Meeting Banner
Abstract #3869

Robust GRASP: A novel approach using the Huber norm in projection space for robust data consistency in undersampled radial MRI

Marcelo V. W. Zibetti1, Rebecca Ramb2, Li Feng2, Ricardo Otazo2, Leon Axel2, and Gabor T. Herman3

1NYU/CUNY/UTFPR, New York, NY, United States, 2Radiology, NYU School of Medicine, New York, NY, United States, 3Computer Science, CUNY, New York, NY, United States

Robust data consistency using the Huber norm is proposed for compressed sensing radial MRI to reduce artifacts associated with outliers in the acquired data that cannot be removed by the sparse reconstruction. System imperfections such as chemical shift can introduce this type of large data distortions, or outliers. The quadratic shape of the usually employed Euclidean norm for data consistency is very sensitive to very large errors. In the proposed method, named RObust Golden-angle Radial Sparse Parallel MRI (ROGRASP), the Huber norm enables large errors to remain in the data discrepancy, not transferring them to the reconstructed image. In vivo acquisitions with outlier-contaminated data illustrate this improvement in quality for free-breathing cardiac MRI.

This abstract and the presentation materials are available to members only; a login is required.

Join Here