Abstract #1798

Comparison of strict sparsity and low-rank constraints for accelerated FMRI data reconstruction

Charles Guan¹ and Mark Chiew²

¹Electrical Engineering, Stanford University, Fremont, CA, United States, ²FMRIB Centre, University of Oxford, Oxford, United Kingdom

Functional MRI has been slow to benefit from data acceleration techniques based on non-linear image reconstruction. We present a comparison of two non-linear image reconstruction methods based on sparsity and low-rank models of FMRI data. k-t FOCUSS uses an asymptotic L1 minimization program to solve for a sparse x-f reconstruction. In contrast, k-t FASTER solves for a spatio-temporally low-rank reconstruction using an iterative hard thresholding and matrix shrinkage algorithm, without requiring a pre-specified basis. We applied each algorithm to incoherently sampled FMRI data and demonstrate that the strict rank-constraint method outperforms spectral- and Karhunen-Loeve Transform (KLT)-sparsity across different metrics.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.