Diagnosing coronary plaque buildup faster with 1D models

AbstractThis study aims to test the hypothesis that the effect of excimer laser coronary angioplasty (ELCA) not only vaporizes thrombi and their underlying coronary plaque, it also changes their quality. We performed a series of cross-sectional analyses in 52 lesions in 51 patients before and after ELCA with integrated backscatter-intravascular ultrasound (IB-IVUS). The constituent parts of the plaque can be assessed by IB-IVUS (i.e., calcified, fibrous, lipid) according to integrated backscatter values. Minimum lumen diameter, lumen volume and vessel volume expanded after ELCA, while plaque volume did not significantly decrease. There was also a decrease of ‘lipid’ component (35.4–30.3%, P < 0.001) and an increase of IB-IVUS-derived ‘fibrous’ part (34.5–38.3%, P < 0.001). These results may help in understanding plaque change after ELCA. Excimer laser coronary angioplasty seems to contribute to the modification of coronary plaque composition in addition to debulking it.

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Clinical Relevance of Coronary Computed Tomography Angiography Beyond Coronary Artery Stenosis

RöFo - Fortschritte auf dem Gebiet der Röntgenstrahlen und der bildgebenden Verfahren ◽

10.1055/a-1395-7905 ◽

2021 ◽

Author(s):

Mohammed Nooruddin Meah ◽

Michelle C. Williams

Keyword(s):

Computed Tomography ◽

Coronary Artery ◽

High Risk ◽

Atherosclerotic Plaque ◽

Computed Tomography Angiography ◽

Coronary Computed Tomography Angiography ◽

Calcium Score ◽

Coronary Plaque ◽

Coronary Computed Tomography ◽

Low Attenuation Plaque

Background The capabilities of coronary computed tomography angiography (CCTA) have advanced significantly in the past decade. Its capacity to detect stenotic coronary arteries safely and consistently has led to a marked decline in invasive diagnostic angiography. However, CCTA can do much more than identify coronary artery stenoses. Method This review discusses applications of CCTA beyond coronary stenosis assessment, focusing in particular on the visual and quantitative analysis of atherosclerotic plaque. Results Established signs of visually assessed high-risk plaque on CT include positive remodeling, low-attenuation plaque, spotty calcification, and the napkin-ring sign, which correlate with the histological thin-cap fibroatheroma. Recently, quantification of plaque subtypes has further improved the assessment of coronary plaque on CT. Quantitatively assessed low-attenuation plaque, which correlates with the necrotic core of the thin-cap fibroatheroma, has demonstrated superiority over stenosis severity and coronary calcium score in predicting subsequent myocardial infarction. Current research aims to use radiomic and machine learning methods to further improve our understanding of high-risk atherosclerotic plaque subtypes identified on CCTA. Conclusion Despite rapid technological advances in the field of coronary computed tomography angiography, there remains a significant lag in routine clinical practice where use is often limited to lumenography. We summarize some of the most promising techniques that significantly improve the diagnostic and prognostic potential of CCTA. Key Points: Citation Format

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Layered coronary plaque associated with coronary vasospasm

Cardiovascular Intervention and Therapeutics ◽

10.1007/s12928-021-00780-9 ◽

2021 ◽

Author(s):

Kazuya Tateishi ◽

Yuichi Saito ◽

Takaaki Matsuoka ◽

Hideki Kitahara ◽

Yoshio Kobayashi

Keyword(s):

Coronary Plaque ◽

Coronary Vasospasm

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A Systematic Review and Meta-Analysis of the Effectiveness of Additional Lipid Apheresis Therapy on Coronary Plaque Quantity in Patients With Familial Hypercholesterolaemia Using Coronary Angiogram

Heart Lung and Circulation ◽

10.1016/j.hlc.2018.06.563 ◽

2018 ◽

Vol 27 ◽

pp. S295-S296

Author(s):

S. Mirzaee ◽

P. Thein ◽

J. Cameron ◽

A. Nasis

Keyword(s):

Systematic Review ◽

Familial Hypercholesterolaemia ◽

Meta Analysis ◽

Coronary Plaque ◽

Coronary Angiogram ◽

Lipid Apheresis ◽

Apheresis Therapy

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Predicting plaque vulnerability change using intravascular ultrasound + optical coherence tomography image-based fluid–structure interaction models and machine learning methods with patient follow-up data: a feasibility study

BioMedical Engineering OnLine ◽

10.1186/s12938-021-00868-6 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Xiaoya Guo ◽

Akiko Maehara ◽

Mitsuaki Matsumura ◽

Liang Wang ◽

Jie Zheng ◽

...

Keyword(s):

Machine Learning ◽

Coronary Plaque ◽

Support Vector ◽

Single Factor ◽

Plaque Vulnerability ◽

Learning Methods ◽

Machine Learning Methods ◽

Vulnerability Prediction ◽

Biomechanical Factors

Abstract Background Coronary plaque vulnerability prediction is difficult because plaque vulnerability is non-trivial to quantify, clinically available medical image modality is not enough to quantify thin cap thickness, prediction methods with high accuracies still need to be developed, and gold-standard data to validate vulnerability prediction are often not available. Patient follow-up intravascular ultrasound (IVUS), optical coherence tomography (OCT) and angiography data were acquired to construct 3D fluid–structure interaction (FSI) coronary models and four machine-learning methods were compared to identify optimal method to predict future plaque vulnerability. Methods Baseline and 10-month follow-up in vivo IVUS and OCT coronary plaque data were acquired from two arteries of one patient using IRB approved protocols with informed consent obtained. IVUS and OCT-based FSI models were constructed to obtain plaque wall stress/strain and wall shear stress. Forty-five slices were selected as machine learning sample database for vulnerability prediction study. Thirteen key morphological factors from IVUS and OCT images and biomechanical factors from FSI model were extracted from 45 slices at baseline for analysis. Lipid percentage index (LPI), cap thickness index (CTI) and morphological plaque vulnerability index (MPVI) were quantified to measure plaque vulnerability. Four machine learning methods (least square support vector machine, discriminant analysis, random forest and ensemble learning) were employed to predict the changes of three indices using all combinations of 13 factors. A standard fivefold cross-validation procedure was used to evaluate prediction results. Results For LPI change prediction using support vector machine, wall thickness was the optimal single-factor predictor with area under curve (AUC) 0.883 and the AUC of optimal combinational-factor predictor achieved 0.963. For CTI change prediction using discriminant analysis, minimum cap thickness was the optimal single-factor predictor with AUC 0.818 while optimal combinational-factor predictor achieved an AUC 0.836. Using random forest for predicting MPVI change, minimum cap thickness was the optimal single-factor predictor with AUC 0.785 and the AUC of optimal combinational-factor predictor achieved 0.847. Conclusion This feasibility study demonstrated that machine learning methods could be used to accurately predict plaque vulnerability change based on morphological and biomechanical factors from multi-modality image-based FSI models. Large-scale studies are needed to verify our findings.

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