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Proceedings Paper

Reconstructing MR images from under- or unevenly-sampled k-space
Author(s): Linda Vu; Arsen R. Hajian; Paul H. Calamai; Andrew T. Cenko; Sarbast Rasheed; Jae K. Kim; Cameron Piron; Simon S. So; Jeff T. Meade; Kevin H. Knuth
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Paper Abstract

In MRI, non-rectilinear sampling trajectories are applied in k-space to enable faster imaging. Traditional image reconstruction methods such as a fast Fourier transform (FFT) can not process datasets sampled in non-rectilinear forms (e.g., radial, spiral, random, etc.) and more advanced algorithms are required. The Fourier reduction of optical interferometer data (FROID) algorithm is a novel image reconstruction method1-3 proven to be successful in reconstructing spectra from sparsely and unevenly sampled astronomical interferometer data. The framework presented allows a priori information, such as the locations of the sampled points, to be incorporated into the reconstruction of images. In this paper, the FROID algorithm has been adapted and implemented to reconstruct magnetic resonance (MR) images from data acquired in k-space where the sampling positions are known. Also, simulated data, including randomly sampled data, are tested and analyzed.

Paper Details

Date Published: 27 August 2010
PDF: 14 pages
Proc. SPIE 7800, Image Reconstruction from Incomplete Data VI, 780006 (27 August 2010); doi: 10.1117/12.858833
Show Author Affiliations
Linda Vu, Univ. of Waterloo (Canada)
Arsen R. Hajian, Univ. of Waterloo (Canada)
Paul H. Calamai, Univ. of Waterloo (Canada)
Andrew T. Cenko, Univ. of Waterloo (Canada)
Sarbast Rasheed, Univ. of Waterloo (Canada)
Jae K. Kim, Thunder Bay Regional Research Institute (Canada)
Cameron Piron, Sentinelle Medical Inc. (Canada)
Simon S. So, Univ. of Waterloo (Canada)
Jeff T. Meade, Univ. of Waterloo (Canada)
Kevin H. Knuth, Univ. at Albany (United States)


Published in SPIE Proceedings Vol. 7800:
Image Reconstruction from Incomplete Data VI
Philip J. Bones; Michael A. Fiddy; Rick P. Millane, Editor(s)

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