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

DOI: 10.1155/2014/759805

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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. 1. Introduction Functional magnetic resonance imaging (fMRI) offers a noninvasive method in studying human brain functions by recording blood-oxygen-level-dependent (BOLD) signal changes related to neuronal activity across the brain with high spatial resolution [1]. Real-time fMRI (rt-fMRI) is a method to assess the acquired data for evidence of an experimentally induced effect at every intracerebra voxel individually and simultaneously. In rt-fMRI, data are processed as fast as they are acquired [2]. For real-time fMRI applications, mapping the activations within a repetition time makes it possible to interact with fMRI experiments in a much more efficient way [3]. Online functional mapping enables researchers to monitor data quality, evolve experimental protocols more rapidly, perform interactive experimental paradigms for neurological investigation [4], achieve neurofeedback by providing feedback of brain activation to the subject in real time [5], which may have potential use in clinical applications [6]. In common fMRI experiments, MRI scanner acquires whole brain data at an interval of 2 seconds, also called repetition time. To meet the real-time requirements, all the processing steps of real-time fMRI need to be completed within a repetition time. Simple real-time fMRI processing steps consist of data reconstruction, spatial realignment (head motion correction), and statistical analysis. Among them, incremental statistical analysis on each voxel of the fMRI dataset will result in huge computational costs. To overcome the computational costs of

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