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To better identify task-activated brain regions in task-based functional magnetic resonance imaging (tb-fMRI), various space-time models have been used to reconstruct image sequences from k-space data. These models decompose a fMRI timecourse into a static background and a dynamic foreground, aiming to separate task-correlated components from non-task signals. This paper proposes a model based on assumptions of the activation waveform shape and smoothness of the timecourse, and compare it to two contemporary tb-fMRI decomposition models. We experiment in the image domain using a simulated task with known region of interest, and a real visual task. The proposed model yields fewer false activations in task activation maps.
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A Temporal Model for Task-Based Functional MR Images
To better identify task-activated brain regions in task-based functional magnetic resonance imaging (tb-fMRI), various space-time models have been used to reconstruct image sequences from k-space data. These models decompose a fMRI timecourse into a static background and a dynamic foreground, aiming to separate task-correlated components from non-task signals. This paper proposes a model based on assumptions of the activation waveform shape and smoothness of the timecourse, and compare it to two contemporary tb-fMRI decomposition models. We experiment in the image domain using a simulated task with known region of interest, and a real visual task. The proposed model yields fewer false activations in task activation maps.