TOF-PET Image Reconstruction With Multiple Timing Kernels Applied on Cherenkov Radiation in BGO.
TOF-PET Image Reconstruction With Multiple Timing Kernels Applied on Cherenkov Radiation in BGO.
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DOI：
10.1109/trpms.2020.3048642
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发表时间：
2020-09
影响因子：
4.4
通讯作者：
Pizzichemi M
中科院分区：
文献类型：
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作者：
Efthimiou N;Kratochwil N;Gundacker S;Polesel A;Salomoni M;Auffray E;Pizzichemi M
如今，PET扫描仪中的飞行时间（TOF）假设所有事件都有一个明确定义的时间分辨率。然而，最近的BGO-Cherenkov探测器，结合提示Cherenkov发射和典型的BGO闪烁，可以分类事件到多个定时内核，最好的高斯混合模型描述。每个事件检测到的切伦科夫光子的数量直接影响检测器时间分辨率和信号上升时间，其随后可以用于提高符合定时分辨率。这项工作提出了一个模拟工具包，它适用于多个时间蔓延的重合事件和图像重建，将这些信息。一个完整的圆柱BGO-Cherenkov PET模型进行了比较，在对比度恢复和对比度噪声比方面，对LYSO模型的时间分辨率为213 ps。测试了两种混合核的重建方法：1）混合高斯核和2）分解简单高斯核。分解模型使用模拟期间应用的精确混合物组分。使用混合核重建的图像提供了类似的平均值和更少的噪声比分解。然而，通常需要更多的迭代。类似地，具有单个TOF内核的LYSO模型比具有多个内核的BGO-Cherenkov收敛得更快。结果表明，模型的复杂性减缓收敛。然而，由于更高的灵敏度，BGO模型的对比度噪声比好26.4%。
Today Time-of-Flight (TOF), in PET scanners, assumes a single, well-defined timing resolution for all events. However, recent BGO–Cherenkov detectors, combining prompt Cherenkov emission and the typical BGO scintillation, can sort events into multiple timing kernels, best described by the Gaussian mixture models. The number of Cherenkov photons detected per event impacts directly the detector time resolution and signal rise time, which can later be used to improve the coincidence timing resolution. This work presents a simulation toolkit which applies multiple timing spreads on the coincident events and an image reconstruction that incorporates this information. A full cylindrical BGO–Cherenkov PET model was compared, in terms of contrast recovery and contrast-to-noise ratio, against an LYSO model with a time resolution of 213 ps. Two reconstruction approaches for the mixture kernels were tested: 1) mixture Gaussian and 2) decomposed simple Gaussian kernels. The decomposed model used the exact mixture component applied during the simulation. Images reconstructed using mixture kernels provided similar mean value and less noise than the decomposed. However, typically, more iterations were needed. Similarly, the LYSO model, with a single TOF kernel, converged faster than the BGO–Cherenkov with multiple kernels. The results indicate that the model complexity slows down convergence. However, due to the higher sensitivity, the contrast-to-noise ratio was 26.4% better for the BGO model.