Molecular Imaging Center Antwerp, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium.
University Hospital Antwerp, Wilrijkstraat 10, 2650, Antwerp, Belgium.
Med Biol Eng Comput. 2019 Feb;57(2):505-518. doi: 10.1007/s11517-018-1899-8. Epub 2018 Sep 22.
Motion tracking with finite time sampling causing an associated unknown residual motion between two motion measurements is one of the factors contributing to resolution loss in small animal PET motion correction. The aim of this work is (i) to provide a means to estimate the effect of the finite motion sampling on the spatial resolution of the motion correction reconstructions and (ii) to correct for this residual motion thereby minimizing resolution loss. We calculate a tailored spatially variant deconvolution kernel from the measured motion data which is then used to deconvolve the motion corrected image using a 3D Richardson-Lucy algorithm. A simulation experiment of numerical phantoms as well as a microDerenzo phantom experiment wherein the phantom was manually moved at different speeds was performed to assess the performance of our proposed method. In the motion corrected images of the microDerenzo phantom there was an average rod FWHM differences between the slow and fast motion cases of 9.7%. This difference was reduced to 5.8% after applying the residual motion deconvolution. In awake animal experiments, the proposed method can serve to mitigate the finite sampling factor degrading the spatial resolution as well as the resolution differences between fast-moving and slow-moving animals. Graphical abstract Motion correction of positron emission tomography (PET) scans of moving subjects can be performed by measuring the motion of the subject during the PET scan with an optical tracking camera. The motion tracking data obtained from the tracking camera is then used to correct the PET image reconstructions for motion. Due to finite time sampling of the motion data, the motion corrected reconstructions suffer from loss of spatial resolution. In the proposed method, a spatially variant deconvolution kernel is calculated from the motion tracking data, which is then used to correct the motion-corrected PET reconstructions for the blurring effect of the finite motion sampling through a Richardson-Lucy deconvolution.
运动跟踪的有限时间采样导致两次运动测量之间存在未知的残留运动，这是导致小动物 PET 运动校正中分辨率损失的因素之一。本工作的目的是：(i) 提供一种估计有限运动采样对运动校正重建空间分辨率影响的方法；(ii) 纠正这种残留运动，从而最小化分辨率损失。我们从测量的运动数据中计算出一个特制的空间变分反卷积核，然后使用 3D Richardson-Lucy 算法对运动校正图像进行反卷积。为了评估我们提出的方法的性能，我们进行了数值 phantom 的模拟实验和微 Derenzo phantom 实验，其中 phantom 以不同的速度手动移动。在微 Derenzo phantom 的运动校正图像中，慢运动和快运动情况下的棒状物体 FWHM 平均差异为 9.7%。在应用残留运动反卷积后，这一差异缩小到 5.8%。在清醒动物实验中，该方法可以缓解有限采样因子降低空间分辨率以及快速运动和慢速运动动物之间的分辨率差异的问题。运动校正可以通过使用光学跟踪相机测量 PET 扫描过程中受检者的运动来对移动受检者的正电子发射断层扫描（PET）进行校正。然后，使用从跟踪相机获得的运动跟踪数据来校正 PET 图像重建以进行运动校正。由于运动数据的有限时间采样，运动校正后的重建会出现空间分辨率损失。在提出的方法中，从运动跟踪数据中计算出一个空间变分反卷积核，然后使用 Richardson-Lucy 反卷积通过反卷积来校正运动校正的 PET 重建中的有限运动采样的模糊效应。
