A novel depth-of-interaction rebinning strategy for ultrahigh resolution PET
A novel depth-of-interaction rebinning strategy for ultrahigh resolution PET
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DOI：
10.1088/1361-6560/aad58c
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发表时间：
2018-08-01
影响因子：
3.5
通讯作者：
Li, Quanzheng
中科院分区：
文献类型：
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作者：
Kim, Kyungsang;Duttah, Joyita;Li, Quanzheng
小动物正电子发射断层扫描(PET)成像通常需要高分辨率(类似于几百微米)，以便能够在动物大脑等小结构中进行准确的定量。最近，我们开发了一个原型的超高分辨率相互作用深度(DOI)PET系统，它使用了探测器像素尺寸为350微米的镉锌Te探测器和8个深度分辨率为250微米的DOI层。由于DOI的大量响应线(LOR)组合，用于重建的系统矩阵是没有DOI的系统矩阵的倍。虽然可以采用高分辨率的虚拟环形几何结构来简化系统矩阵并创建正弦图，但这种正弦图中的LOR往往是稀疏和不规则的，导致重建图像质量的潜在劣化。在本文中，我们提出了一种新的高分辨率正弦图反投影方法，其中采用了均匀的亚采样DOI策略。然而，即使使用高分辨率反弹策略，由于许多高分辨率正弦图像素中的光子计数不足，重建的图像往往非常噪声。为了减少噪声的影响，我们提出了一种带有泊松对数似然和非凸全变差惩罚的惩罚性最大似然重构框架。利用有序子集可分二次代理和乘子交替方向法对优化问题进行求解。为了评估所提出的亚采样方法和惩罚最大似然重建技术的性能，我们进行了模拟和初步的点源实验。通过对不含DOI的正弦图重建的图像和剖面图与重建DOI、重建DOI和亚采样DOI重建的图像和剖面的比较，证明了该方法能够以较低的剂量显著改善图像质量，并获得300微米的高分辨率。
Small animal positron emission tomography (PET) imaging often requires high resolution (similar to few hundred microns) to enable accurate quantitation in small structures such as animal brains. Recently, we have developed a prototype ultrahigh resolution depth-of-interaction (DOI) PET system that uses CdZnTe detectors with a detector pixel size of 350 mu m and eight DOI layers with a 250 mu m depth resolution. Due to the large number of line-of-response (LOR) combinations of DOIs, the system matrix for reconstruction is 64 times larger than that without DOI. While a high resolution virtual ring geometry can be employed to simplify the system matrix and create a sinogram, the LORs in such a sinogram tend to be sparse and irregular, leading to potential degradation of the reconstructed image quality. In this paper, we propose a novel high resolution sinogram rebinning method in which a uniform sub-sampling DOI strategy is employed. However, even with the high resolution rebinning strategy, the reconstructed image tends to be very noisy due to insufficient photon counts in many high resolution sinogram pixels. To reduce noise effects, we developed a penalized maximum likelihood reconstruction framework with the Poisson log-likelihood and a non-convex total variation penalty. Here, an ordered subsets separable quadratic surrogate and alternating direction method of multipliers are utilized to solve the optimization. To evaluate the performance of the proposed sub-sampling method and the penalized maximum likelihood reconstruction technique, we perform simulations and preliminary point source experiments. By comparing the reconstructed images and profiles based on sinograms without DOI, with rebinned DOI and with sub-sampled DOI, we demonstrate that the proposed method with sub-sampled DOIs can significantly improve the image quality with lower dose and yield a high resolution of < 300 mu m.