Department of Radiology, University of Wisconsin, Madison, WI, USA.
PET/MR Engineering, GE Healthcare, Waukesha, WI, USA.
Med Phys. 2021 Jun;48(6):3031-3041. doi: 10.1002/mp.14889. Epub 2021 May 14.
Data-driven rigid motion estimation for PET brain imaging is usually performed using data frames sampled at low temporal resolution to reduce the overall computation time and to provide adequate signal-to-noise ratio in the frames. In recent work it has been demonstrated that list-mode reconstructions of ultrashort frames are sufficient for motion estimation and can be performed very quickly. In this work we take the approach of using image-based registration of reconstructions of very short frames for data-driven motion estimation, and optimize a number of reconstruction and registration parameters (frame duration, MLEM iterations, image pixel size, post-smoothing filter, reference image creation, and registration metric) to ensure accurate registrations while maximizing temporal resolution and minimizing total computation time.
Data from F-fluorodeoxyglucose (FDG) and F-florbetaben (FBB) tracer studies with varying count rates are analyzed, for PET/MR and PET/CT scanners. For framed reconstructions using various parameter combinations interframe motion is simulated and image-based registrations are performed to estimate that motion.
For FDG and FBB tracers using 4 × 10 true and scattered coincidence events per frame ensures that 95% of the registrations will be accurate to within 1 mm of the ground truth. This corresponds to a frame duration of 0.5-1 sec for typical clinical PET activity levels. Using four MLEM iterations with no subsets, a transaxial pixel size of 4 mm, a post-smoothing filter with 4-6 mm full width at half maximum, and averaging two or more frames to create the reference image provides an optimal set of parameters to produce accurate registrations while keeping the reconstruction and processing time low.
It is shown that very short frames (≤1 sec) can be used to provide accurate and quick data-driven rigid motion estimates for use in an event-by-event motion corrected reconstruction.
用于 PET 脑成像的基于数据的刚体运动估计通常使用低时间分辨率采样的数据帧来执行，以减少总计算时间并在帧中提供足够的信噪比。在最近的工作中已经证明，超短帧的列表模式重建足以进行运动估计，并且可以非常快速地进行。在这项工作中，我们采用基于图像的非常短帧重建的配准方法进行数据驱动的运动估计，并优化了许多重建和配准参数（帧持续时间、MLEM 迭代次数、图像像素大小、后平滑滤波器、参考图像创建和配准度量），以确保准确配准，同时最大限度地提高时间分辨率并最小化总计算时间。
分析了使用不同计数率的 F-氟脱氧葡萄糖（FDG）和 F-氟硼替苯（FBB）示踪剂的 PET/MR 和 PET/CT 扫描仪的数据。对于使用各种参数组合的帧重建，模拟帧间运动，并进行基于图像的配准以估计该运动。
对于 FDG 和 FBB 示踪剂，每帧使用 4×10 个真实和散射符合事件确保 95%的配准将准确到地面真实值的 1 毫米以内。这对应于典型临床 PET 活动水平的 0.5-1 秒的帧持续时间。使用四个无子集的 MLEM 迭代、4 毫米的横向像素大小、4-6 毫米 FWHM 的后平滑滤波器以及平均两个或更多帧以创建参考图像提供了一组最佳参数，以在保持低重建和处理时间的同时产生准确的配准。
结果表明，非常短的帧（≤1 秒）可用于提供准确且快速的数据驱动刚体运动估计，用于事件驱动的运动校正重建。
