Lee Seungeun, Lee Jae Sung
Department of Nuclear Medicine, Seoul National University, Seoul, 03080, Republic of Korea.
Department of Biomedical Sciences, Seoul National University, Seoul, 03080, Republic of Korea.
Phys Med Biol. 2023 Apr 26;68(9). doi: 10.1088/1361-6560/accacb.
. One major limiting factor for achieving high resolution of positron emission tomography (PET) is a Compton scattering of the photon within the crystal, also known as inter-crystal scattering (ICS). We proposed and evaluated a convolutional neural network (CNN) named ICS-Net to recover ICS in light-sharing detectors for real implementations preceded by simulations. ICS-Net was designed to estimate the first-interacted row or column individually from the 8 × 8 photosensor amplitudes.. We tested 8 × 8, 12 × 12, and 21 × 21 LuSiOarrays with pitches of 3.2, 2.1, and 1.2 mm, respectively. We first performed simulations to measure the accuracies and error distances, comparing the results to previously studied pencil-beam-based CNN to investigate the rationality of implementing fan-beam-based ICS-Net. For experimental implementation, the training dataset was prepared by obtaining coincidences between the targeted row or column of the detector and a slab crystal on a reference detector. ICS-Net was applied to the detector pair measurements with moving a point source from the edge to center using automated stage to evaluate their intrinsic resolutions. We finally assessed the spatial resolution of the PET ring.. The simulation results showed that ICS-Net improved the accuracy compared with the case without recovery, reducing the error distance. ICS-Net outperformed a pencil-beam CNN, which provided a rationale to implement a simplified fan-beam irradiation. With the experimentally trained ICS-Net, the degree of improvements in intrinsic resolutions were 20%, 31%, and 62% for the 8 × 8, 12 × 12, and 21 × 21 arrays, respectively. The impact was also shown in the ring acquisitions, achieving improvements of 11%-46%, 33%-50%, and 47%-64% (values differed from the radial offset) in volume resolutions of 8 × 8, 12 × 12, and 21 × 21 arrays, respectively.. The experimental results demonstrate that ICS-Net can effectively improve the image quality of high-resolution PET using a small crystal pitch, requiring a simplified setup for training dataset acquisition.
实现正电子发射断层扫描（PET）高分辨率的一个主要限制因素是光子在晶体内部的康普顿散射，也称为晶体间散射（ICS）。我们提出并评估了一种名为ICS-Net的卷积神经网络（CNN），用于在光共享探测器中恢复ICS，在模拟之前进行实际应用。ICS-Net旨在根据8×8光电传感器的幅度分别估计首次相互作用的行或列。我们分别测试了间距为3.2、2.1和1.2毫米的8×8、12×12和21×21的硅酸镥阵列。我们首先进行模拟以测量精度和误差距离，将结果与先前研究的基于铅笔束的CNN进行比较，以研究实施基于扇形束的ICS-Net的合理性。对于实验实施，通过获取探测器的目标行或列与参考探测器上的平板晶体之间的符合事件来准备训练数据集。使用自动平台将点源从边缘移动到中心，将ICS-Net应用于探测器对测量，以评估其固有分辨率。我们最终评估了PET环的空间分辨率。模拟结果表明，与未恢复的情况相比，ICS-Net提高了精度，减小了误差距离。ICS-Net优于铅笔束CNN，这为实施简化的扇形束照射提供了理论依据。使用经过实验训练的ICS-Net，8×8、12×12和21×21阵列的固有分辨率分别提高了20%、31%和62%。在环采集方面也显示出了这种影响，8×8、12×12和21×21阵列的体积分辨率分别提高了11%-46%、33%-50%和47%-64%（值因径向偏移而异）。实验结果表明，ICS-Net可以使用小晶体间距有效地提高高分辨率PET的图像质量，并且在获取训练数据集时需要简化的设置。
