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A robust segmentation method with triple‐factor non‐negative matrix factorization for myocardial blood flow quantification from dynamic 82 Rb positron emission tomography
Author(s) -
Liu Hui,
Wu Jing,
Sun JingYi,
Wu TungHsin,
FazzoneChettiar Ramesh,
Thorn Stephanie,
Sinusas Albert J.,
Liu YiHwa
Publication year - 2019
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1002/mp.13783
Subject(s) - positron emission tomography , segmentation , blood flow , nuclear medicine , imaging phantom , cardiac pet , myocardial perfusion imaging , reproducibility , perfusion , biomedical engineering , artificial intelligence , computer science , mathematics , medicine , radiology , statistics
Purpose In this work, we proposed a triple‐factor non‐negative matrix factorization (TNMF) method to semiautomatically 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). Methods The proposed TNMF method was evaluated using NCAT phantom simulation with three noise levels. The segmented ROIs, time–activity curves (TACs), and K 1 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 interoperator variations of MBF and myocardial flow reserve (MFR) were compared between the two methods. Results Our 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 K 1 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 (R 2 ≥ 0.964). The interoperator variations of MBF and MFR were markedly reduced using the TNMF method. Conclusions In conclusion, the TNMF method is highly feasible for semiautomatic segmentation of the LV cavity and myocardium, with the potential to improve the precision of MBF quantification by improving segmentation.