Abstract #2031

Suppressing MRI Background Noise via Modeling Phase Variations

Yuchou Chang¹, Gulfam Ahmed Saju¹, Jasina Yu¹, Reza Abiri², Zhiqiang Li³, and Tianming Liu⁴

¹Computer and Information Science, University of Massachusetts Dartmouth, North Dartmouth, MA, United States, ²Electrical, Computer and Biomedical Engineering, University of Rhode Island, Kingston, RI, United States, ³Neuroradiology, Barrow Neurological Institute, Phoenix, AZ, United States, ⁴Computer Science, University of Georgia, Athens, GA, United States

Synopsis

Keywords: Image Reconstruction, Parallel ImagingThe background phase variations exist in coil sensitivities. Optimized phase distribution can minimize the noise of the parallel MRI reconstruction. However, phase variation may be originated from different factors, and it is difficult to be modeled. A random feature method is proposed to model phase variations in coil sensitivities. Through a linear reconstruction using the random phase feature, background noise can be suppressed. Augmented phase features make the linear reconstruction better remove background noise.

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