A machine learning based method for sensitivity estimation for accelerated magnetic resonance spectroscopy imaging using phased array coils

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%.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2018

Author dc:creator

• Perkins, Kevin

Contributors dc:contributor

• Liang, Zhi-Pei

• Magnetic Resonance Spectroscopic Imaging, MRSI, Parallel Imaging, SENSE, Deep Learning, DnCNN

Statement dc:rights

• Copyright 2018 Kevin Perkins

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/100976

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/100976