Improved myocardial perfusion PET imaging using artificial neural networks

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 defects through the receiver operating characteristic analysis. The quantitative results demonstrated that the ANN enhancement method reduced bias and improved contrast while reaching comparable noise to what the post-smoothed ML reconstruction achieved. Moreover, the ANN fusion technique significantly improved the defect detectability of both the non-transmural and transmural defects. In addition to the simulation study, we further evaluated the proposed method using patient data. Compared with the post-smoothed ML reconstruction, the ANN fusion improved the tradeoff between noise and the mean value on the LV myocardium, indicating its potential clinical application in MP PET imaging.