Deep learning based accelerated MRI reconstruction using mixed CNN and vision transformer

A non-transitory computer-readable medium comprising instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• providing a zero-filled image to a dual-branch image reconstruction network having a transformer branch and a convolutional neural network (CNN) branch, the zero-filled image having been generated using zero-filled k-space data;
• generating, using the CNN branch and based on the zero-filled image, a set of CNN output features;
• partitioning the zero-filled image to form a partitioned image;
• generating, using the transformer branch and based on the partitioned image, a set of transformer output features;
• fusing the set of transformer output features with the set of CNN features to form a fused output;
• and
• generating a reconstructed image from the fused output.

A system comprising:

• one or more processors;
• and
• a computer-readable medium comprising instructions that, when executed by the one or more processors, cause the one or more processors to perform operations comprising:
• providing a zero-filled image to a dual-branch image reconstruction network having a transformer branch and a convolutional neural network (CNN) branch, the zero-filled image having been generated using zero-filled k-space data;
• generating, using the CNN branch and based on the zero-filled image, a set of CNN output features;
• partitioning the zero-filled image to form a partitioned image;
• generating, using the transformer branch and based on the partitioned image, a set of transformer output features;
• fusing the set of transformer output features with the set of CNN features to form a fused output;
• and
• generating a reconstructed image from the fused output.