Washington University in St. Louis, Saint Louis, MO, 63130, United States of America.
University of Pittsburgh, Pittsburgh, PA, 15260, United States of America.
Phys Med Biol. 2023 Jul 5;68(14):145002. doi: 10.1088/1361-6560/acdf38.
Dual-energy computed tomography (DECT) has been widely used to reconstruct numerous types of images due its ability to better discriminate tissue properties. Sequential scanning is a popular dual-energy data acquisition method as it requires no specialized hardware. However, patient motion between two sequential scans may lead to severe motion artifacts in DECT statistical iterative reconstructions (SIR) images. The objective is to reduce the motion artifacts in such reconstructions.We propose a motion-compensation scheme that incorporates a deformation vector field into any DECT SIR. The deformation vector field is estimated via the multi-modality symmetric deformable registration method. The precalculated registration mapping and its inverse or adjoint are then embedded into each iteration of the iterative DECT algorithm.Results from a simulated and clinical case show that the proposed framework is capable of reducing motion artifacts in DECT SIRs. Percentage mean square errors in regions of interest in the simulated and clinical cases were reduced from 4.6% to 0.5% and 6.8% to 0.8%, respectively. A perturbation analysis was then performed to determine errors in approximating the continuous deformation by using the deformation field and interpolation. Our findings show that errors in our method are mostly propagated through the target image and amplified by the inverse matrix of the combination of the Fisher information and Hessian of the penalty term.We have proposed a novel motion-compensation scheme to incorporate a 3D registration method into the joint statistical iterative DECT algorithm in order to reduce motion artifacts caused by inter-scan motion, and successfully demonstrate that interscan motion corrections can be integrated into the DECT SIR process, enabling accurate imaging of radiological quantities on conventional SECT scanners, without significant loss of either computational efficiency or accuracy.
双能 CT(DECT)因其能够更好地区分组织特性而被广泛用于重建多种类型的图像。连续扫描是一种流行的双能数据采集方法,因为它不需要特殊的硬件。然而,在两次连续扫描之间,患者的运动会导致 DECT 统计迭代重建(SIR)图像中严重的运动伪影。目的是减少这些重建中的运动伪影。
我们提出了一种运动补偿方案,将变形向量场纳入任何 DECT SIR 中。变形向量场是通过多模态对称可变形配准方法来估计的。然后,将预先计算的配准映射及其逆或伴随项嵌入到迭代 DECT 算法的每次迭代中。
模拟和临床案例的结果表明,所提出的框架能够减少 DECT SIR 中的运动伪影。在模拟和临床病例中,感兴趣区域的百分比均方根误差分别从 4.6%降低到 0.5%和从 6.8%降低到 0.8%。然后进行了扰动分析,以确定使用变形场和插值来近似连续变形的误差。我们的发现表明,我们方法中的误差主要通过目标图像传播,并通过 Fisher 信息和惩罚项 Hessian 的组合的逆矩阵放大。
我们提出了一种新的运动补偿方案,将 3D 配准方法纳入联合统计迭代 DECT 算法中,以减少两次扫描之间的运动引起的运动伪影,并成功地证明了可以将扫描间运动校正集成到 DECT SIR 过程中,从而可以在没有显著降低计算效率或精度的情况下,在传统 SECT 扫描仪上准确成像放射性量。