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Abstract：
OBJECTIVE: We propose a dual-domain cone beam computed tomography (CBCT) reconstruction framework DualCBR-Net based on improved differentiable domain transform for cone-angle artifact correction.
METHODS: The proposed CBCT dual-domain reconstruction framework DualCBR-Net consists of 3 individual modules: projection preprocessing, differentiable domain transform, and image post-processing. The projection preprocessing module first extends the original projection data in the row direction to ensure full coverage of the scanned object by X-ray. The differentiable domain transform introduces the FDK reconstruction and forward projection operators to complete the forward and gradient backpropagation processes, where the geometric parameters correspond to the extended data dimension to provide crucial prior information in the forward pass of the network and ensure the accuracy in the gradient backpropagation, thus enabling precise learning of cone-beam region data. The image post-processing module further fine-tunes the domain-transformed image to remove residual artifacts and noises.
RESULTS: The results of validation experiments conducted on Mayo\'s public chest dataset showed that the proposed DualCBR-Net framework was superior to other comparison methods in terms of artifact removal and structural detail preservation. Compared with the latest methods, the DualCBR-Net framework improved the PSNR and SSIM by 0.6479 and 0.0074, respectively.
CONCLUSIONS: The proposed DualCBR-Net framework for cone-angle artifact correction allows effective joint training of the CBCT dual-domain network and is especially effective for large cone-angle region.
METHODS: The proposed CBCT dual-domain reconstruction framework DualCBR-Net consists of 3 individual modules: projection preprocessing, differentiable domain transform, and image post-processing. The projection preprocessing module first extends the original projection data in the row direction to ensure full coverage of the scanned object by X-ray. The differentiable domain transform introduces the FDK reconstruction and forward projection operators to complete the forward and gradient backpropagation processes, where the geometric parameters correspond to the extended data dimension to provide crucial prior information in the forward pass of the network and ensure the accuracy in the gradient backpropagation, thus enabling precise learning of cone-beam region data. The image post-processing module further fine-tunes the domain-transformed image to remove residual artifacts and noises.
RESULTS: The results of validation experiments conducted on Mayo\'s public chest dataset showed that the proposed DualCBR-Net framework was superior to other comparison methods in terms of artifact removal and structural detail preservation. Compared with the latest methods, the DualCBR-Net framework improved the PSNR and SSIM by 0.6479 and 0.0074, respectively.
CONCLUSIONS: The proposed DualCBR-Net framework for cone-angle artifact correction allows effective joint training of the CBCT dual-domain network and is especially effective for large cone-angle region.
摘要：
目的：我们提出了一种基于改进的可微域变换的双域锥束计算机断层扫描（CBCT）重建框架DualCBR-Net，用于锥角伪影校正。
方法：提出的CBCT双域重建框架DualCBR-Net由3个单独的模块组成：投影预处理，可微域变换，和图像后处理。投影预处理模块首先在行方向上扩展原始投影数据以确保X射线对扫描对象的完全覆盖。微域变换引入FDK重构和前向投影算子来完成正向和梯度反向传播过程,其中几何参数对应于扩展的数据维度，在网络的正向传递中提供关键的先验信息，并确保梯度反向传播的准确性，从而实现锥束区域数据的精确学习。图像后处理模块进一步微调域变换图像以去除残余伪影和噪声。
结果：在Mayo\的公共胸部数据集上进行的验证实验结果表明，所提出的DualCBR-Net框架在伪影去除和结构细节保留方面优于其他比较方法。与最新方法相比，DualCBR-Net框架将PSNR和SSIM分别提高了0.6479和0.0074。
结论：提出的用于锥角伪影校正的DualCBR-Net框架允许对CBCT双域网络进行有效的联合训练，并且对于大锥角区域尤其有效。
方法：提出的CBCT双域重建框架DualCBR-Net由3个单独的模块组成：投影预处理，可微域变换，和图像后处理。投影预处理模块首先在行方向上扩展原始投影数据以确保X射线对扫描对象的完全覆盖。微域变换引入FDK重构和前向投影算子来完成正向和梯度反向传播过程,其中几何参数对应于扩展的数据维度，在网络的正向传递中提供关键的先验信息，并确保梯度反向传播的准确性，从而实现锥束区域数据的精确学习。图像后处理模块进一步微调域变换图像以去除残余伪影和噪声。
结果：在Mayo\的公共胸部数据集上进行的验证实验结果表明，所提出的DualCBR-Net框架在伪影去除和结构细节保留方面优于其他比较方法。与最新方法相比，DualCBR-Net框架将PSNR和SSIM分别提高了0.6479和0.0074。
结论：提出的用于锥角伪影校正的DualCBR-Net框架允许对CBCT双域网络进行有效的联合训练，并且对于大锥角区域尤其有效。
