Accelerated Cardiovascular MRI using Compressed Sensing and Artificial Intelligence

Shen, Daming

doi:https://doi.org/10.21985/n2-fyrb-8w56

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Accelerated Cardiovascular MRI using Compressed Sensing and Artificial Intelligence

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Cardiovascular disease is the leading cause of death in US and non-invasive cardiac imaging has vital importance for early detection and diagnosis of heart disease. Cardiac Magnetic Resonance (CMR) is arguably the most versatile imaging modality and capable of a comprehensive evaluation of heart disease without ionization radiation. Despite the advantages of CMR, it is seldom used (only 1% footprint) due to lack of availability and higher cost, which is mainly caused by the long scan times. Meanwhile, many accelerated CMR acquisition and quantification methods were feasible, but their clinical translation was limited due to long image reconstruction time and long manual processing time (e.g. segmentation). The purpose of this dissertation was to describe the development and validation of accelerated CMR methods using compressed sensing (CS) and deep learning (DL) to overcome current limitations. This dissertation includes the following topics: (i) CS image reconstruction of accelerated coronary quiescent-interval slice-selective (QISS) magnetic resonance angiography (MRA) that enabled single-shot, free-breathing acquisition, (ii) a newly developed high-resolution late gadolinium enhancement (LGE) CMR sequence with novel CS image reconstruction that provides multi-TI image contrast, (iii) rapid image reconstruction of highly undersampled real‐time cine using deep learning without significant loss in image quality, visual scores and functional parameters, (iv) automated image segmentation of biventricular tissue phase mapping (TPM) using deep learning.

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