基于改进可微分域转换的双域锥束计算机断层扫描重建网络用于锥角伪影校正

A dual-domain cone beam computed tomography reconstruction framework with improved differentiable domain transform for cone-angle artifact correction

目的 提出一种基于改进可微分域转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 所提出的双域CBCT重建框架DualCBR-Net包含3个模块:投影域预处理、可微分域转换和图像后处理。投影域预处理模块首先对投影数据进行排方向扩充,使被扫描物体能够被X射线完全覆盖。可微分域转换模块引入重建和前投影算子去完成双域网络的前向和梯度回传过程,其中几何参数对应扩大的数据维度,扩大几何在网络前向过程中提供了重要先验信息,在反向过程中保证了回传梯度的精度,使得锥角区域的数据学习更为精准。图像域后处理模块对域转换后的图像进一步微调以去除残留伪影和噪声。结果 在Mayo公开的胸部数据集上进行的验证实验结果显示,本研究提出的DualCBR-Net在伪影去除和结构细节保持方面均优于其他竞争方法;定量上,这种DualCBR-Net方法在PSNR和SSIM上相对于最新方法分别提高了0.6479和0.0074。结论 本研究提出的基于改进可微分域转换的双域CBCT重建框架DualCBR-Net用于锥角伪影校正方法使有效联合训练CBCT双域网络成为可能,尤其是对于大锥角区域。

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.Conclusion 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.