Work

Regularizing GRAPPA using simultaneous sparsity to recover de-noised images

Year: 2011

Type: conference-paper

Abstract: To enable further acceleration of magnetic resonance (MR) imaging, compressed sensing (CS) is combined with GRAPPA, a parallel imaging method, to reconstruct images from highly undersampled data with significantly improved RMSE compared to reconstructions using GRAPPA alone. This novel combination of GRAPPA and CS regularizes the GRAPPA kernel computation step using a simultaneous sparsity penalty function of the coil images. This approach can be implemented by formulating the problem as the joint optimization of the least squares fit of the kernel to the ACS lines and the sparsity of the images generated using GRAPPA with the kernel.

Source: Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE

DOI: 10.1117/12.896655

Authors: Daniel S. Weller, Jonathan R. Polimeni, Leo Grady, Lawrence L. Wald, Elfar Adalsteinsson, Vivek K. Goyal

Institutions: Massachusetts Institute of Technology, Harvard University, Massachusetts General Hospital, Siemens (United States)

Language: English

Topic: Advanced MRI Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

Open Access status: bronze

Funders: National Science Foundation, National Institutes of Health, National Institute of Biomedical Imaging and Bioengineering, National Center for Research Resources

Awards: G3890511387, G5520316217, G6671297155, Spiral Spectroscopic Human Neuroimaging, CAREER: Acquisition, Approximation, and Compression - An Integrated Study