{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/32073"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/32073","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Dynamic speech imaging with low-rank approximation","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Fu, Maojing"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Liang, Zhi-Pei"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012-06-27T21:31:36Z","date_published":"2012-06-27T21:31:36Z","updated_at":"2026-07-22T22:25:30Z","subjects":["spatiotemporal modeling","partially separable functions","speech imaging","dynamic MRI","Magnetic resonance imaging (MRI)"],"languages":["en"],"rights":["Copyright 2012 Maojing Fu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/32073","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Liang, Zhi-Pei"]},{"key":"dc:creator","label":"Author","values":["Fu, Maojing"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2012-06-27T21:31:36Z","2014-06-28T10:00:29Z","2012-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["spatiotemporal modeling","partially separable functions","speech imaging","dynamic MRI","Magnetic resonance imaging (MRI)"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2012 Maojing Fu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/32073"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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"]},{"key":"dc:title","label":"Title","values":["Dynamic speech imaging with low-rank approximation"]}]}],"canonical_facts":{"dc:contributor":["Liang, Zhi-Pei"],"dc:creator":["Fu, Maojing"],"dc:date":["2012-06-27T21:31:36Z","2014-06-28T10:00:29Z","2012-05"],"dc:description":["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"],"dc:identifier":["http://hdl.handle.net/2142/32073"],"dc:language":["en"],"dc:rights":["Copyright 2012 Maojing Fu"],"dc:subject":["spatiotemporal modeling","partially separable functions","speech imaging","dynamic MRI","Magnetic resonance imaging (MRI)"],"dc:title":["Dynamic speech imaging with low-rank approximation"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:30Z"}