Keywords: Analysis/Processing, fMRI Analysis, BOLD, VASO, brain layer profiling, brain layer activity, GAN model
Motivation: Though useful, implementing VASO for layer activity analysis requires specialized pulse sequences, which can be more complex than standard BOLD fMRI sequences.
Goal(s): To develop a GAN model that synthetically generate VASO contrast images from acquired BOLD images.
Approach: We trained a GAN model from paired BOLD and VASO image dataset. Model was evaluated using metrics like PSNR, SSIM, and MAE and layer profiling.
Results: Our GAN model translates the acquired BOLD contrast images into VASO contrast images with an average SSIM of 0.85 ± 0.02. Further, brain layer profiling shows agreement between acquired and GAN-assisted VASO images.
Impact: We present a method to eliminate the need for implementing VASO pulse sequence by synthetically generating VASO images from acquired BOLD 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.
Keywords