Norberg Pernilla, Bake Björn, Jacobsson Lars, Carlsson Gudrun Alm, Gustafsson Agnetha
Department of Radiation Physics, Faculty of Health Sciences, Linköping University, Sweden.
Nucl Med Commun. 2007 Dec;28(12):929-36. doi: 10.1097/MNM.0b013e3282f1acac.
In studies of the distribution of lung function, the image quality of lung single photon emission computed tomography (SPECT) is important and one factor influencing it is the reconstruction algorithm.
To systematically evaluate ordered subsets expectation maximization (OSEM) and compare it with filtered back-projection (FBP) for lung SPECT with Tc.
The evaluation of the number of iterations used in OSEM was based on the image quality parameter contrast. The comparison between OSEM and FBP was based on trade-off plots between statistical noise and spatial resolution for different filter parameters, collimators and count-levels. A Monte Carlo technique was used to simulate SPECT studies of a digital thorax phantom containing two sets of activity: one with a homogeneous activity distribution within the lungs and the other with superposed high- and low-activity objects. Statistical noise in the reconstructed images was calculated as the coefficient of variation (CV) and spatial resolution as full width at half-maximum (FWHM).
For the configuration studied, the OSEM reconstruction in combination with post-filtering should be used in lung SPECT studies with at least 60 MLEM equivalent iterations. Compared to FBP the spatial resolution was improved by about 1 mm. For a constant level of CV, a four-fold increase in count level resulted in an increased resolution of about 2 mm. Spatial resolution and cut-off frequency depends on what value of noise in the image is acceptable also increased by using a low-energy, high-resolution collimator for CV values above 3%. The choice of noise-reducing filter and cut-off frequency depends on what value of noise in the image is acceptable.
在肺功能分布研究中，肺部单光子发射计算机断层扫描（SPECT）的图像质量很重要，影响其的一个因素是重建算法。
系统评估有序子集期望最大化（OSEM）算法，并将其与用于锝肺SPECT的滤波反投影（FBP）算法进行比较。
基于图像质量参数对比度评估OSEM算法中使用的迭代次数。OSEM算法与FBP算法的比较基于不同滤波参数、准直器和计数水平下统计噪声与空间分辨率之间的权衡图。采用蒙特卡罗技术模拟对包含两组放射性活度的数字胸部模型进行SPECT研究：一组在肺内具有均匀的放射性活度分布，另一组具有叠加的高、低放射性活度物体。重建图像中的统计噪声计算为变异系数（CV），空间分辨率计算为半高宽（FWHM）。
对于所研究的配置，在肺SPECT研究中应使用结合了后置滤波的OSEM重建算法，且至少进行60次最大似然期望最大化（MLEM）等效迭代。与FBP算法相比，空间分辨率提高了约1毫米。对于恒定的CV水平，计数水平增加四倍会导致分辨率提高约2毫米。空间分辨率和截止频率还取决于图像中可接受的噪声值，对于CV值高于3%的情况，使用低能高分辨率准直器也会提高空间分辨率。降噪滤波器和截止频率的选择取决于图像中可接受的噪声值。
