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

Date
2019-05-07
Authors
Almodovar-Rivera, Israel
Maitra, Ranjan
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Statistics
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Statistics
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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This is a manuscript of an article published as Almodóvar-Rivera, Israel, and Ranjan Maitra. "FAST adaptive smoothing and thresholding for improved activation detection in low-signal fMRI." IEEE Transactions on Medical Imaging 38, no. 12 (2019): 2821-2828. DOI: 10.1109/TMI.2019.2915052. Posted with permission.

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