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Abstract #0872

Low-Rank O-Space Reconstruction

Haifeng Wang1, Emre Kopanoglu1, R. Todd Constable1,2, and Gigi Galiana1

1Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, United States, 2Department of Neurosurgery, Yale University, New Haven, CT, United States

Low-Rank O-Space presents a scheme to incorporate O-Space imaging with Low-Rank matrix recovery. The Low-Rank reconstruction based on iterative nonlinear conjugate gradient algorithm is applied to substitute the previous Kaczmarz and Compressed Sensing (CS) reconstructions to recover highly undersampled O-Space data. The simulations and experiments illustrate the proposed scheme can remove artifacts and noise in O-Space imaging at high reduction factors, compared to results recovered by Kaczmarz and CS. Moreover, the proposed method does not need to modify the conventional O-Space pulse sequences, and reconstruction results are better than those in radial imaging recovered by Kaczmarz, CS, or Low-Rank methods.

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