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Abstract #4629

Improvements of Reconstruction Performance in a Simultaneously Multislice Imaging Using Deep Learning-based Image Separation

Masaki FURUTA1, Satoshi ITO1, and Kazuki YAMATO1
1Graduate School of Regional Development and Creativity, Utsunomiya University, Utsunomiya, Japan

Synopsis

Keywords: Machine Learning/Artificial Intelligence, AI/ML Image Reconstruction

Motivation: A new simultaneous multi-slice imaging (SMS) that does not use the sensitivity of receiver RF coils has been proposed. Image quality could be improved with the use of an appropriate network.

Goal(s): Our goal is to improve the quality of reconstructed images in proposed SMS.

Approach: mage reconstruction by SARA-GAN, SwinMR and 3D U-Net were tested and the results were compared with our previous work using U-Net.

Results: Simulation experiments showed that higher PSNRs were obtained by SwinMR, and the best LPIPS was achieved by SARA-GAN, showing a significant improvements of image quality in appearance.

Impact: The image quality of Deep learning-based SMS has been greatly improved.

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