Optimizing the frame duration for data-driven rigid motion estimation in brain PET imaging.
Optimizing the frame duration for data-driven rigid motion estimation in brain PET imaging.
复制标题
优化脑PET成像中数据驱动刚性运动估计的帧持续时间。
作者：
用于PET脑成像的数据驱动刚性运动估计通常使用以低时间分辨率采样的数据帧来执行，以减少总体计算时间并在帧中提供足够的信噪比。在最近的工作中，已经证明了超短帧的列表模式重建对于运动估计是足够的，并且可以非常快速地执行。在这项工作中，我们采取的方法，使用基于图像的配准重建的非常短的帧数据驱动的运动估计，并优化了一些重建和配准参数（帧持续时间，MLEM迭代，图像像素大小，后平滑滤波器，参考图像的创建和配准度量），以确保准确的配准，同时最大限度地提高时间分辨率和最小化总计算时间。分析了PET/MR和PET/CT扫描仪的18 F-氟脱氧葡萄糖（FDG）和18 F-氟倍他本（FBB）示踪剂研究的数据，这些研究具有不同的计数率。对于使用各种参数组合的帧重建，模拟帧间运动，并且执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 × 105个真实和散射符合事件，可确保95%的配准精度在地面真实值的1 mm范围内。这对应于0.5 - 2.0的帧持续时间。典型临床PET活性水平为1秒。使用4次无子集的MLEM迭代、4 mm的轴向像素大小、具有4-6 mm半高全宽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，以产生准确配准，同时保持重建和处理时间较短。它表明，非常短的帧（≤ 1秒），可以用来提供准确和快速的数据驱动的刚性运动估计中使用的逐事件的运动校正重建。
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 ultra-short 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 18F-fluorodeoxyglucose (FDG) and 18F-florbetaben (FBB) tracer studies with varying count rates are analysed, for PET/MR and PET/CT scanners. For framed reconstructions using various parameter combinations inter-frame motion is simulated and image-based registrations are performed to estimate that motion. For FDG and FBB tracers using 4 × 105 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 4 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.