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Title:A machine learning based method for sensitivity estimation for accelerated magnetic resonance spectroscopy imaging using phased array coils
Author(s):Perkins, Kevin
Advisor(s):Liang, Zhi-Pei
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:M.S.
Genre:Thesis
Subject(s):Magnetic Resonance Spectroscopic Imaging, MRSI, Parallel Imaging, SENSE, Deep Learning, DnCNN
Abstract:Magnetic resonance spectroscopic imaging (MRSI) enables in-vivo analysis of the spatial distribution of chemicals within the human body. Through MRSI, one can infer the concentration of various metabolites in different regions throughout the body. While the medical implications of such an imaging paradigm are remarkable, a poor trade-off between imaging speed and image resolution has stunted development of MRSI applications. A combination of many technological advancements is necessary to bring MRSI to its full potential; one advancement is an accelerated imaging technique known as parallel imaging. Parallel imaging exploits differences in receiver sensitivities in phased array coils to recover additional location information. Accurate estimation of the sensitivity profiles is necessary to prevent parallel imaging induced artifacts. However, accurate sensitivity profile estimations require fully sampled high-resolution images which adds an excessive data acquisition burden. A novel sensitivity profile estimation strategy which relies on deep learning is presented. It is shown how prior information in the form of learned image feature representations may be combined with noisy imaging data to produce high-resolution, artifact-free sensitivity profiles. An in-vivo experiment demonstrates the effectiveness of the proposed method. The relative SENSE reconstruction error for the proposed method is 1.96% compared to a signal processing baseline of 2.52%.
Issue Date:2018-04-15
Type:Text
URI:http://hdl.handle.net/2142/100976
Rights Information:Copyright 2018 Kevin Perkins
Date Available in IDEALS:2018-09-04
Date Deposited:2018-05


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