UIH America Inc., Houston TX, 77479, United States of America.
Phys Med Biol. 2018 Aug 10;63(16):165010. doi: 10.1088/1361-6560/aac71b.
In conventional gating approaches for positron emission tomography (PET), a single number of gates is predetermined for the whole field of view (FOV) regardless of spatially variant motion blurring effects, which compromises image quality by under-gating regions of large motion and over-gating static regions. To achieve the best resolution-noise trade-off for the whole FOV, we proposed a new approach that incorporates a spatially variant number of gates into gated image reconstruction. The first step was to estimate the motion amplitude of each spatial location. A preliminary set of gated image reconstructions was generated from the PET data. The spatially variant motion amplitudes were approximated based on the registration of 2D maximum intensity projections of the gated reconstructions as well as prior knowledge. Second, the spatially varying motion amplitudes were used to determine the optimal number of gates for each region. Finally, the adaptive gating image reconstruction algorithm that incorporates a gating transform function to model the spatially variant number of gates was applied to generate adaptively gated 4D images. Scans from large FOV systems were simulated using actual multi-bed patient data from a clinical scanner for evaluation purposes. Images reconstructed with the conventional gating scheme as well as static reconstruction were obtained for comparison with the results obtained using our new method. In areas with lower estimated motion amplitudes (such as the spine), the reconstructed images using the new approach showed reduced noise compared to images with conventional gated reconstructions and comparable quality with non-gated images. In areas with large estimated motion amplitudes, such as in the lung and liver, contrast and resolution of images using the new method and conventional gated-reconstructions were comparable, and both were higher than those of non-gated images. The results indicate that using a locally adaptive number of gates based on respiratory motion amplitude instead of a fixed number of gates can improve the statistics of gated PET images by optimizing the local noise-resolution trade-off.
在正电子发射断层扫描（PET）的传统门控方法中，整个视野（FOV）的门数量是预先确定的，无论运动模糊效应的空间变化如何，这会通过对大运动区域的欠门控和对静态区域的过度门控来降低图像质量。为了实现整个 FOV 的最佳分辨率-噪声折衷，我们提出了一种新方法，即将空间变化的门数量纳入门控图像重建中。第一步是估计每个空间位置的运动幅度。从 PET 数据生成初步的一组门控图像重建。基于门控重建的 2D 最大强度投影的配准以及先验知识，对空间变化的运动幅度进行了近似。其次，使用空间变化的运动幅度来确定每个区域的最佳门数量。最后，应用自适应门控图像重建算法，该算法使用门控变换函数来模拟空间变化的门数量，以生成自适应门控 4D 图像。使用实际来自临床扫描仪的多床位患者数据模拟大 FOV 系统的扫描，以便进行评估。获得了使用常规门控方案以及静态重建的图像，以与使用我们的新方法获得的结果进行比较。在运动幅度估计较低的区域（如脊柱），与常规门控重建图像相比，新方法重建的图像噪声降低，与非门控图像质量相当。在运动幅度估计较大的区域，如肺部和肝脏，新方法和常规门控重建图像的对比度和分辨率相当，均高于非门控图像。结果表明，使用基于呼吸运动幅度的局部自适应门数量而不是固定门数量可以通过优化局部噪声-分辨率折衷来改善门控 PET 图像的统计数据。
