Abstract 1122‐000172: High Resolution Vessel Wall Imaging and Cerebrovascular Plaques: Comparison with DSA, MRA, and CTA

Introduction : Current imaging modalities might underestimate the presence and severity of intracranial atherosclerosis (ICAD). High resolution vessel wall imaging (HR‐VWI) MRI emerged as a powerful tool to diagnose plaques not detected on routine imaging. We aim to compare different imaging modalities (HR‐VWI MRI; digital subtraction angiogram (DSA); Time‐of‐flight (TOF) MRA; and CTA) in the identification and characterization of intracranial atherosclerotic culprit plaques. Methods : Patients diagnosed with ICAD were prospectively imaged with HR‐VWI MRI. Culprit plaques were identified based on the likelihood of causing the stroke. Using cross‐sectional images of intracranial vessels, regions of interest (ROI) were delineated. Then, diameters and ROI areas were measured for the purpose of calculating the following variables: degree of stenosis (DS) at the plaque level, plaque burden (PB), and remodeling index (RI). Additional imaging modalities (DSA, TOF MRA, and CTA) were identified retrospectively for each patient. The sensitivity of detecting a culprit plaque as well as the correlations between the different variables were analyzed for each modality. Linear regression analysis was used to determine the association of DS with PB and RI. Interobserver agreement on the determination of a culprit plaque on every imaging modality was evaluated. Results : A total of 44 patients who underwent HR‐VWI had ICAD and were included in the final analysis. Of those, 34 had CTA, 18 had TOF‐MRA, and 18 had DSA. Using HR‐VWI as gold standard, the sensitivity for culprit plaque detection was 88% for DSA, 78% for TOF MRA, and 76% for CTA. We found no difference between the DS in all four modalities using measured cross‐sectional diameters, but difference was found when measuring ROI areas to calculate DS. There was a significant positive correlation between PB and DS on HR‐VWI MRI (p<0.001), but not on the DSA (p = 0.168), MRA (p = 0.144), or CTA (p = 0.253), and a significant negative correlation between RI and DS on HR‐VWI MRI (p = 0.003), but not on DSA (p = 0.783), MRA (p = 0.405), or CTA (p = 0.751). PB and RI predicted the degrees of stenosis on HR‐VWI, but not on the other modalities. There was good inter‐rater agreement for culprit plaque detection on HR‐VWI (k = 0.48, p = 0.001), but no agreement was found on the other modalities. Conclusions : HR‐VWI MRI can locate otherwise undetectable plaques on conventional imaging through the ability to measure plaque burden, an essential component for characterization of plaques severity and a strong predictor of stenosis. HR‐VWI also showed more accurate measurements of degree of stenosis through measurement of ROI areas, and had good inter‐rater agreement for accurate plaque detection, compared to DSA, MRA, and CTA.

Parameters of arterial stiffness in hypertensive patients with and without subclinical carotid atherosclerosis

Objective. To assess the relationship between arterial stiffness parameters in hypertensive patients with and without atherosclerotic lesions.Design and methods. We included 127 subjects who were divided into 3 groups: patients with hypertension (HTN) without atherosclerosis (n = 42); patients with HTN and subclinical atherosclerosis (SА) (n = 52) and control group which consisted of individuals without HTN, SA, or coronary artery disease (n = 33). All groups matched by age and gender. All subjects underwent following examinations: ultrasonography of extracranial segments of carotid arteries, 24-hour blood pressure monitoring with the assessment of arterial stiffness parameters.Results. In subjects with HTN compared to controls, pulse wave velocity in aorta (PWVao) was significantly higher (11,3 ± 1,5; 12,3 ± 1,8 vs 10,4 ± 1,3 m/s; p < 0,05), as well as pulse pressure (PP) (46,4 ± 9,8; 45,6 ± 10,6 vs 39,9 ± 6,5 mmHg; p < 0,05), central pulse pressure (PPао) (35,5 ± 8,5; 34,9 ± 8,5 vs 30,9 ± 5,4 mmHg; p < 0,05), and arterial stiffness index (ASI) (141 (127, 159); 139 (128,5, 160,5) vs 126 (118, 138) mmHg; p < 0,05). In subjects with HTN and SA, PWVao was significantly higher compared to other groups (p < 0,05). No significant difference in augmentation index was found (–32,5 (–45, –12); –22 (–36, –12); –37 (–50, –17); p = 0,25). Аmbulatory arterial stiffness index was higher in controls (0,5 ± 0,2) compared to HTN group (0,4 ± 0,2; p = 0,05), while HTN and SA group did not differ significantly (0,5 ± 0,2; p = 0,3). PWVao above 11,15 m/s is associated with 4,3 (2,3–8,2) times higher rate of atherosclerosis plaque detection.Conclusions. In HTN patients, arterial stiffness is changed compared to healthy individuals. PWVao above 11,15 m/s is associated with 4,3 (2,3–8,2) times higher rate of atherosclerosis plaque detection. In patients with HTN and SA arterial stiffness is higher, which might have additional predictive value in risk stratification.

Autoradiographical assessment of inflammation-targeting radioligands for atherosclerosis imaging: potential for plaque phenotype identification

Purpose Many radioligands have been developed for the visualization of atherosclerosis by targeting inflammation. However, interpretation of in vivo signals is often limited to plaque identification. We evaluated binding of some promising radioligands in an in vitro approach in atherosclerotic plaques with different phenotypes. Methods Tissue sections of carotid endarterectomy tissue were characterized as early plaque, fibro-calcific plaque, or phenotypically vulnerable plaque. In vitro binding assays for the radioligands [111In]In-DOTATATE; [111In]In-DOTA-JR11; [67Ga]Ga-Pentixafor; [111In]In-DANBIRT; and [111In]In-EC0800 were conducted, the expression of the radioligand targets was assessed via immunohistochemistry. Radioligand binding and expression of radioligand targets was investigated and compared. Results In sections characterized as vulnerable plaque, binding was highest for [111In]In-EC0800; followed by [111In]In-DANBIRT; [67Ga]Ga-Pentixafor; [111In]In-DOTA-JR11; and [111In]In-DOTATATE (0.064 ± 0.036; 0.052 ± 0.029; 0.011 ± 0.003; 0.0066 ± 0.0021; 0.00064 ± 0.00014 %Added activity/mm2, respectively). Binding of [111In]In-DANBIRT and [111In]In-EC0800 was highest across plaque phenotypes, binding of [111In]In-DOTA-JR11 and [67Ga]Ga-Pentixafor differed most between plaque phenotypes. Binding of [111In]In-DOTATATE was the lowest across plaque phenotypes. The areas positive for cells expressing the radioligand’s target differed between plaque phenotypes for all targets, with lowest percentage area of expression in early plaque sections and highest in phenotypically vulnerable plaque sections. Conclusions Radioligands targeting inflammatory cell markers showed different levels of binding in atherosclerotic plaques and among plaque phenotypes. Different radioligands might be used for plaque detection and discerning early from vulnerable plaque. [111In]In-EC0800 and [111In]In-DANBIRT appear most suitable for plaque detection, while [67Ga]Ga-Pentixafor and [111In]In-DOTA-JR11 might be best suited for differentiation between plaque phenotypes.

Medical Prior Knowledge Guided Automatic Detection of Coronary Arteries Calcified Plaque with Cardiac CT

Calcified plaque in coronary arteries is one major cause and prediction of future coronary artery disease risk. Therefore, the detection of calcified plaque in coronary arteries is exceptionally significant in clinical for slowing coronary artery disease progression. At present, the Convolutional Neural Network (CNN) is exceedingly popular in natural images’ object detection field. Therefore, CNN in the object detection field of medical images also has a wide range of applications. However, many current calcified plaque detection methods in medical images are based on improving the CNN model algorithm, not on the characteristics of medical images. In response, we propose an automatic calcified plaque detection method in non-contrast-enhanced cardiac CT by adding medical prior knowledge. The training data merging with medical prior knowledge through data augmentation makes the object detection algorithm achieve a better detection result. In terms of algorithm, we employ a deep learning tool knows as Faster R-CNN in our method for locating calcified plaque in coronary arteries. To reduce the generation of redundant anchor boxes, Region Proposal Networks is replaced with guided anchoring. Experimental results show that the proposed method achieved a decent detection performance.