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Abstract #3815

The Novel Anisotropic Filtering Method for Noise Reduction in fMRI Utilizing Phase Information

Vahid Malekian1,2,3, Danny JJ Wang4, Gholam-Ali Hossein-Zadeh2,5, and Abbas Nasiraei Moghaddam1,2

1Biomedical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran, 2School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran, 3Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands, 4Neurology, UCLA, Los Angeles, CA, United States, 5School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran

To minimize noise and artifacts in fMRI studies, authors have mostly focused on magnitude-based filtering methods and have neglected phase data due to its noisy nature. However, fMRI is a complex-valued data with also valuable phase information. Here, we propose a novel spatial weighted averaging method which uses the phase information along with magnitude to create a reference signal and utilize it iteratively to updates weights. We evaluate the method on experimental A-BOSS fMRI dataset and compared with conventional smoothing methods. The results indicate that the approach can suppress noise effectively and preserve the boundaries of active regions.

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