2026-09-20T03:25:16Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/32073 2023-07-10 col_2142_5131 col_2142_8888 com_2142_5130 com_2142_8887 com_2142_234

Liang, Zhi-Pei Fu, Maojing 2012-06-27T21:31:36Z 2014-06-28T10:00:29Z 2012-05 2012-06-27T21:31:36Z 2012-05 Dynamic speech imaging is a powerful technique for real-time visualization of speech dynamics. As a promising modality for dynamic speech imaging, magnetic resonance imaging (MRI) can provide good soft-tissue contrast in an arbitrary imaging plane with a non-invasive procedure. However, conventional MRI suffers from low spatiotemporal resolution, which limits its applica-tion in dynamic speech imaging. This thesis presents a novel model-based dynamic MR imaging method to capture speech dynamics in high spatiotemporal resolution. Specifically, high spatiotemporal resolution reconstruction from very sparsely sampled data is achieved using the partial separability (PS) model, which takes advantage of the spatiotemporal correlations of dynamic speech images. The sampling pattern is also optimized to better capture speech dynamics. The spatial-spectral sparsity constraint is further incorporated into the basic PS model-based reconstruction to improve reconstruction quality. The effectiveness of the above approaches is demonstrated through systematic simulations and preliminary in vivo experiments. Item withdrawn by Mark Zulauf (zulauf@illinois.edu) on 2012-04-26T12:38:09Z Item was in collections: University of Illinois Theses & Dissertations (ID: 1) No. of bitstreams: 1 Fu_Maojing.pdf: 2407904 bytes, checksum: b33764a19291d7ef41cbf9a3e12fc64a (MD5) Made available in DSpace on 2012-06-27T21:31:36Z (GMT). No. of bitstreams: 2 Fu_Maojing.pdf: 2407904 bytes, checksum: b33764a19291d7ef41cbf9a3e12fc64a (MD5) license.txt: 4056 bytes, checksum: 04c99059bb5bc851a01f7980572f6f19 (MD5) Item marked as restricted to the 'Administrator' Group (id=1) by William Ingram (wingram2@illinois.edu) on 2012-06-27T21:32:50Z Item is restricted until 2014-06-27T21:32:23Z Item reinstated by Sarah Shreeves (sshreeve@illinois.edu) on 2014-06-28T10:00:29Z Item was in collections: Graduate Theses and Dissertations at Illinois (ID: 204) Dissertations and Theses - Electrical and Computer Engineering (ID: 446) No. of bitstreams: 2 Fu_Maojing.pdf: 2407904 bytes, checksum: b33764a19291d7ef41cbf9a3e12fc64a (MD5) license.txt: 4056 bytes, checksum: 04c99059bb5bc851a01f7980572f6f19 (MD5) Item released from any restrictions by Sarah Shreeves (sshreeve@illinois.edu) on 2014-06-28T10:00:29Z http://hdl.handle.net/2142/32073 en Copyright 2012 Maojing Fu spatiotemporal modeling partially separable functions speech imaging dynamic MRI Magnetic resonance imaging (MRI) Dynamic speech imaging with low-rank approximation text Electrical & Computer Eng 1933 Electrical & Computer Engr 1200 University of Illinois at Urbana-Champaign Thesis M.S. MS:Electr & Computer Eng-UIUC 10KS1200MS