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sparse-domain-transformation
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Deep Learning for Compressed Sensing MRI Reconstruction
, M.Sc. Thesis Sharif University of Technology ; Vosoughi Vahdat, Bijan (Supervisor) ; Fatemizadeh, Emad (Co-Supervisor)
Abstract
Medical imaging is an indispensable component of modern medical research as well as clinical practice. However, Magnetic resonance Imaging and Computational tomography are expensive, and it is difficult to be used in many scenarios worldwide. As such, many parts of the world still do not have sufficient accessibility to these techniques. To make medical devices more accessible, affordable and efficient, it is crucial to reflect upon our current imaging paradigm for smarter imaging. According to Compressed Sensing theory, if there is information other than the signal bandwidth value, such as the being sparse in a suitable domain, nonlinear optimization methods can be used to accurately...