Meng L J, Li Nan
Department of Nuclear, Plasma, and Radiological Engineering, the University of Illinois at Urbana Champaign, Urbana-Champaign, IL 61801 USA.
IEEE Trans Nucl Sci. 2009 Oct;5(6):2777-2788. doi: 10.1109/TNS.2009.2024677. Epub 2009 Nov 6.
This paper presents a non-uniform object-space pixelation (NUOP) approach for image reconstruction using the penalized maximum likelihood methods. This method was developed for use with a single photon emission microscope (SPEM) system that offers an ultrahigh spatial resolution for a targeted local region inside mouse brain. In this approach, the object-space is divided with non-uniform pixel sizes, which are chosen adaptively based on object-dependent criteria. These include (a) some known characteristics of a target-region, (b) the associated Fisher Information that measures the weighted correlation between the responses of the system to gamma ray emissions occurred at different spatial locations, and (c) the linear distance from a given location to the target-region. In order to quantify the impact of this non-uniform pixelation approach on image quality, we used the Modified Uniform Cramer-Rao bound (MUCRB) to evaluate the local resolution-variance and bias-variance tradeoffs achievable with different pixelation strategies. As demonstrated in this paper, an efficient object-space pixelation could improve the speed of computation by 1-2 orders of magnitude, whilst maintaining an excellent reconstruction for the target-region. This improvement is crucial for making the SPEM system a practical imaging tool for mouse brain studies. The proposed method also allows rapid computation of the first and second order statistics of reconstructed images using analytical approximations, which is the key for the evaluation of several analytical system performance indices for system design and optimization.
本文提出了一种使用惩罚最大似然法进行图像重建的非均匀目标空间像素化（NUOP）方法。该方法是为与单光子发射显微镜（SPEM）系统配合使用而开发的，该系统可为小鼠脑内的目标局部区域提供超高空间分辨率。在这种方法中，目标空间被划分为大小不一的像素，这些像素是根据与目标相关的标准自适应选择的。这些标准包括：（a）目标区域的一些已知特征；（b）相关的费舍尔信息，它衡量系统对不同空间位置发生的伽马射线发射的响应之间的加权相关性；（c）从给定位置到目标区域的线性距离。为了量化这种非均匀像素化方法对图像质量的影响，我们使用修正的均匀克拉美-罗界（MUCRB）来评估不同像素化策略可实现的局部分辨率方差和偏差方差权衡。如本文所示，一种有效的目标空间像素化可以将计算速度提高1-2个数量级，同时保持对目标区域的出色重建效果。这一改进对于使SPEM系统成为用于小鼠脑研究的实用成像工具至关重要。所提出的方法还允许使用解析近似快速计算重建图像的一阶和二阶统计量，这是评估系统设计和优化的几个解析系统性能指标的关键。
