Low Adiponectin Levels Are an Independent Predictor of Mixed and Non-Calcified Coronary Atherosclerotic Plaques

Atherosclerosis is the primary cause of coronary artery disease (CAD). There is increasing recognition that lesion composition rather than size determines the acute complications of atherosclerotic disease. Low serum adiponectin levels were reported to be associated with coronary artery disease and future incidence of acute coronary syndrome (ACS). The impact of adiponectin on lesion composition still remains to be determined. We measured serum adiponectin levels in 303 patients with stable typical or atypical chest pain, who underwent dual-source multi-slice CT-angiography to exclude coronary artery stenosis. Atherosclerotic plaques were classified as calcified, mixed or non-calcified. In bivariate analysis adiponectin levels were inversely correlated with total coronary plaque burden (r = -0.21, p = 0.0004), mixed (r = -0.20, p = 0.0007) and non-calcified plaques (r = -0.18, p = 0.003). No correlation was seen with calcified plaques (r = -0.05, p = 0.39). In a fully adjusted multivariate model adiponectin levels remained predictive of total plaque burden (estimate: -0.036, 95%CI: -0.052 to -0.020, p<0.0001), mixed (estimate: -0.087, 95%CI: -0.132 to -0.042, p = 0.0001) and non-calcified plaques (estimate: -0.076, 95%CI: -0.115 to -0.038, p = 0.0001). Adiponectin levels were not associated with calcified plaques (estimate: -0.021, 95% CI: -0.043 to -0.001, p = 0.06). Since the majority of coronary plaques was calcified, adiponectin levels account for only 3% of the variability in total plaque number. In contrast, adiponectin accounts for approximately 20% of the variability in mixed and non-calcified plaque burden. Adiponectin levels predict mixed and non-calcified coronary atherosclerotic plaque burden. Low adiponectin levels may contribute to coronary plaque vulnerability and may thus play a role in the pathophysiology of ACS

Improving the Quality of Synthetic FLAIR Images with Deep Learning Using a Conditional Generative Adversarial Network for Pixel-by-Pixel Image Translation.

BACKGROUND AND PURPOSE:Synthetic FLAIR images are of lower quality than conventional FLAIR images. Here, we aimed to improve the synthetic FLAIR image quality using deep learning with pixel-by-pixel translation through conditional generative adversarial network training.MATERIALS AND METHODS:Forty patients with MS were prospectively included and scanned (3T) to acquire synthetic MR imaging and conventional FLAIR images. Synthetic FLAIR images were created with the SyMRI software. Acquired data were divided into 30 training and 10 test datasets. A conditional generative adversarial network was trained to generate improved FLAIR images from raw synthetic MR imaging data using conventional FLAIR images as targets. The peak signal-to-noise ratio, normalized root mean square error, and the Dice index of MS lesion maps were calculated for synthetic and deep learning FLAIR images against conventional FLAIR images, respectively. Lesion conspicuity and the existence of artifacts were visually assessed.RESULTS:The peak signal-to-noise ratio and normalized root mean square error were significantly higher and lower, respectively, in generated-versus-synthetic FLAIR images in aggregate intracranial tissues and all tissue segments (all P < .001). The Dice index of lesion maps and visual lesion conspicuity were comparable between generated and synthetic FLAIR images (P = 1 and .59, respectively). Generated FLAIR images showed fewer granular artifacts (P = .003) and swelling artifacts (in all cases) than synthetic FLAIR images.CONCLUSIONS:Using deep learning, we improved the synthetic FLAIR image quality by generating FLAIR images that have contrast closer to that of conventional FLAIR images and fewer granular and swelling artifacts, while preserving the lesion contrast

Relationship among coronary plaque compliance, coronary risk factors and tissue characteristics evaluated by integrated backscatter intravascular ultrasound

<p>Abstract</p> <p>Background</p> <p>The purpose of the present study was to evaluate the mechanical properties of coronary plaques and plaque behavior, and to elucidate the relationship among tissue characteristics of coronary plaques, mechanical properties and coronary risk factors using integrated backscatter intravascular ultrasound (IB-IVUS).</p> <p>Methods</p> <p>Non-targeted plaques with moderate stenosis (plaque burden at the minimal lumen site: 50-70%) located proximal to the site of the percutaneous coronary intervention target lesions were evaluated by IB-IVUS. Thirty-six plaques (less calcified group: an arc of calcification ≤10°) in 36 patients and 22 plaques (moderately calcified group: 10° < an arc of calcification ≤60°) in 22 patients were evaluated. External elastic membrane volume (EEMV) compliance, lumen volume (LV) compliance, plaque volume (PV) response (difference between PV in systole and diastole), EEM area stiffness index were measured at the minimal lumen site. Relative lipid volume (lipid volume/internal elastic membrane volume) was calculated by IB-IVUS.</p> <p>Results</p> <p>In the less calcified group, there was a significant correlation between EEMV compliance and the relative lipid volume (r = 0.456, p = 0.005). There was a significant inverse correlation between EEM area stiffness index and the relative lipid volume (p = 0.032, r = −0.358). The LV compliance and EEM area stiffness index were significantly different in the diabetes mellitus (DM) group than in the non-DM group (1.32 ± 1.49 vs. 2.47 ± 1.79%/10 mmHg, p =0.014 and 28.3 ± 26.0 vs. 15.7 ± 17.2, p =0.020). The EEMV compliance and EEM area stiffness index were significantly different in the hypertension (HTN) group than in the non-HTN group (0.77 ± 0.68 vs. 1.57 ± 0.95%/10 mmHg, p =0.012 and 26.5 ± 24.3 vs. 13.0 ± 16.7, p =0.020). These relationships were not seen in the moderately calcified group.</p> <p>Conclusion</p> <p>The present study provided new findings that there was a significant correlation between mechanical properties and tissue characteristics of coronary arteries. In addition, our results suggested that the EEMV compliance and the LV compliance were independent and the compliance was significantly impaired in the patients with DM and/or HTN. Assessment of coronary mechanical properties during PCI may provide us with useful information regarding the risk stratification of patients with coronary heart disease.</p