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Abstract #4296

BOLD Acquisitions and GAN Synthetized VASO Contrasts for Rapid Layer-dependent fMRI

Ashish Saxena1, Divya Bharti2, Teck Beng Desmond Yeo3, and Afis Ajala3
1GE Healthcare, Bangalore, India, 2IIT Madras, Chennai, India, 3GE Healthcare, Niskayuna, NY, United States

Synopsis

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.

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