Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence, Transformer, Swin, Attention
Motivation: Shifted window (Swin) Vision Transformers are increasingly outperforming CNNs in computer vision tasks, particularly if adequate GPU resources are available for training.
Goal(s): In this work, we investigate cascaded Swin transformers with hybrid attention for accelerated MRI reconstruction.
Approach: Our proposed Hybrid SwinV2-MRI-cascade architecture incorporates multi-coil data and k-space consistency constraints while offering a high degree of flexibility in network choice depending on performance requirements and compute capabilities.
Results: Experiments show that both hybrid attention and longer cascades can be used in a granular manner to improve MRI reconstruction performance in Swin transformer networks.
Impact: A highly configurable cascaded hybrid attention SwinV2 transformer architecture for MRI reconstruction is proposed. Its modular nature offers the ability to create transformer networks that fully leverage available training compute resources while producing high quality output.
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