Novel Approach for In Vivo Detection of Vulnerable Coronary Plaques using Molecular 3-T CMR Imaging with an Albumin-Binding ProbeRevista : JACC-Cardiovascular Imaging
Tipo de publicación : ISI Ir a publicación
This study sought to investigate the potential of the noninvasive albumin-binding probe gadofosveset-enhanced cardiac magnetic resonance (GE-CMR) for detection of coronary plaques that can cause acute coronary syndromes (ACS).
ACS are frequently caused by rupture or erosion of coronary plaques that initially do not cause hemodynamically significant stenosis and are therefore not detected by invasive x-ray coronary angiography (XCA).
A total of 25 patients with ACS or symptoms of stable coronary artery disease underwent GE-CMR, clinically indicated XCA, and optical coherence tomography (OCT) within 24 h. GE-CMR was performed approximately 24 h following a 1-time application of gadofosveset-trisodium. Contrast-to-noise ratio (CNR) was quantified within coronary segments in comparison with blood signal.
A total of 207 coronary segments were analyzed on GE-CMR. Segments containing a culprit lesion in ACS patients (n = 11) showed significant higher signal enhancement (CNR) following gadofosveset-trisodium application than segments without culprit lesions (n = 196; 6.1 [3.9 to 16.5] vs. 2.1 [0.5 to 3.5]; p < 0.001). GE-CMR was able to correctly identify culprit coronary lesions in 9 of 11 segments (sensitivity 82%) and correctly excluded culprit coronary lesions in 162 of 195 segments (specificity 83%). Additionally, segmented areas of thin-cap fibroatheroma (n = 22) as seen on OCT demonstrated significantly higher CNR than segments without coronary plaque or segments containing early atherosclerotic lesions (n = 185; 9.2 [3.3 to 13.7] vs. 2.1 [0.5 to 3.4]; p = 0.001). Conclusions In this study, we demonstrated for the first time the noninvasive detection of culprit coronary lesions and thin-cap fibroatheroma of the coronary arteries in vivo by using GE-CMR. This method may represent a novel approach for noninvasive cardiovascular risk prediction.