Abstract #0159

SwinV2-MRI: Accelerated Multi-Coil MRI Reconstruction using Shifted Window Vision Transformers

Tahsin Rahman¹, Sergio D. Cabrera¹, and Ali Bilgin²

¹Electrical and Computer Engineering, The University of Texas at El Paso, El Paso, TX, United States, ²Electrical and Computer Engineering, University of Arizona, Tucson, AZ, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Image Reconstruction

Vision transformers (ViT) are increasingly utilized in computer vision and have been shown to outperform CNNs in many tasks. In this work, we explore the use of Shifted Window (Swin) transformers for accelerated MRI reconstruction. Our proposed SwinV2-MRI architecture enables the use of multi-coil data and k-space consistency constraints with Swin transformers. Experimental results show that the proposed architecture outperforms CNNs even when trained on a limited dataset and without any pre-training.

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