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Mechanical stress and strain conditions are closely related to atherosclerotic plaque progression and rupture and have been under intensive investigations in recent years. It is well known that arteries have a three-layer structure: intima, media and adventitia. However, in vivo image-based multilayer plaque models are not available in the current literature due to lack of multilayer image segmentation data. A multilayer segmentation and repairing technique was introduced to segment coronary plaque optical coherence tomography (OCT) image to obtain its three-layer vessel structure. A total of 200 OCT slices from 20 patients (13 male; 7 female) were used to construct multilayer and single-layer 3D thin-slice models to calculate plaque stress and strain and compare model differences. Our results indicated that the average maximum plaque stress values of 20 patients from multilayer and single-layer models were 385.13 ± 110.09 kPa and 270.91 ± 95.86 kPa, respectively. The relative difference was 42.2%, with single-layer stress serving as the base value. The average mean plaque stress values from multilayer and single-layer models were 129.59 ± 32.77 kPa and 93.27 ± 18.20 kPa, respectively, with a relative difference of 38.9%. The maximum and mean plaque strain values obtained from the multilayer models were 11.6% and 19.0% higher than those from the single-layer models. Similarly, the maximum and mean cap strains showed increases of 9.6% and 12.9% over those from the single-layer models. These findings suggest that use of multilayer models could improve plaque stress and strain calculation accuracy and may have large impact on plaque progression and vulnerability investigation and potential clinical applications. Further large-scale studies are needed to validate our findings. [ABSTRACT FROM AUTHOR]

Aims We aimed to use optical coherence tomography (OCT) to identify differences in atherosclerotic culprit lesion morphology in women with myocardial infarction (MI) with non-obstructive coronary arteries (MINOCA) compared with MI with obstructive coronary artery disease (MI-CAD). Methods and results Women with an OCT-determined atherosclerotic aetiology of non-ST segment elevation (NSTE)-MINOCA (angiographic diameter stenosis <50%) who were enrolled in the multicentre Women's Heart Attack Research Program (HARP) study were compared with a consecutive series of women with NSTE-MI-CAD who underwent OCT prior to coronary intervention at a single institution. Atherosclerotic pathologies identified by OCT included plaque rupture, plaque erosion, intraplaque haemorrhage (IPH, a region of low signal intensity with minimum attenuation adjacent to a lipidic plaque without fibrous cap disruption), layered plaque (superficial layer with clear demarcation from the underlying plaque indicating early thrombus healing), or eruptive calcified nodule. We analysed 58 women with NSTE-MINOCA and 52 women with NSTE-MI-CAD. Optical coherence tomography features of underlying vulnerable plaque (thin-cap fibroatheroma) were less common in MINOCA (3 vs. 35%) than in MI-CAD. Intraplaque haemorrhage (47 vs. 2%) and layered plaque (31 vs. 12%) were more common in MINOCA than MI-CAD, whereas plaque rupture (14 vs. 67%), plaque erosion (8 vs. 14%), and calcified nodule (0 vs. 6%) were less common in MINOCA. The angle of ruptured cavity was smaller and thrombus burden was lower in MINOCA. Conclusion The prevalence of atherothrombotic culprit lesion subtype varied substantially between MINOCA and MI-CAD. A majority of culprit lesions in MINOCA had the appearance of IPH or layered plaque. Clinical Trial Registration Information Clinical Trial Name: Heart Attack Research Program-Imaging Study (HARP); ClinicalTrial.gov Identifier. NCT02905357; URL: https://clinicaltrials.gov/ct2/show/NCT02905357 [ABSTRACT FROM AUTHOR]

This study aims to elucidate the relationships between the flexural strength and surface topography of composite resin blocks, utilized for the CAD/CAM system, after milling in various step-over amounts. The rectangular specimens were milled from CAD/CAM blocks with step-over amount 0.01, 0.02, 0.05, 0.1, and 0.2 mm; further, a three-point bending test was conducted to reveal the flexural strength. The surface morphology after milling was assessed by a 3D laser microscope. The surface roughness significantly decreased by reducing the step-over amount. Although there was significant association between the surface roughness and flexural strength by the Pearson correlation, the 95% confidence intervals of the flexural strength were between the mirror-polished and sand-blasted groups. These results suggest that a precise step-over amount enables us to obtain a smooth surface. Furthermore, the flexural strength of the rough surface milled by a large step-over amount caused no damage to the composite resin for CAD/CAM crown. [ABSTRACT FROM AUTHOR]

Aims Intraplaque haemorrhage is considered a major contributor to lesion progression. We assessed coronary lesions with intraplaque haemorrhage using intravascular ultrasound (IVUS) and near-infrared spectroscopy (NIRS). Methods and results We evaluated coronary arteries from autopsy hearts using 40MHz IVUS and NIRS and compared the imaging findings to histopathology. A total of 2324 2-mm long histological segments from 101 coronary arteries from 56 autopsy hearts were included. Intraplaque haemorrhage was found pathologically in 0.8% (18/2324) of segments. Segments with intraplaque haemorrhage had more fibroatheromas (FAs) with a greater IVUS plaque burden, a greater prevalence of IVUS echolucent zones, and a higher NIRS-lipid core burden index (LCBI) compared to segments without intraplaque haemorrhage (FAs: 72.2% vs. 18.3%, P < 0.0001; plaque burden: 59.7% [95% confidence interval: 55.5, 64.0] vs. 48.6% [45.8, 51.3], P < 0.0001; echolucent zones: 88.9% vs. 2.8%, P < 0.0001; NIRS-LCBI: 176 [88, 264] vs. 72 [53, 91], P = 0.02). The 16 IVUS superficial echolucent zones with intraplaque haemorrhage had more late FAs but shorter echolucent zone lengths (0.9 mm [0.7, 1.1] vs. 1.7 mm [1.5, 1.9], P < 0.0001) compared to 65 IVUS superficial echolucent zones without intraplaque haemorrhage. Conclusions Intracoronary imaging features consistent with intraplaque haemorrhage included a greater plaque burden, a higher NIRS-LCBI, and a greater prevalence of IVUS echolucent zones compared to lesions without intraplaque haemorrhage. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases are closely associated with sudden rupture of atherosclerotic plaques. Previous image modalities such as magnetic resonance imaging (MRI) and intravascular ultrasound (IVUS) were unable to identify vulnerable plaques due to their limited resolution. Optical coherence tomography (OCT) is an advanced intravascular imaging technique developed in recent years which has high resolution approximately 10 microns and could provide more accurate morphology of coronary plaque. In particular, it is now possible to identify plaques with fibrous cap thickness <65 ^m, an accepted threshold value for vulnerable plaques. However, the current segmentation of OCT images are still performed manually by physicians and the process is time consuming. Automatic segmentation and recognition of vulnerable plaques through quantification of plaque components have great clinical significance for cardiovascular research. Two segmentation methods for intracoronary OCT image based on support vector machine (SVM) and convolutional neural network (CNN) were performed to detect the lumen borders and characterize the plaque component. In vivo IVUS and OCT coronary plaque data from 5 patients were acquired at Emory University with patient's consent obtained. Seventy-seven matched IVUS and OCT slices with good image quality and medium to large lipid cores were selected for our segmentation study. Manual OCT segmentation was performed by experts and used as gold standard in the automatic segmentations. VH-IVUS was used as references and guide by the experts in the manual segmentation process. Three plaque component tissue classes were identified from OCT images in this work: lipid tissue (LT), fibrous tissue (FT) and background (BG). Procedures using two machine learning methods (CNN and SVM) were developed to segment OCT images, respectively. For CNN method, the U-Net architecture was selected due to its good performance in very different biomedical segmentation and very few annotated images. For SVM method, local binary patterns (LBPs), gray level co-occurrence matrices (GLCMs) which contains contrast, correlation, energy and homogeneity, entropy and mean value were calculated as features and assembled to feed SVM classifier. The accuracies of two segmentation methods were evaluated and compared using the OCT dataset. Segmentation accuracy is defined as the ratio of the number of pixels correctly classified over the total number of pixels. The overall classification accuracy based CNN method reached 95.8%, and the accuracies for LT, FT and BG were 86.8%, 83.4%, and 98.2%, respectively. The overall classification accuracy based SVM was 71.9%, and per-class accuracy for LT, FT and BG was 75.4%, 78.3%, and 67.0%, respectively. The two methods proposed can automatically detect the lumen borders and characterize the composition of the superficial plaque in OCT images and greatly reduce the time spent by doctors in segmenting and identifying plaques. CNN provided better segmentation accuracies compared to those achieved by SVM. [ABSTRACT FROM AUTHOR]

Atherosclerotic plaque progression is generally considered to be closely associated with morphological and mechanical factors. Plaque morphological information on intravascular ultrasound (IVUS) and optical coherence tomography (OCT) images could complement each other and provide for more accurate plaque morphology. Fluid-structure interaction (FSI) models combining IVUS and OCT were constructed to obtain accurate plaque stress/strain and flow shear stress data for analysis. Accuracy and completeness of imaging and advanced modeling lead to accurate plaque progression predictions. In vivo IVUS and OCT coronary plaque data at baseline and follow-up were acquired from left circumflex coronary and right coronary artery of one patient with patient's consent obtained. Co-registration and segmentation of baseline and follow-up IVUS and OCT images were performed by experts. Baseline and follow-up 3D FSI models with cyclic bending based on merged IVUS and OCT data were constructed to obtain plaque stress/strain and flow shear stress data for plaque progression prediction. Nine factors (6 morphological factors and 3 mechanical factors) including average cap thickness, lipid area, calcification area, lumen area, plaque area, plaque burden, wall shear stress (WSS), plaque wall stress (PWS) and plaque wall strain (PWSn) were selected for each slice. Plaque area increase (PAI) and plaque burden increase (PBI) were chosen to measure plaque progression and serve as the target variables for prediction. All possible combinations of nine factors were fed to a generalized linear mixed model for PAI and PBI prediction and quantification of their prediction accuracies. In this paper, prediction accuracy was defined as the sum of sensitivity and specificity. The optimized predictor combining 9 factors gave the best prediction for PAI with accuracy=1.7087 (sensitivity: 0.8679; specificity: 0.8408). PWSn was the best single-factor predictor for PAI with accuracy=1.5918 (sensitivity: 0.7143; specificity 0.8776). A combination of average cap thickness, calcification area, plaque area, PWS and PWSn gave the best prediction for PBI with accuracy=1.8698 (sensitivity: 0.8892; specificity: 0.9806). PWSn was also the best single-factor predictor for PBI with accuracy=1.8461 (sensitivity: 0.8784; specificity 0.9677). Although WSS was commonly accepted as an important factor for plaque progression, it showed relatively poor ability for prediction of plaque progression in any measure (accuracy, sensitivity, specificity of PAI: 1.0607, 0.0893, 0.9714; PBI: 1.5431/0.6811/0.9032). Combining morphological and mechanical risk factors may lead to more accurate progression prediction, compared to the predictions using single factor. PWSn is better than WSS for plaque progression using single factor. IVUS+OCT formed basis for accurate data for morphological and mechanical factors. [ABSTRACT FROM AUTHOR]