Abstract #0435

Rapid Quantitative Imaging Using Wave-Encoded Model-Based Deep Learning for Joint Reconstruction

Jaejin Cho^1,2, Borjan Gagoski^2,3, Tae Hyung Kim^1,2, Qiyuan Tian^1,2, Robert Frost^1,2, Itthi Chatnuntawech⁴, and Berkin Bilgic^1,2,5

¹Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States, ²Department of Radiology, Harvard Medical School, Boston, MA, United States, ³Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children’s Hospital, Boston, MA, United States, ⁴National Nanotechnology Center, Pathum Thani, Thailand, ⁵Harvard/MIT Health Sciences and Technology, Cambridge, MA, United States

Synopsis

We propose a wave-encoded model-based deep learning (wave-MoDL) method for joint multi-contrast image reconstruction with volumetric encoding using an interleaved look-locker acquisition sequence with T₂ preparation pulse (3D-QALAS). Wave-MoDL enables a 2-minute acquisition at R=4x3-fold acceleration using a 32-channel array to provide T₁, T₂, and proton density maps at 1 mm isotropic resolution, from which standard contrast-weighted images can also be synthesized.

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.