Myocardial perfusion (MP) PET imaging plays a key role in risk assessment and stratification of patients with coronary artery disease. In this work, we proposed a patch-based artificial neural network (ANN) fusion approach that integrates information from the ML and the post-smoothed ML reconstruction to improve MP PET imaging. The proposed method was applied to images reconstructed from different noise levels to enhance quantification and task-based MP defect detection. Using the XCAT phantom, we simulated three MP PET imaging cases, one with normal perfusion and the other two with non-transmural and transmural regionally reduced perfusion of the left ventricular (LV) myocardium. The proposed ANN fusion technique was quantitatively evaluated in terms of the noise versus bias and noise versus contrast tradeoff, and compared with the post-smoothed ML reconstruction. Using the channelized Hotelling observer, we evaluated the detectability of the non-transmural and transmural defec...

中文翻译：

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使用人工神经网络改进的心肌灌注PET成像。

心肌灌注（MP）PET成像在冠心病患者的风险评估和分层中起着关键作用。在这项工作中，我们提出了一种基于补丁的人工神经网络（ANN）融合方法，该方法融合了来自ML的信息和平滑后的ML重建，以改善MP PET成像。所提出的方法应用于从不同噪声水平重建的图像，以增强量化和基于任务的MP缺陷检测。使用XCAT幻像，我们模拟了3例MP PET影像学病例，其中1例灌注正常，另2例非左室（LV）心肌的非经壁和经壁区域灌注减少。拟议的ANN融合技术是根据噪声与偏差以及噪声与对比度的权衡进行定量评估的，并与平滑后的ML重建进行比较。我们使用信道化的Hotelling观测器，评估了非透壁和透壁缺陷的可检测性。