Meeting Banner
Abstract #3164

CEST Mapping from Undersampled Z-spectra in the Brain Using Deep Learning

Karandeep S Cheema1,2, Pei Han1, Hsu-Lei Lee1, Hui Han1, Yibin Xie1, Anthony Christodoulou1,2, and Debiao Li1,2
1Cedars Sinai Medical Center, Los Angeles, CA, United States, 2Bioengineering, University of California, Los Angeles (UCLA), Los Angeles, CA, United States

Synopsis

Keywords: CEST & MT, Machine Learning/Artificial Intelligence, CEST, frequency offsets, Fisher Information gainChemical exchange saturation transfer (CEST) imaging uses radio frequency pulses at different frequency offsets to generate CEST maps. In this work, we used deep learning to calculate CEST maps from steady-state CEST (ss-CEST) images at undersampled frequency offsets, reducing the total scan time by a factor of 3.5. The Z-spectrum was undersampled by selecting the top 15 frequency offsets from Fisher information gain analysis. Fitting results from the proposed method were compared with those from multi-pool fitting with fully sampled Z-spectrum. We showed that it is feasible to reconstruct CEST maps from undersampled, field uncorrected ss-CEST images.

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