Loading [a11y]/accessibility-menu.js
Accelerated dynamic MRI using self expressiveness prior | IEEE Conference Publication | IEEE Xplore

Accelerated dynamic MRI using self expressiveness prior


Abstract:

We introduce a self-expressiveness prior to exploit the redundancies between voxel profiles in dynamic MRI. Specifically, we express the temporal profile of each voxel in...Show More

Abstract:

We introduce a self-expressiveness prior to exploit the redundancies between voxel profiles in dynamic MRI. Specifically, we express the temporal profile of each voxel in the dataset as a sparse linear combination of temporal profiles of other voxels. This scheme can be thought of as a direct approach to exploit the inter-voxel redundancies as opposed to low-rank and dictionary based schemes, which learn dictionaries from the data to represent the signal. The proposed representation may be interpreted as a union of subspaces model or as an analysis transform. The use of this algorithm is observed to considerably improve the recovery of myocardial perfusion MRI data from under sampled measurements.
Date of Conference: 16-19 April 2015
Date Added to IEEE Xplore: 23 July 2015
Electronic ISBN:978-1-4799-2374-8

ISSN Information:

Conference Location: Brooklyn, NY, USA

References

References is not available for this document.