Abstract #3815

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

Vahid Malekian^1,2,3, Danny JJ Wang⁴, Gholam-Ali Hossein-Zadeh^2,5, and Abbas Nasiraei Moghaddam^1,2

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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