Respiratory motion correction in 4D-PET by simultaneous motion estimation and image reconstruction (SMEIR).
Respiratory motion correction in 4D-PET by simultaneous motion estimation and image reconstruction (SMEIR).
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DOI：
10.1088/0031-9155/61/15/5639
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发表时间：
2016-08-07
影响因子：
3.5
通讯作者：
Wang J
中科院分区：
文献类型：
--
作者：
Kalantari F;Li T;Jin M;Wang J
在传统的4D正电子发射断层扫描（4D-PET）中，来自不同帧的图像被单独重建并通过配准方法对齐。这种方法出现的两个问题如下：1）重建算法没有充分利用投影统计;以及2）噪声图像之间的配准可能导致不良对准。在这项研究中，我们研究了使用同步运动估计和图像重建（SMEIR）的方法在4D-PET的运动估计/校正。采用改进的有序子集期望最大化算法结合全变差最小化（OSEM-TV），以Demons导出的变形矢量场（DVF）为初始运动矢量，从所有投影数据中获得初始运动补偿PET（pmc-PET）。通过将变形的pmc-PET的前向投影与来自其他相位的测量投影进行匹配，执行运动模型更新以获得pmc-PET和其他相位中的DVF的最佳集合。使用更新的DVF重复OSEM-TV图像重建，并基于更新的图像估计新的DVF。生成具有典型FDG生物分布的4D-XCAT体模，以评价SMEIR算法在具有不同对比度和不同直径（10至40 mm）的肺和肝肿瘤中的性能。SMEIR算法大大提高了4D-PET的图像质量。当所有投影都用于重建3D-PET而不进行运动补偿时，存在运动模糊伪影，导致高达150%的肿瘤尺寸高估和显着的定量误差，包括50%的肿瘤对比度低估和59%的肿瘤吸收低估。通过使用SMEIR算法，在大多数图像中误差降低到10%以下，显示了其在4D-PET运动估计/校正中的潜力。
In conventional 4D positron emission tomography (4D-PET), images from different frames are reconstructed individually and aligned by registration methods. Two issues that arise with this approach are as follows: 1) the reconstruction algorithms do not make full use of projection statistics; and 2) the registration between noisy images can result in poor alignment. In this study, we investigated the use of simultaneous motion estimation and image reconstruction (SMEIR) methods for motion estimation/correction in 4D-PET. A modified ordered-subset expectation maximization algorithm coupled with total variation minimization (OSEM-TV) was used to obtain a primary motion-compensated PET (pmc-PET) from all projection data, using Demons derived deformation vector fields (DVFs) as initial motion vectors. A motion model update was performed to obtain an optimal set of DVFs in the pmc-PET and other phases, by matching the forward projection of the deformed pmc-PET with measured projections from other phases. The OSEM-TV image reconstruction was repeated using updated DVFs, and new DVFs were estimated based on updated images. A 4D-XCAT phantom with typical FDG biodistribution was generated to evaluate the performance of the SMEIR algorithm in lung and liver tumors with different contrasts and different diameters (10 to 40 mm). The image quality of the 4D-PET was greatly improved by the SMEIR algorithm. When all projections were used to reconstruct 3D-PET without motion compensation, motion blurring artifacts were present, leading up to 150% tumor size overestimation and significant quantitative errors, including 50% underestimation of tumor contrast and 59% underestimation of tumor uptake. Errors were reduced to less than 10% in most images by using the SMEIR algorithm, showing its potential in motion estimation/correction in 4D-PET.