Low-Rank O-Space Reconstruction

Haifeng Wang^{1}, Emre Kopanoglu^{1}, R. Todd Constable^{1,2}, and Gigi Galiana^{1}

Figure 1 shows simulation results, including reference, radial and O-Space imaging at reduction factors of 8 and 16. Few improvements to reduce aliasing artifacts are observed if using the CS reconstruction in radial and O-Space imaging, but applying Low-Rank reconstruction clearly reduces undersampling artifacts, particularly for the O-Space encoded images. Figure 2 summarizes these results for a range of reduction factors and reconstruction methods applied to both radial and O-Space images. The proposed Low-Rank method improves NMSE over CS reconstruction, especially at high reduction factors, and the improvement is greater for O-Space encoded images. In Figure 3, experimental phantom results also show the proposed Low-Rank O-Space method reduces artifacts and recovers more detail (red arrows) than the either Kaczmarz or CS reconstruction of O-Space with pseudo-random disturbance. Moreover it is better than the best image attainable from radial encoding.

Support from the NIH, grant numbers R01-EB012289, R01-EB016978 and K01-CA168977 are gratefully acknowledged.

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Figure 1: Simulations of geometric phantom with the 64×64 resolution including
reference, radial imaging, O-Space imaging and their different images with the
referent images at reduction factors of 8 and 16.

Figure 2: NMSE (Normalized Mean Square Error) of simulations (64×64) at different
reduction factors including corresponding reconstruction methods of radial and O-Space imaging.

Figure 3. Experimental results with the 128×128 resolution including
reference, radial imaging, and O-Space imaging at a reduction factor of 8.

Proc. Intl. Soc. Mag. Reson. Med. 24 (2016)

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