A Robust Segmentation Method with Triple-factor Non-Negative Matrix Factorization for Myocardial Blood Flow Quantification from Dynamic 82 Rb Positron Emission Tomography.

Abstract

PURPOSE\nIn this work, we proposed a triple-factor non-negative matrix factorization (TNMF) method to semi-automatically segment the regions of interest (ROIs) of the left ventricular (LV) cavity and myocardium to improve the reproducibility of myocardial blood flow (MBF) quantification from dynamic 82 Rb positron emission tomography (PET).\n\n\nMETHODS\nThe proposed TNMF method was evaluated using NCAT phantom simulation with three noise levels. The segmented ROIs, time activity curves (TACs) and K1 derived from the TNMF method were compared with the ground truth simulated. The TNMF method was further evaluated in two patients each undergone both rest and stress 82 Rb PET studies. The TNMF and manual segmentations were implemented by two different observers, and the inter-operator variations of MBF and myocardial flow reserve (MFR) were compared between the two methods.\n\n\nRESULTS\nOur simulation results showed that the TNMF method for dynamic PET image segmentation was robust as evidenced by the high Dice similarity coefficient, regardless of high or low count level. The relative bias in K1 estimation was less than 1%. Our patient results also showed that reasonable ROIs for the LV cavity and myocardium could be obtained precisely for patients with and without myocardial perfusion defects. The TACs derived from the TNMF method were highly correlated with those obtained with the manual method (R2 ≥ 0.964). The inter-operator variations of MBF and MFR were markedly reduced using the TNMF method. Conclusions In conclusion, the TNMF method is highly feasible for semi-automatic segmentation of the LV cavity and myocardium, with the potential to improve the precision of MBF quantification by improving segmentation. This article is protected by copyright. All rights reserved.