kV成像演示了其在椎旁SBRT运动跟踪中的应用。
方法：在椎旁SBRT期间获取在线2DkV投影，用于患者运动监测。通过随机移动和旋转从设置CBCT创建的仅脊柱DRR来生成患者特定的先前图像。模拟潜在的运动。使用多头交叉注意将先前图像的潜在特征合并到PCAT中。神经网络旨在学习选择性地放大与先验特征相关的投影图像特征的传输。PCAT网络结构由(1)分离kV投影图像的脊柱和软组织分量的双分支发生器和(2)提供预测图像的真实性分数的双功能鉴别器(DFD)组成。为了监督，我们使用了组合平均绝对误差损失的损失，鉴别器损失，知觉损失，总变异，和软组织的均方误差损失。拟议的PCAT方法是针对使用ResNet生成对抗网络（ResNetGAN）的先前工作进行的，而没有事先信息。
结果：经过训练的PCAT在有效保留和保留脊柱结构和纹理信息方面具有改善的性能，同时抑制了kV投影图像中的软组织。分解的仅脊柱X射线图像在所有光束角度都具有亚毫米匹配精度。与ResNetGAN的0.92mm（~4像素）相比，分解的仅脊柱X射线将最大误差显着降低至0.44mm（<2像素）。PCAT分解的脊柱图像也具有较高的PSNR和SSIM(p值<0.001)。
结论：PCAT通过将患者特定的先验知识纳入深度学习算法，选择性地学习了重要的潜在特征，显著提高了kV投影图像分解的鲁棒性,并提高了椎旁SBRT的运动跟踪精度。
