Time-modified OSEM algorithm for more robust assessment of left ventricular dyssynchrony with phase analysis in ECG-gated myocardial perfusion SPECT

Correlation analysis of epicardial adipose tissue volume quantified by computed tomography images and coronary heart disease under optimized reconstruction algorithm

Objectives:

This paper was aimed to explore the adoption value of low-dose computed tomography (CT) imaging based on optimized ordered subset expectation maximization (OSEM) reconstruction algorithm in the correlation analysis between epicardial adipose tissue (EAT) volume and coronary heart disease (CHD).

Methods:

A total of 110 patients with CHD were selected for CT angiography (CTA) and coronary arteriography (CAG) examinations from October 2017 to October 2019. The predictive value of EAT for CHD was analyzed via receiver operating characteristic (ROC) curve.

Results:

The results showed that the iteration time and error of the improved OSEM reconstruction algorithm were better than that of MLEM algorithm under the same number of iterations. Age, smoking, hypertension, diabetes, and EAT in control group were obviously lower in contrast to those in CHD group (P<0.05). EAT in control group was (124.50±26.72) mL, and EAT in the CHD group was (159.41±38.51) mL. EAT (B=0.023, P=0.003) was an independent risk factor for CHD, which was suggested by Multiple linear regression analysis. Moreover, EAT was a risk factor for CHD, and was positively correlated with the degree and NSCV.

Conclusion:

The optimized OSEM algorithm was used to improve the reconstruction quality of low-dose CT images and used in quantitative measurement of epicardial fat volume. Results showed EAT was an independent risk factor for CHD, and was positively correlated with the number of coronary lesions and Gensini score. It was of great value for the prediction of CHD.

Effect of data conserving respiratory motion compensation on left ventricular functional parameters assessed in gated myocardial perfusion SPECT

Background

Respiratory motion compromises image quality in myocardial perfusion (MP) single-photon emission computed tomography (SPECT) imaging and may affect analysis of left ventricular (LV) functional parameters, including phase analysis-quantified mechanical dyssynchrony parameters. In this paper, we investigate the performance of two algorithms, respiratory blur modeling (RBM) and joint motion-compensated (JMC) ordered-subsets expectation maximization (OSEM), and the effects of motion compensation on cardiac-gated MP-SPECT studies.

Methods

Image acquisitions were carried out with a dual-detector SPECT/CT system in list-mode format. A cardiac phantom was imaged as stationary and under respiratory motion. The images were reconstructed with OSEM, RBM-OSEM, and JMC-OSEM algorithms, and compared in terms of mean squared error (MSE). Subsequently, MP-SPECT data of 19 patients were binned into dual-gated (respiratory and cardiac gating) projection images. The images of the patients were analyzed with Quantitative Gated SPECT (QGS) 2012 program (Cedars-Sinai Medical Center, USA). The parameters of interest were LV volumes, ejection fraction, wall motion, wall thickening, phase analysis, and perfusion parameters.

Results

In phantom experiment, compared to the stationary OSEM reconstruction, the MSE values for OSEM, RBM-OSEM, and JMC-OSEM were 8.5406·10⁻⁵,2.7190·10⁻⁵, and 2.0795·10⁻⁵, respectively. In the analysis of LV function, use of JMC had a small but statistically significant (p < 0.05) effect on several parameters: it increased LV volumes and standard deviation of phase angle histogram, and it decreased ejection fraction, global wall motion, and lateral, septal, and apical perfusion.

Conclusions

Compared to standard OSEM algorithm, RBM-OSEM and JMC-OSEM both improve image quality under motion. Motion compensation has a minor effect on LV functional parameters.

Supplementary Information

The online version contains supplementary material available at (10.1186/s40658-021-00355-w).