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.
