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Optimization of bone SPECT image reconstruction based on a hybrid framework

机译：基于混合框架的骨骼SPECT图像重建优化

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Ordered subset expectation maximization (OSEM) is one of the most widely used reconstruction algorithm for Singlephoton emission computed tomography (SPECT) images reconstruction because of their efficiency in providing a better image quality. However, by increasing the number of subsets of this method, the convergence of this algorithm is speeded which can lead to undesirable noise levels amplification and inaccurate detection of lesion in low activity image regions. This paper presents a new algorithm for bone SPECT image reconstruction based on ordered subset expectation maximization (OSEM) algorithms and can remove the noise from images with the best degree of accuracy. In our proposed method, a de-noising pre-processing Butterworth filter is applied on the projections followed by OSEM algorithm to reconstruct 128 axial slices from a 128 enhanced sinograms, and finally we extract the coronal and sagittal slices from the enhanced axial slices volume. Our method was compared to Maximum Likelihood Expectation Maximization (MLEM) and OSEM techniques used only. Each method was tested on a three dimensional Shepp-Logan phantom and a bone SPECT database and evaluated qualitatively and quantitatively. The results show that the proposed method kept quantitative accuracy with preservation of the singularity and exhibited lower noise in low-activity regions while achieving high-resolution recovery in structures with high activity uptake in comparison to other methods.
机译：有序子集期望最大化（OSEM）是单光子发射计算机断层扫描（SPECT）图像重建中使用最广泛的重建算法之一，因为它可以提供更好的图像质量。但是，通过增加此方法的子集数量，可以加快此算法的收敛速度，这可能会导致不希望的噪声水平放大和低活动图像区域中病变的不准确检测。本文提出了一种基于有序子集期望最大化（OSEM）算法的骨SPECT图像重建新算法，该算法可以以最高的准确度去除图像中的噪声。在我们提出的方法中，在投影上应用去噪预处理的巴特沃斯滤波器，然后使用OSEM算法从128个增强的正弦图中重建128个轴向切片，最后从增强的轴向切片体积中提取冠状和矢状切片。将我们的方法与仅使用的最大似然期望最大化（MLEM）和OSEM技术进行了比较。每种方法都在三维Shepp-Logan体模和骨骼SPECT数据库上进行了测试，并进行了定性和定量评估。结果表明，与其他方法相比，该方法在保留奇异性的同时保持定量精度，并且在低活性区域显示较低的噪声，同时在具有高活性吸收的结构中实现了高分辨率的恢复。

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