Abstract #0872

Low-Rank O-Space Reconstruction

Haifeng Wang¹, Emre Kopanoglu¹, R. Todd Constable^1,2, 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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