Li Yusheng, Matej Samuel, Metzler Scott D
Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104.
Med Phys. 2014 Dec;41(12):121912. doi: 10.1118/1.4901552.
Spatial resolution in positron emission tomography (PET) is still a limiting factor in many imaging applications. To improve the spatial resolution for an existing scanner with fixed crystal sizes, mechanical movements such as scanner wobbling and object shifting have been considered for PET systems. Multiple acquisitions from different positions can provide complementary information and increased spatial sampling. The objective of this paper is to explore an efficient and useful reconstruction framework to reconstruct super-resolution images from super-sampled low-resolution data sets.
The authors introduce a super-sampling data acquisition model based on the physical processes with tomographic, downsampling, and shifting matrices as its building blocks. Based on the model, we extend the MLEM and Landweber algorithms to reconstruct images from super-sampled data sets. The authors also derive a backprojection-filtration-like (BPF-like) method for the super-sampling reconstruction. Furthermore, they explore variant methods for super-sampling reconstructions: the separate super-sampling resolution-modeling reconstruction and the reconstruction without downsampling to further improve image quality at the cost of more computation. The authors use simulated reconstruction of a resolution phantom to evaluate the three types of algorithms with different super-samplings at different count levels.
Contrast recovery coefficient (CRC) versus background variability, as an image-quality metric, is calculated at each iteration for all reconstructions. The authors observe that all three algorithms can significantly and consistently achieve increased CRCs at fixed background variability and reduce background artifacts with super-sampled data sets at the same count levels. For the same super-sampled data sets, the MLEM method achieves better image quality than the Landweber method, which in turn achieves better image quality than the BPF-like method. The authors also demonstrate that the reconstructions from super-sampled data sets using a fine system matrix yield improved image quality compared to the reconstructions using a coarse system matrix. Super-sampling reconstructions with different count levels showed that the more spatial-resolution improvement can be obtained with higher count at a larger iteration number.
The authors developed a super-sampling reconstruction framework that can reconstruct super-resolution images using the super-sampling data sets simultaneously with known acquisition motion. The super-sampling PET acquisition using the proposed algorithms provides an effective and economic way to improve image quality for PET imaging, which has an important implication in preclinical and clinical region-of-interest PET imaging applications.
在许多成像应用中，正电子发射断层扫描（PET）的空间分辨率仍是一个限制因素。为了提高现有晶体尺寸固定的扫描仪的空间分辨率，PET系统已考虑采用诸如扫描仪摆动和物体移动等机械运动。从不同位置进行多次采集可以提供互补信息并增加空间采样。本文的目的是探索一种高效且有用的重建框架，以便从超采样的低分辨率数据集中重建超分辨率图像。
作者引入了一种基于物理过程的超采样数据采集模型，该模型以断层矩阵、下采样矩阵和移位矩阵为构建模块。基于该模型，我们扩展了最大似然期望最大化（MLEM）算法和兰道韦伯（Landweber）算法，以从超采样数据集中重建图像。作者还推导了一种类似反投影滤波（BPF）的超采样重建方法。此外，他们探索了超采样重建的变体方法：单独的超采样分辨率建模重建和无下采样重建，以在增加计算量的代价下进一步提高图像质量。作者使用分辨率体模的模拟重建来评估在不同计数水平下具有不同超采样的三种算法类型。
对于所有重建，在每次迭代时计算对比度恢复系数（CRC）与背景变化率的关系，以此作为图像质量指标。作者观察到，在相同计数水平下，所有三种算法在固定背景变化率时都能显著且一致地提高CRC，并减少超采样数据集的背景伪影。对于相同的超采样数据集，MLEM方法比Landweber方法实现了更好的图像质量，而Landweber方法又比类似BPF的方法实现了更好的图像质量。作者还证明，与使用粗系统矩阵的重建相比，使用精细系统矩阵从超采样数据集中进行的重建产生了更高的图像质量。不同计数水平的超采样重建表明，在更大的迭代次数下，更高的计数可以获得更多的空间分辨率提升。
作者开发了一种超采样重建框架，该框架可以使用已知采集运动的超采样数据集同时重建超分辨率图像。使用所提出算法的超采样PET采集为提高PET成像的图像质量提供了一种有效且经济的方法，这在临床前和临床感兴趣区域PET成像应用中具有重要意义。
