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Abstract #5663

Improving the PI+CS Reconstruction for Highly Undersampled Multi-contrast MRI using Local Deep Network

Enhao Gong1, Greg Zaharchuk2, and John Pauly1

1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States

A typical clinical MR protocol includes multiple scans with different contrasts for complementary diagnostic information. Various methods have been proposed to specifically accelerate multi-contrast scans by using more complicated sparsity regularization in PI+CS.

Here we proposed a Non-iterative Deep-Learning approach to further improve existing methods for highly undersampled multi-contrast MRI reconstruction.

This method uses a Sequential+Joint+Local scheme, which takes fast PI+CS reconstruction as the initial input, uses a Deep-Network on local patches, and efficiently generates a better reconstruction for different contrasts with reduced noise and artifacts.

Experiments demonstrate the proposed method has superior performance compared with existing PI+CS methods.

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