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oai:dspace.mit.edu:1721.1/1389782022-01-15T04:01:42Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022

Setsompop, Kawin Wang, Fuyixue Harvard-MIT Program in Health Sciences and Technology 2022-01-14T14:42:22Z 2022-01-14T14:42:22Z 2021-06 2021-06-15T14:03:02.334Z https://hdl.handle.net/1721.1/138978 https://orcid.org/0000-0001-8975-2775 Magnetic resonance imaging (MRI) is a widely used non-invasive imaging technology for both clinical diagnosis and neuroscientific research. However, the imaging sensitivity and specificity of brain MRI are limited by the well-known technical challenge of MRI acquisition—low image encoding efficiency, leading to limited acquisition speed, spatial resolution and signal-to-noise ratio especially for in-vivo imaging. In order to address these challenges, this thesis presents newly developed spatiotemporal encoding methods, which are used to improve the sensitivity and specificity as well as provide time and cost savings for different MRI applications, including diffusion, quantitative relaxometry and functional imaging. The novel encoding strategies in high-dimensional space together with efficient data sampling schemes allow better use of radio-frequency pulse, modern receiver coil arrays and shared data correlation. The high imaging efficiency provided by these spatiotemporal acquisition methods was demonstrated to help overcome several long-standing challenges in brain MRI, which should help increase its diagnosis power and gain further understanding of the structural and functional organization of the human brain. Ph.D. Massachusetts Institute of Technology In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ Spatiotemporal Encoding Methods for Brain Magnetic Resonance Imaging Thesis application/pdf Doctoral Doctor of Philosophy Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="22d32ef4-2b89-4cc8-a05f-d2fdbe6566ea"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Title>Spatiotemporal Encoding Methods for Brain Magnetic Resonance Imaging</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2021-06</PublicationDate> <Authors> <Author> <DisplayName>Wang, Fuyixue</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://rightsstatements.org/page/InC-EDU/1.0/</License> <Abstract>Magnetic resonance imaging (MRI) is a widely used non-invasive imaging technology for both clinical diagnosis and neuroscientific research. However, the imaging sensitivity and specificity of brain MRI are limited by the well-known technical challenge of MRI acquisition—low image encoding efficiency, leading to limited acquisition speed, spatial resolution and signal-to-noise ratio especially for in-vivo imaging. In order to address these challenges, this thesis presents newly developed spatiotemporal encoding methods, which are used to improve the sensitivity and specificity as well as provide time and cost savings for different MRI applications, including diffusion, quantitative relaxometry and functional imaging. The novel encoding strategies in high-dimensional space together with efficient data sampling schemes allow better use of radio-frequency pulse, modern receiver coil arrays and shared data correlation. The high imaging efficiency provided by these spatiotemporal acquisition methods was demonstrated to help overcome several long-standing challenges in brain MRI, which should help increase its diagnosis power and gain further understanding of the structural and functional organization of the human brain.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>