Keywords: Machine Learning/Artificial Intelligence, Image ReconstructionDeep learning architectures such as Convolutional Neural Networks (CNNs) are widely used alongside fixed k-space undersampling for reconstructing highly undersampled MRI k-space data. However, CNN-based architectures may perform sub-optimally due to their limited ability to capture long range dependencies, and fixed undersampling patterns may not be amenable to optimal reconstruction. We propose dual-domain (image and k-space), transformer-based reconstruction architectures paired with learning-based undersampling to reconstruct undersampled k-space data. Experimental results demonstrate improved reconstruction quality compared to CNN-based (UNet) architecture across 5x to 100x acceleration factors; dual domain outperformed single domain reconstructions.
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