Quantitative assessment of stenosis severity and atherosclerotic plaque composition using 256-slice computed tomography

2010 ◽  
Vol 20 (8) ◽  
pp. 1841-1850 ◽  
Author(s):  
Grigorios Korosoglou ◽  
Dirk Mueller ◽  
Stephanie Lehrke ◽  
Henning Steen ◽  
Waldemar Hosch ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Atherosclerotic Plaque ◽  
Quantitative Assessment ◽  
Plaque Composition ◽  
Stenosis Severity

scholarly journals Changes in Atherosclerotic Plaque Composition with Anti-Lipid Therapy as Detected by Coronary Computed Tomography Angiography

2021 ◽  
Author(s):  
Drew Thomas ◽  
Darma Marcelin ◽  
Shone Almeida
Keyword(s):  
Computed Tomography ◽  
Cardiovascular Disease ◽  
Atherosclerotic Plaque ◽  
Computed Tomography Angiography ◽  
Coronary Computed Tomography Angiography ◽  
Plaque Composition ◽  
Cardiac Events ◽  
Mortality And Morbidity ◽  
Lipid Management ◽  
Coronary Computed Tomography

Lipid management remains the mainstay of cardiovascular disease prevention. Drugs that target cholesterol reduction, such as HMG-CoA reductase inhibitors (statins) and proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors, have shown significant mortality and morbidity benefit. Predominantly targeting low-density lipoprotein (LDL). These drugs have been indicated to reduce lipid composition and plaque proliferation. Total plaque burden and composition can now be assessed with noninvasive advanced cardiac imaging modalities. This chapter will address the components of atherosclerotic plaque as identified with coronary computed tomography angiography (CCTA) and review in detail the changes in plaque characteristics that may be responsible for reduction in cardiac events. These changes in plaque composition may help guide future management of cardiovascular disease, serving as an imaging biomarker for better risk stratification. Readers will gain a deeper understanding of plaque morphology with direct clinical applicability as well as an understanding of how noninvasive imaging can be utilized to assess plaque composition.

scholarly journals Lipid biomarkers in statin users with coronary artery disease annotated by coronary computed tomography angiography

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Elena Michelucci ◽  
Nicoletta Di Giorgi ◽  
Francesco Finamore ◽  
Jeff M. Smit ◽  
Arthur J. H. A. Scholte ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Computed Tomography Angiography ◽  
Plasma Lipids ◽  
Coronary Computed Tomography Angiography ◽  
Lipid Biomarkers ◽  
Plaque Composition ◽  
Stenosis Severity ◽  
Artery Disease

AbstractMolecular markers are suggested to improve the diagnostic and prognostic accuracy in patients with coronary artery disease (CAD) beyond current clinical scores based on age, gender, symptoms and traditional risk factors. In this context, plasma lipids are emerging as predictors of both plaque composition and risk of future events. We aim to identify plasma lipid biomarkers associated to CAD indexes of stenosis severity, plaque lipid content and a comprensive score of CAD extent and its risk. We used a simple high performance liquid chromatography-tandem mass spectrometry method to identify 69 plasma lipids in 132 subjects referred to Coronary Computed Tomography Angiography (CCTA) for suspected CAD, all under statin treatment. Patients were stratified in groups using three different CCTA-based annotations: CTA-risk score, lipid plaque prevalence (LPP) ratio and the coronary artery disease-reporting and data system (CAD-RADS). We identified a common set of lipid biomarkers composed of 7 sphingomyelins and 3 phosphatidylethanolamines, which discriminates between high risk CAD patients and controls regardless of the CAD annotations used (CTA score, LPP ratio, or CAD-RADS). These results highlight the potential of circulating lipids as biomarkers of stenosis severity, non calcified plaque composition and overall plaque risk of events.

Reproducibility, Accuracy, and Predictors of Accuracy for the Detection of Coronary Atherosclerotic Plaque Composition by Computed Tomography

2010 ◽  
Vol 45 (11) ◽  
pp. 693-701 ◽  
Author(s):  
Alina G. van der Giessen ◽  
Michael H. Toepker ◽  
Patrick M. Donelly ◽  
Fabian Bamberg ◽  
Christopher L. Schlett ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Atherosclerotic Plaque ◽  
Plaque Composition ◽  
Coronary Atherosclerotic Plaque

scholarly journals Atherosclerotic Plaque Composition and Classification Identified by Coronary Computed Tomography

2013 ◽  
Vol 6 (5) ◽  
pp. 655-664 ◽  
Author(s):  
Daniel R. Obaid ◽  
Patrick A. Calvert ◽  
Deepa Gopalan ◽  
Richard A. Parker ◽  
Stephen P. Hoole ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Atherosclerotic Plaque ◽  
Plaque Composition ◽  
Coronary Computed Tomography

scholarly journals Radiation dose reduction with deep-learning image reconstruction for coronary computed tomography angiography

2021 ◽  
Author(s):  
Dominik C. Benz ◽  
Sara Ersözlü ◽  
François L. A. Mojon ◽  
Michael Messerli ◽  
Anna K. Mitulla ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Image Reconstruction ◽  
Radiation Dose ◽  
Coronary Computed Tomography Angiography ◽  
Image Noise ◽  
Plaque Composition ◽  
Plaque Volume ◽  
Normal Dose ◽  
Stenosis Severity

Abstract Objectives Deep-learning image reconstruction (DLIR) offers unique opportunities for reducing image noise without degrading image quality or diagnostic accuracy in coronary CT angiography (CCTA). The present study aimed at exploiting the capabilities of DLIR to reduce radiation dose and assess its impact on stenosis severity, plaque composition analysis, and plaque volume quantification. Methods This prospective study includes 50 patients who underwent two sequential CCTA scans at normal-dose (ND) and lower-dose (LD). ND scans were reconstructed with Adaptive Statistical Iterative Reconstruction-Veo (ASiR-V) 100%, and LD scans with DLIR. Image noise (in Hounsfield units, HU) and quantitative plaque volumes (in mm3) were assessed quantitatively. Stenosis severity was visually categorized into no stenosis (0%), stenosis (< 20%, 20–50%, 51–70%, 71–90%, 91–99%), and occlusion (100%). Plaque composition was classified as calcified, non-calcified, or mixed. Results Reduction of radiation dose from ND scans with ASiR-V 100% to LD scans with DLIR at the highest level (DLIR-H; 1.4 mSv vs. 0.8 mSv, p < 0.001) had no impact on image noise (28 vs. 27 HU, p = 0.598). Reliability of stenosis severity and plaque composition was excellent between ND scans with ASiR-V 100% and LD scans with DLIR-H (intraclass correlation coefficients of 0.995 and 0.974, respectively). Comparison of plaque volumes using Bland–Altman analysis revealed a mean difference of − 0.8 mm3 (± 2.5 mm3) and limits of agreement between − 5.8 and + 4.1 mm3. Conclusion DLIR enables a reduction in radiation dose from CCTA by 43% without significant impact on image noise, stenosis severity, plaque composition, and quantitative plaque volume. Key Points •Deep-learning image reconstruction (DLIR) enables radiation dose reduction by over 40% for coronary computed tomography angiography (CCTA). •Image noise remains unchanged between a normal-dose CCTA reconstructed by ASiR-V and a lower-dose CCTA reconstructed by DLIR. •There is no impact on the assessment of stenosis severity, plaque composition, and quantitative plaque volume between the two scans.

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