Atherosclerosis builds up slowly and affects the whole vascular system without showing any symptoms, until it manifests itself with clinical events, e.g. transient ischemic attack, angina, stroke and myocardial infarction. Despite advances in treating this chronic inflammatory disease with pharmaceuticals and surgical interventions, it remains the underlying pathology that causes the highest number of deaths in the world. Non-invasive imaging modalities, such as Ultrasound, CT, MRI, PET and SPECT can offer early diagnosis of vascular disease, risk stratification, and monitoring of therapy, providing insights on the pathophysiology of atherosclerosis, on plaque growth and rupture, as well as on vascular function and remodeling. Recent technological advances in medical imaging hardware and in image analysis software based on machine learning algorithms have provided the opportunity to move towards more accurate, quantitative and automated imaging methods for non-invasive characterization of vascular disease.
The goal of this Research Topic is to present novel vascular imaging methods, single and multi-modality techniques, their relative strengths and weaknesses, and the advantages of combining information from different modalities (Ultrasound, CT, MRI, PET and SPECT). We aim to collect reviews, opinion and original research articles on the development and validation of image acquisition and analysis methods, as well as reports on pilot and proof of concept studies using vascular imaging as surrogate endpoints in clinical trials. Our intention is to discuss emerging methods, challenges and future opportunities for multi-modality vascular imaging approaches that provide insights into vascular blood flow, vessel wall morphology and function. We hope this Research Topic will highlight promising techniques that could potentially translate from bench to bedside and directly benefit patients, acknowledging current limitations to their clinical implementation and adoption.
We invite the submission of manuscripts that describe emerging methods for vascular imaging and highlight their potential clinical applications, with particular interest in multimodality and ‘full-stack’ approaches integrating image acquisition and AI-based analysis.
We welcome original research articles, methods, perspectives, opinions and reviews on the following themes:
1) Measuring the severity of vascular disease in the aorta, carotid, intracranial, peripheral arteries and veins;
2) Potential clinical applications, risk stratification, and support for therapeutic decisions;
3) Technical improvements in Ultrasound, CT, MRI, PET and SPECT for vascular imaging;
4) Characterization of atherosclerotic plaque morphology and composition, intra-plaque haemorrhage, angiogenesis and inflammation;
5) Quantification of vascular blood flow, arterial compliance, biomechanical wall shear stress, stenosis and aneurysm;
6) Deep Learning in vascular image acquisition, reconstruction and processing;
7) AI-based automated vessel wall, plaque tissue segmentation and classification algorithms;
8) Novel vascular imaging biomarkers and vascular imaging as surrogate endpoint in clinical trials.
Atherosclerosis builds up slowly and affects the whole vascular system without showing any symptoms, until it manifests itself with clinical events, e.g. transient ischemic attack, angina, stroke and myocardial infarction. Despite advances in treating this chronic inflammatory disease with pharmaceuticals and surgical interventions, it remains the underlying pathology that causes the highest number of deaths in the world. Non-invasive imaging modalities, such as Ultrasound, CT, MRI, PET and SPECT can offer early diagnosis of vascular disease, risk stratification, and monitoring of therapy, providing insights on the pathophysiology of atherosclerosis, on plaque growth and rupture, as well as on vascular function and remodeling. Recent technological advances in medical imaging hardware and in image analysis software based on machine learning algorithms have provided the opportunity to move towards more accurate, quantitative and automated imaging methods for non-invasive characterization of vascular disease.
The goal of this Research Topic is to present novel vascular imaging methods, single and multi-modality techniques, their relative strengths and weaknesses, and the advantages of combining information from different modalities (Ultrasound, CT, MRI, PET and SPECT). We aim to collect reviews, opinion and original research articles on the development and validation of image acquisition and analysis methods, as well as reports on pilot and proof of concept studies using vascular imaging as surrogate endpoints in clinical trials. Our intention is to discuss emerging methods, challenges and future opportunities for multi-modality vascular imaging approaches that provide insights into vascular blood flow, vessel wall morphology and function. We hope this Research Topic will highlight promising techniques that could potentially translate from bench to bedside and directly benefit patients, acknowledging current limitations to their clinical implementation and adoption.
We invite the submission of manuscripts that describe emerging methods for vascular imaging and highlight their potential clinical applications, with particular interest in multimodality and ‘full-stack’ approaches integrating image acquisition and AI-based analysis.
We welcome original research articles, methods, perspectives, opinions and reviews on the following themes:
1) Measuring the severity of vascular disease in the aorta, carotid, intracranial, peripheral arteries and veins;
2) Potential clinical applications, risk stratification, and support for therapeutic decisions;
3) Technical improvements in Ultrasound, CT, MRI, PET and SPECT for vascular imaging;
4) Characterization of atherosclerotic plaque morphology and composition, intra-plaque haemorrhage, angiogenesis and inflammation;
5) Quantification of vascular blood flow, arterial compliance, biomechanical wall shear stress, stenosis and aneurysm;
6) Deep Learning in vascular image acquisition, reconstruction and processing;
7) AI-based automated vessel wall, plaque tissue segmentation and classification algorithms;
8) Novel vascular imaging biomarkers and vascular imaging as surrogate endpoint in clinical trials.
