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Noise reduction for Multi-Harmonic Phase Analysis of gated SPECT myocardial perfusion imaging

机译:门控SPECT心肌灌注成像多谐波相位分析的降噪

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Multi-Harmonic Phase Analysis (MHPA) has been developed for heart failure prognosis by reliably measuring left-ventricular (LV) dyssynchrony from conventional gated single photon emission tomography (GSPECT) myocardial perfusion imaging (MPI). Although MHPA has been evaluated in multiple clinical studies, its accuracy can be limited under highly noisy conditions. The purpose of this study is to develop a 4D postreconstruction method for suppressing noise in GSPECT MPI data. SPECT images acquired at different temporal points were first registered to the same point. By summing the registered images, the uncorrelated noise was greatly reduced and the myocardial signals were strengthened. The same process then repeated for different temporal points. A deformable registration with a simulated annealing optimization scheme was implemented in this work. The proposed method was evaluated using a simulation study on an extended cardiac torso phantom (XCAT). The projection data were first calculated analytically and Poisson noise was added with a level matching that in clinical data. An ordered subsets expectation maximization (OSEM) algorithm was used in the reconstruction. The comparison of SPECT images without versus with the noise reduction showed that the proposed noise reduction method increased the signal-to-noise ratio (SNR) by an average factor of ∼2.3. Our approach was able to substantially reduce image noise without losing faithful information of myocardial activities. The next step is to use this method to reduce noise in clinical GSPECT MPI data to improve the accuracy of MHPA, especially in a region with severe myocardial scar.
机译:通过可靠地测量常规门控单光子发射断层扫描(GSPECT)心肌灌注成像(MPI)的左心室(LV)不同步,开发了多谐波相位分析(MHPA)用于心力衰竭的预后。尽管已在多个临床研究中对MHPA进行了评估,但在高度嘈杂的条件下,其准确性可能受到限制。这项研究的目的是开发一种用于抑制GSPECT MPI数据中噪声的4D后重建方法。首先将在不同时间点获取的SPECT图像配准到同一点。通过对注册图像进行求和,可以大大减少不相关的噪声,并增强心肌信号。然后针对不同的时间点重复相同的过程。在这项工作中实现了具有模拟退火优化方案的可变形配准。通过对扩展的心脏躯干体模(XCAT)进行仿真研究,对提出的方法进行了评估。首先通过分析计算投影数据,然后添加与临床数据相匹配的水平的泊松噪声。重构中使用了有序的子集期望最大化(OSEM)算法。比较不带降噪的SPECT图像与带降噪的SPECT图像的比较表明,所提出的降噪方法将信噪比(SNR)提高了约2.3的平均因子。我们的方法能够在不丢失真实的心肌活动信息的情况下大幅降低图像噪声。下一步是使用此方法来减少临床GSPECT MPI数据中的噪声,以提高MHPA的准确性,尤其是在心肌严重瘢痕区域。

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