Matsuoka S, Shinohara H, Yamamoto T, Niio Y, Shima H, Yamada M, Hasebe S, Uchiyama K, Kuniyasu Y, Takahashi M, Yokoi T
Department of Radiology, Showa University Fujigaoka Hospital.
Nihon Igaku Hoshasen Gakkai Zasshi. 1998 Nov;58(13):751-7.
There are two possible ways to obtain scatter-corrected images with the ML-EM (maximum likelihood expectation maximization) algorithm: one is the subtraction of scatter estimate si from projection data pi, and then (pi-si) is used for scatter-corrected projection data (denoted as SC(T)); the other method is the addition of scatter estimate si to the projections calculated from the reconstructed image without performing data subtraction (SC(E)). This paper investigated these two ML-EM algorithms of combined scatter and attenuation correction on 201Tl myocardial perfusion SPECT imaging. Scatter windows were placed one full width at half maximum (FWHM) below and above the photopeak centerline. The scatter fraction in the primary peak was estimated using trapezoidal approximation by the triple energy window method. Phantom and clinical images were reconstructed using 6 iterations of ordered subsets EM algorithm (OS-EM). A cylindrical phantom with a cold-rod insert and a heart/thorax phantom with liver insert were used to evaluate scatter and the attenuation compensation technique. A cylindrical phantom filled with uniform 201Tl solution was used to evaluate statistical noise. The percent root-mean-square uncertainty (%RMSU) was used as a quantitative measure of noise amplification. %RMSU showed that the SC(E) method amplified noise less in comparison with the SC(T) method, however, no significant difference in image quality was observed between the two methods. In conclusion, both the SC(T) and SC(E) methods provided significant and similar improvement in the removal of scatter in 201Tl myocardial perfusion SPECT imaging.
有两种可能的方法使用最大似然期望最大化(ML-EM)算法来获取散射校正图像:一种是从投影数据pi中减去散射估计值si,然后将(pi - si)用于散射校正投影数据(记为SC(T));另一种方法是在不进行数据减法的情况下,将散射估计值si添加到从重建图像计算得到的投影中(SC(E))。本文研究了这两种用于201Tl心肌灌注单光子发射计算机断层显像(SPECT)成像的联合散射和衰减校正的ML-EM算法。散射窗放置在光电峰中心线上下一个半高宽(FWHM)处。使用三能量窗法通过梯形近似估计主峰中的散射分数。使用有序子集期望最大化算法(OS-EM)进行6次迭代重建体模和临床图像。使用带有冷棒插入物的圆柱形体模和带有肝脏插入物的心脏/胸部体模来评估散射和衰减补偿技术。使用填充均匀201Tl溶液的圆柱形体模来评估统计噪声。均方根不确定性百分比(%RMSU)用作噪声放大的定量度量。%RMSU表明,与SC(T)方法相比,SC(E)方法对噪声的放大较小,然而,两种方法在图像质量上未观察到显著差异。总之,SC(T)和SC(E)方法在去除201Tl心肌灌注SPECT成像中的散射方面均提供了显著且相似的改善。