Deep Learning for Automated Analysis of Cardiac Imaging: Applications in Cine and 4D Flow MRI

Deep Learning for Automated Analysis of Cardiac Imaging: Applications in Cine and 4D Flow MRI

Cine and 4D flow cardiac MRI are two important non-invasive MR imaging techniques to assess cardiac function and diagnose cardiovascular diseases. Cine MRI offers great soft tissue detail which allows clinical experts to evaluate structure and function of the heart. 4D flow MRI further has the ability of three-dimensional time-resolved acquisition of blood flow velocity, which can be used to derive intra-cardiac hemodynamic parameters. In this thesis, we developed deep learning-based approaches to analyze cine and 4D flow cardiac MRI. This thesis proposes deep learning based methods for quantifying cardiac MRI. The described methods can be applied for cine MR image quality classification and ventricle segmentation without any human interactions. Investigating combining and fusing magnitude and velocity images can be helpful for left ventricle segmentation in 4D flow MRI, which is not fully explored yet. Moreover, we proposed a network to predict the blood flow pattern from the cine MRI. By combining visualization of the blood flow and myocardial motion in the routinely acquired standard CMR exams, the method can be potentially used in clinical studies.