FAST Adaptive Smoothing and Thresholding for Improved Activation Detection in Low-Signal fMRI

Israel Almodóvar-Rivera; Ranjan Maitra

doi:10.1109/tmi.2019.2915052

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arXiv · 1702.00111

FAST Adaptive Smoothing and Thresholding for Improved Activation Detection in Low-Signal fMRI

Israel Almodóvar-Rivera·Ranjan Maitra

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Abstract

Functional Magnetic Resonance Imaging is a noninvasive tool for studying cerebral function. Many factors challenge activation detection, especially in low-signal scenarios that arise in the performance of high-level cognitive tasks. We provide a fully automated fast adaptive smoothing and thresholding (FAST) algorithm that uses smoothing and extreme value theory on correlated statistical parametric maps for thresholding. Performance on experiments spanning a range of low-signal settings is very encouraging. The methodology also performs well in a study to identify the cerebral regions that perceive only-auditory-reliable or only-visual-reliable speech stimuli.

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Israel Almodóvar-Rivera, Ranjan Maitra. 2017-02-01. FAST Adaptive Smoothing and Thresholding for Improved Activation Detection in Low-Signal fMRI. https://doi.org/10.1109/tmi.2019.2915052

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