Meeting Banner
Abstract #3503

Joint recovery of time aligned multi-slice dynamic speech MR images from under-sampled data using a deep generative manifold model

Rushdi Zahid Rusho1, Qing Zou2, Mathews Jacob2, and Sajan Goud Lingala1,3
1Roy J. Carver Department of Biomedical Engineering, The University of Iowa, Iowa City, IA, United States, 2Electrical and Computer Engineering, The University of Iowa, Iowa City, IA, United States, 3Department of Radiology, The University of Iowa, Iowa City, IA, United States

Synopsis

Dynamic speech MRI is a powerful tool to characterize complex speech articulations. Current accelerated 2D dynamic speech MRI schemes can achieve high time resolutions of the order of (10-20 ms) while sequentially acquiring multiple 2D slices. However, the complex articulatory motion is difficult to interpret jointly across the slices due to mis-alignment of motion patterns. Here, we apply a novel generative manifold model which reconstructs and generates a time aligned multi-slice 2D speech dataset at 18 ms/frame from under-sampled k-space v/s time data sequentially acquired from multiple 2D slices. We evaluate this scheme on two speakers producing repeated speech tasks.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.

Keywords