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Computational and Mathematical Methods in Medicine
Volume 2014, Article ID 759805, 7 pages
http://dx.doi.org/10.1155/2014/759805
Research Article

Incremental Activation Detection for Real-Time fMRI Series Using Robust Kalman Filter

China National Digital Switching System Engineering and Technological Research Center, Zheng Zhou 450002, China

Received 27 September 2013; Accepted 6 December 2013; Published 6 January 2014

Academic Editor: Sabri Arik

Copyright © 2014 Liang Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Real-time functional magnetic resonance imaging (rt-fMRI) is a technique that enables us to observe human brain activations in real time. However, some unexpected noises that emerged in fMRI data collecting, such as acute swallowing, head moving and human manipulations, will cause much confusion and unrobustness for the activation analysis. In this paper, a new activation detection method for rt-fMRI data is proposed based on robust Kalman filter. The idea is to add a variation to the extended kalman filter to handle the additional sparse measurement noise and a sparse noise term to the measurement update step. Hence, the robust Kalman filter is designed to improve the robustness for the outliers and can be computed separately for each voxel. The algorithm can compute activation maps on each scan within a repetition time, which meets the requirement for real-time analysis. Experimental results show that this new algorithm can bring out high performance in robustness and in real-time activation detection.