2026-09-18T18:39:26Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1475482023-01-20T03:04:47Zcom_1721.1_7582com_1721.1_7581col_1721.1_131022
Setsompop, Kawin
Iyer, Siddharth Srinivasan
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2023-01-19T19:57:45Z
2023-01-19T19:57:45Z
2022-09
2022-10-19T19:08:34.803Z
https://hdl.handle.net/1721.1/147548
Magnetic Resonance Imaging (MRI) is a non-invasive but slow imaging modality that provides unparalleled flexibility in acquiring multiple forms of soft-tissue contrast. Recently, there has been a lot of interest in mapping the inherent magnetization properties of the underlying human tissue and in temporally resolving the acquired data. Broadly classified as spatio-temporal MRI, these methods yield unprecedented details of the human anatomy and function, improving clinical diagnostic performance and prognosis. However, such methods are inherently high-dimensional, resulting in encoding-intensive data acquisition processes and computationally-intensive reconstructions. This begets long acquisition and reconstruction times, making such methods difficult to integrate into clinical workflows. This thesis aims to improve the acquisition and reconstruction times of spatio-temporal MRI to enable its use in clinical and neuroscientific setting.
Ph.D.
Massachusetts Institute of Technology
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https://rightsstatements.org/page/InC-EDU/1.0/
On Improving the Acquisition and Reconstruction Of Spatio-Temporal Magnetic Resonance Imaging
Thesis
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Doctoral
Doctor of Philosophy
Publication
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<Abstract>Magnetic Resonance Imaging (MRI) is a non-invasive but slow imaging modality that provides unparalleled flexibility in acquiring multiple forms of soft-tissue contrast. Recently, there has been a lot of interest in mapping the inherent magnetization properties of the underlying human tissue and in temporally resolving the acquired data. Broadly classified as spatio-temporal MRI, these methods yield unprecedented details of the human anatomy and function, improving clinical diagnostic performance and prognosis. However, such methods are inherently high-dimensional, resulting in encoding-intensive data acquisition processes and computationally-intensive reconstructions. This begets long acquisition and reconstruction times, making such methods difficult to integrate into clinical workflows. This thesis aims to improve the acquisition and reconstruction times of spatio-temporal MRI to enable its use in clinical and neuroscientific setting.</Abstract>
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