A robust segmentation method with triple-factor non-negative matrix factorization for myocardial blood flow
Hui Liu1, Jing Wu2, Jing-Yi Sun3
1Department of Internal Medicine (Cardiology), Yale University, New Haven, CT, 06520, USA.
Medical Physics
|August 25, 2019
Summary
A new triple-factor non-negative matrix factorization (TNMF) method accurately segments cardiac regions in dynamic 82Rb PET scans. This approach improves myocardial blood flow (MBF) quantification reproducibility.
Area of Science:
- Medical Imaging
- Nuclear Medicine
- Image Processing
Background:
- Myocardial blood flow (MBF) quantification using dynamic 82Rb PET is crucial for diagnosing coronary artery disease.
- Accurate segmentation of the left ventricular (LV) cavity and myocardium is essential for reliable MBF and myocardial flow reserve (MFR) calculations.
- Current manual segmentation methods can be time-consuming and prone to interoperator variability.
Purpose of the Study:
- To develop and evaluate a triple-factor non-negative matrix factorization (TNMF) method for semiautomatic segmentation of LV cavity and myocardium.
- To assess the impact of TNMF on the reproducibility of MBF quantification from dynamic 82Rb PET.
- To compare TNMF-based segmentation with manual segmentation in terms of accuracy and interoperator variability.
Main Methods:
- A TNMF algorithm was developed for semiautomatic segmentation of cardiac ROIs in dynamic 82Rb PET.
- The TNMF method was validated using NCAT phantom simulations at varying noise levels.
- Patient data from rest and stress 82Rb PET studies were used to compare TNMF with manual segmentation performed by two observers.
Main Results:
- TNMF demonstrated robust segmentation performance in simulations, achieving high Dice similarity coefficients across different noise levels.
- K1 estimation showed a relative bias of less than 1% in simulations.
- Patient results showed precise LV cavity and myocardium segmentation, highly correlated time-activity curves (R2 ≥ 0.964), and markedly reduced interoperator variability for MBF and MFR.
Conclusions:
- The TNMF method is a feasible and effective tool for semiautomatic segmentation of cardiac structures in dynamic 82Rb PET.
- TNMF has the potential to significantly improve the precision and reproducibility of MBF quantification.
- This method offers a promising advancement for clinical applications of 82Rb PET imaging.
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