The mechanisms of arterial signal intensity profile in non-contrast coronary MRA (NC-MRCA): a 3D printed phantom investigation and clinical translations.

Signal intensity (SI) drop has been proposed as an indirect stenosis assessment in non-contrast coronary MRA (NC-MRCA) but it uses unproven assumptions. We aimed to clarify the mechanisms that govern the SI in vitro and develop a stenosis detection method in vivo. Flow phantom tubes with/without stenosis were scanned under two spatial resolutions (0.5/1.0 mm3) on a 3.0 T MRI. Thirty-two coronary arteries from 11 volunteers were prospectively scanned with an EKG- and respiratory-gated 3D NC-MRCA with a resolution of 1.0 mm3, with coronary computed tomography angiography (CTA) as reference. The normalized SI along the centerline of the tubes or the coronary arteries was assessed against the distance from the orifice using a linear regression model. Its coefficient (SI decay slope) and goodness-of-fit (R2) were extracted to assess the effect of flow velocity and stenosis on the SI profile curve. The R2 was utilized for the stenosis detection. Phantom study: A slow flow velocity caused a steep SI decay slope. The SI drop revealed only at the inlet and outlet of stenosis due to the flow turbulence/vortex and yielded low R2, in which shape changed by the resolution. Clinical study: The R2 cutoff to detect ≥ 50% stenosis for the left and right coronary arteries were 0.64 and 0.20 with a sensitivity/specificity of 71.5/71.5 and 66.7/100 (%), respectively. The SI drop did not reflect the actual stenosis position and not suitable for the stenosis localization. The R2 cutoff represents an alternative method to detect stenoses on NC-MRCA at vessel level.Trial registration: ClinicalTrials.gov; NCT03768999, registered on December 7, 2018.

Perivascular fat attenuation for predicting adverse cardiac events in stable patients undergoing invasive coronary angiography.

BACKGROUND: Inflammation surrounding the coronary arteries can be non-invasively assessed using pericoronary adipose tissue attenuation (PCAT). While PCAT holds promise for further risk stratification of patients with low coronary artery disease (CAD) prevalence, its value in higher risk populations remains unknown. METHODS: CORE320 enrolled patients referred for invasive coronary angiography with known or suspected CAD. Coronary computed tomography angiography (CCTA) images were collected for 381 patients for whom clinical outcomes were assessed 5 years after enrollment. Using semi-automated image analysis software, PCAT was obtained and normalized for the right coronary (RCA), left anterior descending (LAD), and left circumflex arteries (LCx). The association between PCAT and major adverse cardiovascular events (MACE) during follow up was assessed using Cox regression models. RESULTS: Thirty-seven patients were excluded due to technical failure. For the remaining 344 patients, median age was 62 (interquartile range, 55-68) with 59% having ≥1 coronary artery stenosis of ≥50% by quantitative coronary angiography. Mean attenuation values for PCAT in RCA, LAD, and LCx were -74.9, -74.2, and -71.2, respectively. Hazard ratios and 95% confidence intervals (CI) for normalized PCAT in the RCA, LAD, and LCx for MACE were 0.96 (CI: 0.75-1.22, p â€‹= â€‹0.71), 1.31 (95% CI: 0.96-1.78, p â€‹= â€‹0.09), and 0.98 (95% CI: 0.78-1.22, p â€‹= â€‹0.84), respectively. For death, stroke, or myocardial infarction only, hazard ratios were 0.68 (0.44-1.07), 0.85 (0.56-1.29), and 0.57 (0.41-0.80), respectively. CONCLUSIONS: In patients referred for invasive coronary angiography with suspected CAD, PCAT did not predict MACE during long term follow up. Further studies are needed to understand the relationship of PCAT with CAD risk.