Model-based reconstruction of magnetic resonance spectroscopic imaging
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2013
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| Online Access: | http://hdl.handle.net/1721.1/82376 |
| _version_ | 1826190232769265664 |
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| author | Chatnuntawech, Itthi |
| author2 | Elfar Adalsteinsson. |
| author_facet | Elfar Adalsteinsson. Chatnuntawech, Itthi |
| author_sort | Chatnuntawech, Itthi |
| collection | MIT |
| description | Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013. |
| first_indexed | 2024-09-23T08:37:06Z |
| format | Thesis |
| id | mit-1721.1/82376 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T08:37:06Z |
| publishDate | 2013 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/823762019-04-09T18:46:42Z Model-based reconstruction of magnetic resonance spectroscopic imaging Chatnuntawech, Itthi Elfar Adalsteinsson. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013. Cataloged from PDF version of thesis. Includes bibliographical references (p. 77-80). Magnetic resonance imaging (MRI) is a medical imaging technique that is used to obtain images of soft tissue throughout the body. Since its development in the 1970s, MRI has gained tremendous importance in clinical practice because it can produce high quality images of diagnostic value in an ever expanding range of applications from neuroimaging to body imaging to cancer. By far the dominant signal source in MRI is hydrogen nuclei in water. The presence of water at high concentration (-50M) in body tissue, combined with signal contrast modulation induced by the local environment of water molecules, accounts for the success of MRI as a medical imaging modality. As opposed to conventional MRI, which derives its signal from the water component, magnetic resonance spectroscopy (MRS) acquires the magnetic resonance signal from other chemical components, most frequently various metabolites in the brain, but also signals from tumors in breast and prostate. The spectroscopic signal arises from low concentration (-1 - 10mM) compounds, but in spite of the challenges posed by the resulting low signal-to-noise ratio (SNR), the development of MRS is motivated by the desire to directly observe signal sources other than water. The combination of MRS with spatial encoding is called magnetic resonance spectroscopic imaging (MRSI). MRSI captures not only the relative intensities of metabolite signals at each voxel, but also their spatial distributions. While MRSI has been proven to be clinically useful, it suffers from fundamental tradeoffs due to the inherently low SNR, such as long acquisition time and low spatial resolution. In this thesis, techniques that combine benefits from both model-based reconstruction methods and regularized reconstructions with prior knowledge are proposed and demonstrated for MRSI. These methods address constraints on acquisition time in MRSI by undersampling data during acquisition in combination with improved image reconstruction methods. by Itthi Chatnuntawech. S.M. 2013-11-18T19:15:32Z 2013-11-18T19:15:32Z 2013 2013 Thesis http://hdl.handle.net/1721.1/82376 862074792 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 80 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Chatnuntawech, Itthi Model-based reconstruction of magnetic resonance spectroscopic imaging |
| title | Model-based reconstruction of magnetic resonance spectroscopic imaging |
| title_full | Model-based reconstruction of magnetic resonance spectroscopic imaging |
| title_fullStr | Model-based reconstruction of magnetic resonance spectroscopic imaging |
| title_full_unstemmed | Model-based reconstruction of magnetic resonance spectroscopic imaging |
| title_short | Model-based reconstruction of magnetic resonance spectroscopic imaging |
| title_sort | model based reconstruction of magnetic resonance spectroscopic imaging |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/82376 |
| work_keys_str_mv | AT chatnuntawechitthi modelbasedreconstructionofmagneticresonancespectroscopicimaging |