Reconstruction algorithms for MRI
Thesis (Ph. D.)--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/1721.1/79210 |
| _version_ | 1810985029627543552 |
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| author | Bilgic̦, Berkin |
| author2 | Elfar Adalsteinsson. |
| author_facet | Elfar Adalsteinsson. Bilgic̦, Berkin |
| author_sort | Bilgic̦, Berkin |
| collection | MIT |
| description | Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013. |
| first_indexed | 2024-09-23T11:11:50Z |
| format | Thesis |
| id | mit-1721.1/79210 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T11:11:50Z |
| publishDate | 2013 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/792102019-04-10T12:25:02Z Reconstruction algorithms for MRI Reconstruction algorithms for Magnetic Resonance Imaging Bilgic̦, Berkin 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 (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013. Cataloged from PDF version of thesis. Includes bibliographical references (p. 135-142). This dissertation presents image reconstruction algorithms for Magnetic Resonance Imaging (MRI) that aims to increase the imaging efficiency. Algorithms that reduce imaging time without sacrificing the image quality and mitigate image artifacts are proposed. The goal of increasing the MR efficiency is investigated across multiple imaging techniques: structural imaging with multiple contrasts preparations, Diffusion Spectrum Imaging (DSI), Chemical Shift Imaging (CSI), and Quantitative Susceptibility Mapping (QSM). The main theme connecting the proposed methods is the utilization of prior knowledge on the reconstructed signal. This prior often presents itself in the form of sparsity with respect to either a prespecified or learned signal transformation. by Berkin Bilgic. Ph.D. 2013-06-17T19:47:47Z 2013-06-17T19:47:47Z 2013 2013 Thesis http://hdl.handle.net/1721.1/79210 844752790 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 142 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Bilgic̦, Berkin Reconstruction algorithms for MRI |
| title | Reconstruction algorithms for MRI |
| title_full | Reconstruction algorithms for MRI |
| title_fullStr | Reconstruction algorithms for MRI |
| title_full_unstemmed | Reconstruction algorithms for MRI |
| title_short | Reconstruction algorithms for MRI |
| title_sort | reconstruction algorithms for mri |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/79210 |
| work_keys_str_mv | AT bilgicberkin reconstructionalgorithmsformri AT bilgicberkin reconstructionalgorithmsformagneticresonanceimaging |