Emerging Methods in Non-invasive Vascular Imaging

38.8K

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77

authors

11

articles

Editors

4

University of Oxford

Azienda Ospedaliero-Universitaria Cagliari

The University of Manchester

Integrated Research Facility, National Institute of Allergy and Infectious Diseases (NIH)

Impact

Article Cover Image

Original Research

15 September 2022

Intraindividual evaluation of effects of image filter function on image quality in coronary computed tomography angiography

Liang Jin

, 3 more and

Ming Li

1,860 views

1 citations

Bland-Altman analyses for LV. LV EDV (ml), ESV (ml), mass (g) and EF (%) are shown in rows. Comparisons of 3T GRE, 5T GRE, and 5T GRE HR to 3T bSSFP are shown in columns.

Original Research

12 September 2022

Bi-ventricular assessment with cardiovascular magnetic resonance at 5 Tesla: A pilot study

Lu Lin

, 6 more and

Yining Wang

2,594 views

9 citations

Plaque Characteristics. Schematic figure illustrating intraplaque hemorrhage (A), MRI on a 3 Tesla scanner (B); white arrow), The axial T1 turbo spin-echo with fat saturation image post gadolinium shows a pronounced LRNC covered by an intact fibrous cap (C); white arrow), Schematic figure illustrating a pronounced LRNC (**) covered by an intact fibrous cap (*) (D), Schematic figure illustrating a pronounced bulky plaque (E); black arrows), 3D ultrasound volume analysis (F), surface morphology, image obtained by CT (G), Schematic figure illustrating plaque rupture with an ulceration (H); black arrow). LRNC, lipid-rich necrotic core; MRI, Magnetic Resonance Imaging; CT,Computed Tomography.

Mini Review

16 May 2022

Carotid Plaque Composition and the Importance of Non-Invasive in Imaging Stroke Prevention

Martin Andreas Geiger

, 5 more and

Ana Terezinha Guillaumon

6,794 views

26 citations

Original Research

22 March 2022

A Deep Learning System for Fully Automated Retinal Vessel Measurement in High Throughput Image Analysis

Danli Shi

, 7 more and

Mingguang He

Motivation: Retinal microvasculature is a unique window for predicting and monitoring major cardiovascular diseases, but high throughput tools based on deep learning for in-detail retinal vessel analysis are lacking. As such, we aim to develop and validate an artificial intelligence system (Retina-based Microvascular Health Assessment System, RMHAS) for fully automated vessel segmentation and quantification of the retinal microvasculature.

Results: RMHAS achieved good segmentation accuracy across datasets with diverse eye conditions and image resolutions, having AUCs of 0.91, 0.88, 0.95, 0.93, 0.97, 0.95, 0.94 for artery segmentation and 0.92, 0.90, 0.96, 0.95, 0.97, 0.95, 0.96 for vein segmentation on the AV-WIDE, AVRDB, HRF, IOSTAR, LES-AV, RITE, and our internal datasets. Agreement and repeatability analysis supported the robustness of the algorithm. For vessel analysis in quantity, less than 2 s were needed to complete all required analysis.

7,731 views

47 citations

Schematic representation of changes to an artery wall in diastole compared to systole. The change in the diameter of the lumen is used in all local stiffness measurements.

Review

15 March 2022

Carotid Artery Stiffness: Imaging Techniques and Impact on Cerebrovascular Disease

Hediyeh Baradaran

and

Ajay Gupta

3,800 views

17 citations

The diagram of analysis. (A) Four representative cases of UIAs with and without AWE. To be specific, the pre-contrast and post-contrast statues of UIAs were presented. (B) The diagram of analysis. Preoperatively, the appropriate UIA patients would receive high-resolution magnetic resonance examinations. The blood samples of the mentioned patients would be tested with the 46-cytokines examination. After appropriate clipping, the UIAs would be resected for the histological analysis. (C) The flow chart of patient enrollment. This study finally recruited 34 UIA patients. UIA, unruptured intracranial aneurysm; AWE, aneurysm wall enhancement.

Original Research

27 January 2022

Serum IL-1, Pyroptosis and Intracranial Aneurysm Wall Enhancement: Analysis Integrating Radiology, Serum Cytokines and Histology

Qingyuan Liu

, 11 more and

Shuo Wang

3,168 views

14 citations

Case Report

04 March 2022

Case Report: Dual-Energy Computed Tomography of Cardiac Changes in IgG4-Related Disease

Ying Wang

, 5 more and

Xi Zhao

2,389 views

4 citations

Original Research

31 January 2022

Noninvasive Aortic Ultrafast Pulse Wave Velocity Associated With Framingham Risk Model: in vivo Feasibility Study

Jinbum Kang

, 3 more and

Yangmo Yoo

2,845 views

1 citations

(A,B) Display the trends in the main hemodynamic parameters of patient 1 and patient 2, respectively, during continuous follow-up angiography. WSS, wall shear stress; WSSG, wall shear stress gradient; EL, energy loss.

Case Report

21 January 2022

Case Report: Dynamic Changes in Hemodynamics During the Formation and Progression of Intracranial Aneurysms

Xiaodong Zhai

, 4 more and

Chengcheng Zhu

2,403 views

5 citations

(A) The line chart of LRNC volume, (B) The line chart of wall volume, (C) The line chart of fibrous tissue volume, (D) The line chart of calcium volume. BL = Baseline. The bars mean 95% CI.

Systematic Review

27 October 2021

Assessment of Therapeutic Response to Statin Therapy in Patients With Intracranial or Extracranial Carotid Atherosclerosis by Vessel Wall MRI: A Systematic Review and Updated Meta-Analysis

Pengyu Zhou

, 4 more and

Chengcheng Zhu

3,556 views

11 citations

Patient selection flowchart.

Original Research

09 September 2021

The Use of Pointwise Encoding Time Reduction With Radial Acquisition MRA to Assess Middle Cerebral Artery Stenosis Pre- and Post-stent Angioplasty: Comparison With 3D Time-of-Flight MRA and DSA

Feifei Zhang

, 11 more and

Chengcheng Zhu

2,527 views

6 citations

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About

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.

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Editors

University of Oxford

Azienda Ospedaliero-Universitaria Cagliari

The University of Manchester

Integrated Research Facility, National Institute of Allergy and Infectious Diseases (NIH)

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