About this Research Topic
Advanced post-processing techniques of Computed Tomography Coronary Angiography (CTCA) images have improved by leaps and bounds and we are now no longer confined to the anatomic depiction of coronary arteries. These techniques will be discussed below from the simplest to the most complex.
1) Epicardial adipose tissue (EAT): There is growing evidence that epicardial adipose tissue (EAT) is associated with coronary artery disease (CAD) and may be an earlier predictor of CAD risk. EAT is the visceral fat depot of the heart and is a metabolically active organ. Under pathological circumstances, epicardial fat can locally affect the heart and coronary arteries through vasocrine or paracrine secretion of pro-inflammatory cytokines. Studies have shown that EAT is associated with cardiovascular risk factors, coronary atherosclerosis and prevalent coronary artery disease. EAT volume can be quantified using semi-automated software. This is fairly straightforward and easy to perform with the user setting superior and inferior boundaries for the pericardium.
2) Coronary plaque characterization and quantification: Lesions resulting in acute coronary syndrome often have a large necrotic, lipid-rich core. Current scanners can differentiate between calcified plaques and non-calcified plaques (NCPs). However, the sub-classification of NCPs into lipid-rich and fibrous lesions remains challenging, as there is substantial overlap in plaque densities, preventing reliable sub-classification. New automated and semi-automated plaque quantification software tools, with scan-specific adaptive attenuation threshold settings, can potentially overcome some of these limitations and may improve CT number-based plaque component quantification. This is more labour intensive than the quantification of EAT. Studies have shown that quantification of coronary plaque volume is useful to estimate the CAD burden and the future risk of adverse cardiac events. There is consistent evidence that a higher disease burden is associated with higher incidences of adverse cardiac events. Coronary plaque volume has been shown to be useful in risk stratification when correlated with an individual’s circulating lipids.
3) CT-FFR (CT - Fractional Flow Reserve): Fractional flow reserve (FFR) is used along with ICA as the invasive reference standard for estimating ischaemia and to guide coronary revascularisation in cases with a discrepancy between the degree of coronary stenosis and the presence of myocardial ischaemia. In recent years, CT-FFR has emerged as a technology that enables the determination of the haemodynamic significance of coronary lesions non-invasively. CT-FFR uses sophisticated computer algorithms based on computational fluid dynamics applied to pre-existing CTCA images, obviating the need for additional imaging, radiation dose or additional medications such as adenosine CT-FFR (HeartFlow FFRCT) has been incorporated as part of the UK NICE (National Institute for Clinical Excellence, United Kingdom) guideline in 2017 (updated in 2021) on chest pain. HeartFlow FFRCT is also reimbursed by the Japanese government.
4) Artificial Intelligence (AI): The abovementioned post-processing techniques with the exception of EAT quantification are time-consuming and costly and imagine in the near future, all this (including anatomic depiction) can be done with the click of a button. This is where AI comes in and this is the holy grail for all cardiac imaging specialists. Long hours at the cardiac workstation will hopefully be a thing of the past as we look forward to this becoming a reality.
The aim of this Research Topic is to maximise the potential of the commonly performed CTCA scan given that it has now become the first-line investigation for stable chest pain.
We welcome the submission of manuscripts that cover the use of advanced post-processing techniques to maximise the potential of CTCA. Topics of interest include, but are not limited to:
• Quantification of EAT volume using semi-automated software
• Novel automated and semi-automated plaque quantification techniques
• CT Fractional Flow Reserve
• The role of Artificial Intelligence in CTCA
We are confident that this topic will generate interest amongst researchers worldwide as we push towards further automated cardiovascular risk stratification of our patients.
1) Epicardial adipose tissue (EAT): There is growing evidence that epicardial adipose tissue (EAT) is associated with coronary artery disease (CAD) and may be an earlier predictor of CAD risk. EAT is the visceral fat depot of the heart and is a metabolically active organ. Under pathological circumstances, epicardial fat can locally affect the heart and coronary arteries through vasocrine or paracrine secretion of pro-inflammatory cytokines. Studies have shown that EAT is associated with cardiovascular risk factors, coronary atherosclerosis and prevalent coronary artery disease. EAT volume can be quantified using semi-automated software. This is fairly straightforward and easy to perform with the user setting superior and inferior boundaries for the pericardium.
2) Coronary plaque characterization and quantification: Lesions resulting in acute coronary syndrome often have a large necrotic, lipid-rich core. Current scanners can differentiate between calcified plaques and non-calcified plaques (NCPs). However, the sub-classification of NCPs into lipid-rich and fibrous lesions remains challenging, as there is substantial overlap in plaque densities, preventing reliable sub-classification. New automated and semi-automated plaque quantification software tools, with scan-specific adaptive attenuation threshold settings, can potentially overcome some of these limitations and may improve CT number-based plaque component quantification. This is more labour intensive than the quantification of EAT. Studies have shown that quantification of coronary plaque volume is useful to estimate the CAD burden and the future risk of adverse cardiac events. There is consistent evidence that a higher disease burden is associated with higher incidences of adverse cardiac events. Coronary plaque volume has been shown to be useful in risk stratification when correlated with an individual’s circulating lipids.
3) CT-FFR (CT - Fractional Flow Reserve): Fractional flow reserve (FFR) is used along with ICA as the invasive reference standard for estimating ischaemia and to guide coronary revascularisation in cases with a discrepancy between the degree of coronary stenosis and the presence of myocardial ischaemia. In recent years, CT-FFR has emerged as a technology that enables the determination of the haemodynamic significance of coronary lesions non-invasively. CT-FFR uses sophisticated computer algorithms based on computational fluid dynamics applied to pre-existing CTCA images, obviating the need for additional imaging, radiation dose or additional medications such as adenosine CT-FFR (HeartFlow FFRCT) has been incorporated as part of the UK NICE (National Institute for Clinical Excellence, United Kingdom) guideline in 2017 (updated in 2021) on chest pain. HeartFlow FFRCT is also reimbursed by the Japanese government.
4) Artificial Intelligence (AI): The abovementioned post-processing techniques with the exception of EAT quantification are time-consuming and costly and imagine in the near future, all this (including anatomic depiction) can be done with the click of a button. This is where AI comes in and this is the holy grail for all cardiac imaging specialists. Long hours at the cardiac workstation will hopefully be a thing of the past as we look forward to this becoming a reality.
The aim of this Research Topic is to maximise the potential of the commonly performed CTCA scan given that it has now become the first-line investigation for stable chest pain.
We welcome the submission of manuscripts that cover the use of advanced post-processing techniques to maximise the potential of CTCA. Topics of interest include, but are not limited to:
• Quantification of EAT volume using semi-automated software
• Novel automated and semi-automated plaque quantification techniques
• CT Fractional Flow Reserve
• The role of Artificial Intelligence in CTCA
We are confident that this topic will generate interest amongst researchers worldwide as we push towards further automated cardiovascular risk stratification of our patients.
Keywords: Computed Tomography Coronary Angiography, CT-Fractional Flow Reserve, Artificial Intelligence
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