Reconstructing MR Images from Under-or Unevenly-Sampled k-Space

Vu, Linda and Hajian, Arsen and Calamai, Paul and Cenko, Andrew and Rasheed, Sarbast and Kim, Jae (2010) Reconstructing MR Images from Under-or Unevenly-Sampled k-Space. In: SPIE Image Reconstruction from Incomplete Data VI, August 1-5, 2010, San Diago, California, USA.

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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.

Item Type: Conference or Workshop Item (Paper)
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science
Depositing User: Dr. Sarbast Rasheed
Date Deposited: 15 May 2016 06:12
Last Modified: 15 May 2016 06:12

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