Theoretical and Numerical Study of MLEM and OSEM Reconstruction Algorithms for Motion Correction in Emission Tomography
Theoretical and Numerical Study of MLEM and OSEM Reconstruction Algorithms for Motion Correction in Emission Tomography
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DOI：
10.1109/tns.2009.2021765
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发表时间：
2009-10-01
影响因子：
1.8
通讯作者：
King, Michael A.
中科院分区：
文献类型：
--
作者：
Dey, Joyoni;King, Michael A.
患者的身体运动和自主运动影响心脏SPECT和PET灌注图像的图像质量。在文献中存在几种算法来校正迭代最大似然重建框架内的运动。在这项工作中。从泊松统计开始导出三种算法以校正患者运动。第一种是运动补偿MLEM算法（MC-MLEM）。接下来的两个算法称为MGEM-1和MGEM-2（运动门控OSEM的缩写，1和2），以两种不同的方式使用运动状态作为子集。实验进行了NCAT幻影（与确切知道的运动）作为源和衰减分布。还在拟人体模和患者研究中进行了实验。使用SIMIND Monte Carlo模拟软件创建NCAT体模的SPECT投影图像。然后修改投影图像，使其泊松噪声水平与临床采集的泊松噪声水平相等。我们研究了这些算法的应用，以纠正（1）1个大的身体运动的2厘米，在前后（SI）和前后（AP）的方向，（2）呼吸运动的2厘米，在SI和0.6厘米，在AP。我们确定了无噪声重建的NCAT幻影活动的偏差以及噪声重建的偏差方差。MGEM-1比MC-MLEIM更快地沿偏倚-方差曲线前进沿着。与文件MC-MLEM算法相比，MCEM-1还通过迭代更快地降低了无噪声偏差（相对于NCAT真值），正如子集算法所预期的那样。对于具有两个运动状态的身体运动校正，在第9次迭代之后，Was接近于MC-MLEM在迭代17处的Was，从而将迭代次数减少了1.89倍。对于具有9个运动状态的呼吸运动校正，基于无噪声偏置，迭代减少因子约为7。对于MGEM-2来说。偏差图或偏差方差图由于连续的插值误差而被迭代饱和。SPECT数据采集模拟呼吸运动的幅度为2厘米的拟人化的幻影。还采集了在第二次休息时进行身体运动的患者研究。将运动校正应用于这些具有拟人化体模和患者研究的采集，显示出使用估计的运动校正的图像质量的显著改善。
Patient body-motion and respiratory-motion impacts the image quality of cardiac SPECT and PET perfusion images. Several algorithms exist in the literature to correct for motion within the iterative maximum-likelihood reconstruction framework. In this work. three algorithms are derived starting with Poisson statistics to correct for patient motion. The first one is a motion compensated MLEM algorithm (MC-MLEM). The next two algorithms called MGEM-1 and MGEM-2 (short for Motion Gated OSEM, 1 and 2) use the motion states as subsets, in two different ways. Experiments were performed with NCAT phantoms (with exactly known motion) as the source and attenuation distributions. Experiments were also performed on in anthropomorphic phantom and a patient study. The SIMIND Monte Carlo simulation software was used to create SPECT projection images of the NCAT phantoms. The projection images were then modified to have Poisson noise levels equivalent to that of clinical acquisition. We investigated application of these algorithms to correction of (1) 1 large body-motion of 2 cm in Superior-Inferior (SI) and Anterior-Posterior (AP) directions each and (2) respiratory motion of 2 cm in SI and 0.6 cm in AP. We determined the bias with respect to the NCAT phantom activity for noiseless reconstructions as well as the bias-variance for noisy reconstructions. The MGEM-1 advanced along the bias-variance curve faster than the MC-MLEIM with iterations. The MCEM-1 also lowered the noiseless bias (with respect to NCAT truth) faster with iterations, compared to file MC-MLEM algorithms, as expected with subset algorithms. For the body motion correction with two motion states, after the 9th iteration the Was was close to that of MC-MLEM at iteration 17, reducing the number of iterations by a factor of 1.89. For the respiratory motion correction with 9 motion states, based on the noiseless bias, the iteration reduction factor was approximately 7. For the MGEM-2, however. bias-plot or the bias-variance-plot saturated with iteration because of successive interpolation error. SPECT data was acquired simulating respiratory motion of 2 cm amplitude with an anthropomorphic phantom. A patient study acquired with body motion in a second rest was also acquired. The motion correction was applied to these acquisitions with the anthropomorphic phantom and the patient study, showing marked improvements of image quality with the estimated motion correction.