Volume-time curve of cardiac magnetic resonance assessed left ventricular dysfunction in coronary artery disease patients with type 2 diabetes mellitus

Abstract

BackgroundType 2 diabetes mellitus (DM2) may induce epicardial coronary artery diseases and left ventricular myocardial damaging as well. Left ventricular dysfunction was found in DM2. In this research, we compared the left ventricular dysfunction of coronary artery disease (CAD) patients with and without type 2 diabetes mellitus as well as normal controls using the volume-time curve of cardiac magnetic resonance (CMR).MethodsSixty-one CAD patients (28 with DM2 and 33 without DM2) and 18 normal individuals were enrolled in this study. Left ventricular function parameters, including the end-diastolic and end-systolic volumes (EDV, ESV), stroke volume (SV) and ejection fraction (EF), and morphologic dimension parameters (end diastolic and systolic diameter (EDD and ESD), were measured and compared. Volume-time curve parameters, including the peak ejection rate (PER), peak ejection time (PET), peak filling rate (PFR), peak filling time from ES (PFT), peak ejection rate normalized to EDV (PER/EDV), and peak filling rate normalized to EDV (PFR/EDV), were derived automatically and compared.ResultsLVEF in the diabetic CAD group was markedly reduced when compared to the normal and CAD without DM2 groups (all p < 0.05). LVEDD of the diabetic CAD group was significantly enlarged compared to the normal and non-diabetic CAD groups (all p < 0.05). More importantly, the lowest parameters of the left ventricle volume time curve (i.e., PER, PFR, PER/EDV and PFR/EDV) were obtained in diabetic CAD patients (all p < 0.05). In diabetic CAD patients, logistic regression analysis indicated that PET, PFT and PFR/EDV were independent predictors of left ventricular dysfunction (odds ratio [OR]: 1.1208, 1.0161, and 0.0139, respectively). The sensitivity and specificity of PET were 81.2 and 90%, respectively, when the threshold value was greater than 164.4 msec; for PFT, the sensitivity and specificity were 87.5 and 95.0%, respectively (criterion >166.0 msec). Higher sensitivity (87.5%) and specificity (100.0%) were obtained for PFR/EDV (criterion ≤3.7EDV/s).ConclusionsParameters that are derived from the volume-time curve on CMR, including PET, PFT and PFR/EDV, allow clinicians to predict left ventricular dysfunction in diabetic CAD subjects with a high degree of sensitivity and specificity.

Early longitudinal changes in left ventricular function and morphology in diabetic pigs: evaluation by 3.0T magnetic resonance imaging

“…Cardiac magnetic resonance (CMR), which is considered the gold standard for evaluating the function and structure of the heart, provides accurate and reproducible measures of cardiac blood flow and morphology characteristics. The time‒volume curve and feature tracking acquired from conventional CMR cine imaging are noninvasive methods for evaluating cardiac systolic and diastolic function, and have been widely used in scientific research [ 5 – 7 ].…”

Cardiac magnetic resonance feature tracking for quantifying right ventricular deformation in type 2 diabetes mellitus patients

“…The favorable effect of healthy diet and physical activity on RV mechanics indicates that RV myocardial abnormalities are probably modifiable through adequate interventional strategies 11 . Most previous studies on myocardial dysfunction in diabetes have paid more attention to the LV 12,13 . The role of the RV in diabetic cardiomyopathy is still under-investigated.…”

Efficient and Phase-aware Video Super-resolution for Cardiac MRI

“…To well consider the temporal information, we choose ConvL-STM [28], which has been proven effective [6,9], as our backbone. Moreover, we introduce the domain knowledge (i.e., cardiac phase), which has shown to be important for the measurement of the stroke volume [15] and disease diagnosis [29], to provide the direct guidance about the temporal relationship in a cardiac cycle. Combined with the proposed phase fusion module, the model can better utilize the temporal information.…”

“…The cardiac cycle is a cyclic sequence of events when the heart beats, which consists of systole and diastole process. Identification of the end-systole (ES) and the end-diastole (ED) in a cardiac cycle has been proved critical in several applications, such as the measurement of the ejection fraction and stroke volume [15], and disease diagnosis [29]. Hence, we embed the physical meaning of the input frames into our model with the informative phase code generated by projecting the cardiac cycle to the periodic Cosine function as depicted in Fig.…”