Abstract #4949

Locally high rank reconstruction through Partial Separability model (PS-LHR) with regional optimized temporal basis (ROT) of dynamic speech MRI

Riwei Jin¹, Yudu Li², Fangxu Xing³, Imani Gilbert⁴, Jamie Perry⁴, Jonghye Woo³, Zhi-pei Liang⁵, and Brad Sutton¹

¹Department of Bioengineering, University of Illinois Urbana-Champaign, Champaign, IL, United States, ²National Center for Supercomputing Applications, Champaign, IL, United States, ³Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital/Harvard Medical School, Boston, MA, United States, ⁴Department of Communication Sciences and Disorders, East Carolina University, Greenville, NC, United States, ⁵6 Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Champaign, IL, United States

Synopsis

Keywords: Image Reconstruction, Sparse & Low-Rank ModelsTo optimize the reconstruction quality of isotropic 3D dynamic speech magnetic resonance imaging with large scan volume, we applied two novel methods based on the Partial Separability model theory: 1. Locally High-Rank reconstruction through Partial Separability model (PS-LHR) which enables higher rank to be devoted to the dynamic speech region. 2. Implementation of Regional-Optimized Temporal basis (ROT) to focus the temporal navigator information on the speech region. The improvement in reconstruction quality was seen to decrease the noise of regions of the image outside the area of interest and increase dynamic smoothness in the speech region.

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