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Artifacts Reduction in 4D-CBCT via a Joint Free-form Registration Method of Projection Match and Gradient Constraint

机译:通过投影匹配和梯度约束的联合自由形式配准方法在4D-CBCT中减少伪像

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4D-CBCT reconstruction technique could provide a sequence of phase-resolved images to alleviate motion blurring artifacts as a result of respiratory movement during CT scanning. However, 4D-CBCT images are degraded by streaking artifacts due to the under-sampled projection used for the reconstruction of each phase. Based on the high correlation of these 4D-CBCT images, estimating the deformation vector fields (DVF) among them via a deformable registration algorithm is one of the possible solutions to improve the image quality. Often, the intensity-based similarity metric is utilized in the optimization problem by minimizing the squared sum of intensity differences (SSD) of the reference image and the target image. However, this metric is not suitable for the 4D-CBCT registration case, because the quality of both the reference image and the target image are not always guaranteed. As a result, the registration accuracy of the conventional SSD metric still has room to improve. In our method, by considering the characteristic of the phase-depended images, we design a novel similarity metric: 1) A prior image reconstructed by the whole projection set is regarded as the reference image; 2) Instead of an intensity-based similarity metric alone, we proposed a free-form based optimization function associating the gradient information in spatial domain with the projection-based constraint. To validate the performance of the proposed method, we carried out a phantom data and a patient data to compare with the classical Demons algorithm. To be specific, the quality of the registered image was improved to a great extent, especially in regions of interest of moving tissues. Quantitative evaluations were shown in terms of the rooted mean square error (RMSE) by our method when compared with existing Demons method.
机译:4D-CBCT重建技术可以提供一系列相位分辨图像,以减轻由于CT​​扫描过程中呼吸运动而引起的运动模糊伪影。然而,由于用于每个阶段的重构的欠采样投影,导致条纹伪影降低了4D-CBCT图像。基于这些4D-CBCT图像的高度相关性,通过可变形配准算法估计它们之间的变形矢量场(DVF)是提高图像质量的可能解决方案之一。通常,通过最小化参考图像和目标图像的强度差(SSD)的平方和,在优化问题中利用基于强度的相似性度量。但是,此度量标准不适用于4D-CBCT配准情况,因为不能始终保证参考图像和目标图像的质量。结果,常规SSD度量的配准精度仍然有提高的空间。在我们的方法中,通过考虑相位相关图像的特性,我们设计了一种新颖的相似性度量:1)将通过整个投影集重建的先验图像视为参考图像; 2)代替仅基于强度的相似性度量,我们提出了一种基于自由格式的优化函数,该函数将空间域中的梯度信息与基于投影的约束相关联。为了验证所提出方法的性能,我们进行了幻像数据和患者数据比较,以与经典的恶魔算法进行比较。具体而言,配准图像的质量得到了很大程度的改善,特别是在运动组织感兴趣的区域中。与现有的恶魔方法相比,定量分析显示了我们的方法的均方根误差(RMSE)。

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