QURAI

AREA RESEARCH

Category: Computed tomography

Research

Projects

    2011

     | 

    2013

    Atherosclerotic calcifications of head and neck arteries

    Relating calcifications and stenosis in the internal carotid artery in patients with symptoms of cerebrovascular disease

    FINISHED

    2012

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    2016

    Identification and quantification of arterial calcifications in cardiac CT

    Developing machine learning algorithms for coronary calcium scoring in cardiac CT

    FINISHED

    2014

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    2018

    Automatic detection of CVD and osteoporosis in lung cancer screening trials

    Developing AI methods for automatic detection of cardiovascular and spine diseases in chest CT

    FINISHED

    2012

     | 

    2017

    Cardiovascular phenotype-genotype analysis within a CT based lung cancer screening trial

    Developing machine learning methods to measure existing and novel imaging biomarkers related to cardiovascular disease in chest CT

    FINISHED

    2015

     | 

    2019

    Automatic identification of patients with functionally significant obstructive coronary artery disease using non-invasive cardiac CT

    Developing AI models to identify patients with functionally significant coronary artery disease.

    FINISHED

    2020

     | 

    2022

    Early detection of individuals at risk of cardiac events

    Developing deep learning techniques for accurate selection of patients at risk of acute coronary syndrome or sudden cardiac death from CT scans.

    ONGOING

    2016

     | 

    2020

    Cardiovascular risk in breast cancer patients with radiotherapy planning CT

    Automated cardiovascular risk prediction in breast cancer patients undergoing radiotherapy treatment planning CT

    FINISHED

    2020

     | 

    2024

    Non-invasive and intravascular identification and drug-eluting balloon treatment of vulnerable lipid-rich plaques

    Developing non-invasive AI-based method that detects and characterizes lipid-rich plaque in computed tomography coronary angiography.

    ONGOING

    2017

     | 

    2022

    DLMedIA: Deep transfer learning in cardiovascular disease

    Developing deep transfer learning techniques to effectively analyze medical imaging data with variation in scanners, scan protocols, and patient populations.

    ONGOING

    2017

     | 

    2022

    DLMedIA: Deep generative models for cardiovascular disease

    Developing deep generative models to learn more efficiently from less data in the analysis of cardiac CT.

    ONGOING

    2017

     | 

    2022

    DLMedIA: High dimensional data for cardiovascular disease

    Developing deep learning techniques for quantitative analysis of 4- and 5-dimensional CT and MR images of the heart

    ONGOING

    2019

     | 

    2022

    AI for CCTA-based prediction of FFR

    Developing AI models to predict invasively measured fractional flow reserve noninvasively

    ONGOING