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Deep Learning for Multi-modal Cardiac MR Image Analysis and Quantification

Deep Learning for Multi-modal Cardiac MR Image Analysis and Quantification

(Third Party Funds Single)

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Start date: January 1, 2017
End date: May 1, 2020
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Funding source: Deutscher Akademischer Austauschdienst (DAAD)
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Abstract

Cardiovascular diseases (CVDs) and other cardiac pathologies are the leading cause of death in Europe and the USA. Timely diagnosis and post-treatment follow-ups are imperative for improving survival rates and delivering high-quality patient care. These steps rely heavily on numerous cardiac imaging modalities, which include CT (computerized tomography), coronary angiography and cardiac MRI. Cardiac MRI is a non-invasive imaging modality used to detect and monitor cardiovascular diseases. Consequently, quantitative assessment and analysis of cardiac images is vital for diagnosis and devising suitable treatments. The reliability of quantitative metrics that characterize cardiac functions such as, myocardial deformation and ventricular ejection fraction, depends heavily on the precision of the heart chamber segmentation and quantification. In this project, we aim to investigate deep learning methods to improve the diagnosis and prognosis for CVDs,

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