AIMC Topic: Coronary Vessels

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Motion-corrected coronary calcium scores by a convolutional neural network: a robotic simulating study.

European radiology Oct 18, 2019
OBJECTIVE: To classify motion-induced blurred images of calcified coronary plaques so as to correct coronary calcium scores on nontriggered chest CT, using a deep convolutional neural network (CNN) trained by images of motion artifacts.
Humans Neural Networks, Computer Motion Algorithms Coronary Vessels Artifacts Tomography, X-Ray Computed Plaque, Atherosclerotic Robotics Vascular Calcification Coronary Artery Disease
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Automated Detection of Vulnerable Plaque for Intravascular Optical Coherence Tomography Images.

Cardiovascular engineering and technology Sep 18, 2019
PURPOSE: Vulnerable plaque detection is important to acute coronary syndrome (ACS) diagnosis. In recent years, intravascular optical coherence tomography (IVOCT) imaging has been used for vulnerable plaque detection. Current automated detection metho...
Rupture, Spontaneous Humans Plaque, Atherosclerotic Coronary Artery Disease Neural Networks, Computer Image Interpretation, Computer-Assisted Diagnosis, Computer-Assisted Automation Prognosis Predictive Value of Tests Coronary Vessels Reproducibility of Results Tomography, Optical Coherence
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Deep vessel segmentation by learning graphical connectivity.

Medical image analysis Sep 6, 2019
We propose a novel deep learning based system for vessel segmentation. Existing methods using CNNs have mostly relied on local appearances learned on the regular image grid, without consideration of the graphical structure of vessel shape. Effective ...
Humans Angiography Neural Networks, Computer Retinal Vessels Image Processing, Computer-Assisted Coronary Vessels
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Impact of Coronary Computerized Tomography Angiography-Derived Plaque Quantification and Machine-Learning Computerized Tomography Fractional Flow Reserve on Adverse Cardiac Outcome.

The American journal of cardiology Aug 8, 2019
This study investigated the impact of coronary CT angiography (cCTA)-derived plaque markers and machine-learning-based CT-derived fractional flow reserve (CT-FFR) to identify adverse cardiac outcome. Data of 82 patients (60 ± 11 years, 62% men) who u...
Prognosis Retrospective Studies Female Survival Rate Humans Machine Learning United States Follow-Up Studies Coronary Angiography Coronary Vessels Multidetector Computed Tomography Male Middle Aged Severity of Illness Index ROC Curve Plaque, Atherosclerotic Computed Tomography Angiography Coronary Stenosis Fractional Flow Reserve, Myocardial
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Machine learning-based coronary artery disease diagnosis: A comprehensive review.

Computers in biology and medicine Jul 4, 2019
Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) and often leads to a heart attack. It annually causes millions of deaths and billions of dollars in financial losses worldwide. Angiography, which is invasive and risky, is...
Data Mining Coronary Vessels Humans Machine Learning Algorithms Databases, Factual Electrocardiography Coronary Angiography Coronary Artery Disease Tomography, X-Ray Computed Diagnosis, Computer-Assisted
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A case of robotic assisted percutaneous coronary intervention of the left main coronary artery in a patient with very late baffle stenosis after surgical correction of anomalous left coronary artery from the pulmonary artery.

Catheterization and cardiovascular interventions : official journal of the Society for Cardiac Angiography & Interventions Jun 27, 2019
A 34-year-old woman with history of surgical correction (Takeuchi procedure) of anomalous left coronary artery from the pulmonary artery (ALCAPA) presented with reduced left ventricular ejection fraction of 48% and severe ischemia quantified as 21% b...
Humans Treatment Outcome Robotics Cardiac Surgical Procedures Female Adult Pulmonary Artery Angioplasty, Balloon, Coronary Coronary Vessels Bland White Garland Syndrome Drug-Eluting Stents Therapy, Computer-Assisted Coronary Stenosis
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Scoring of Coronary Artery Disease Characteristics on Coronary CT Angiograms by Using Machine Learning.

Radiology Jun 25, 2019
Background Coronary CT angiography contains prognostic information but the best method to extract these data remains unknown. Purpose To use machine learning to develop a model of vessel features to discriminate between patients with and without subs...
Coronary Artery Disease Image Interpretation, Computer-Assisted Coronary Vessels Computed Tomography Angiography Middle Aged Machine Learning Severity of Illness Index Predictive Value of Tests Female Coronary Angiography Reproducibility of Results Risk Factors Young Adult Adult Male Aged Aged, 80 and over Humans
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Prediction of coronary thin-cap fibroatheroma by intravascular ultrasound-based machine learning.

Atherosclerosis May 4, 2019
BACKGROUND AND AIMS: Although grayscale intravascular ultrasound (IVUS) is commonly used for assessing coronary lesion morphology and optimizing stent implantation, detection of vulnerable plaques by IVUS remains challenging. We aimed to develop mach...
Rupture, Spontaneous Plaque, Atherosclerotic Coronary Stenosis Coronary Artery Disease Humans Fibrosis Neural Networks, Computer Image Interpretation, Computer-Assisted Machine Learning Male Coronary Vessels Female Middle Aged Predictive Value of Tests Reproducibility of Results Risk Assessment Tomography, Optical Coherence Risk Factors Disease Progression Aged Bayes Theorem Diagnosis, Computer-Assisted Ultrasonography, Interventional
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Advanced atherosclerosis imaging by CT: Radiomics, machine learning and deep learning.

Journal of cardiovascular computed tomography Apr 21, 2019
In the last decade, technical advances in the field of medical imaging significantly improved and broadened the application of coronary CT angiography (CCTA) for the non-invasive assessment of coronary artery disease. Recently, similar breakthroughs ...
Coronary Artery Disease Coronary Vessels Plaque, Atherosclerotic Computed Tomography Angiography Deep Learning Humans Predictive Value of Tests Prognosis Coronary Angiography Reproducibility of Results Radiographic Image Interpretation, Computer-Assisted Severity of Illness Index
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Learning to Reconstruct Computed Tomography Images Directly From Sinogram Data Under A Variety of Data Acquisition Conditions.

IEEE transactions on medical imaging Apr 11, 2019
Computed tomography (CT) is widely used in medical diagnosis and non-destructive detection. Image reconstruction in CT aims to accurately recover pixel values from measured line integrals, i.e., the summed pixel values along straight lines. Provided ...
Phantoms, Imaging Abdomen Coronary Vessels Head Radiography, Thoracic Humans Tomography, X-Ray Computed Deep Learning Image Processing, Computer-Assisted
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