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Proceedings Paper

Flexible algorithm for real-time convolution supporting dynamic event related fMRI
Author(s): Brent L. Eaton; Randall J. Frank; Lizann Bolinger; Thomas J. Grabowski
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Paper Abstract

An efficient algorithm for generation of the task reference function has been developed that allows real-time statistical analysis of fMRI data, within the framework of the general linear model, for experiments with event-related stimulus designs. By leveraging time-stamped data collection in the Input/Output time-aWare Architecture (I/OWA), we detect the onset time of a stimulus as it is delivered to a subject. A dynamically updated list of detected stimulus event times is maintained in shared memory as a data stream and delivered as input to a real-time convolution algorithm. As each image is acquired from the MR scanner, the time-stamp of its acquisition is delivered via a second dynamically updated stream to the convolution algorithm, where a running convolution of the events with an estimated hemodynamic response function is computed at the image acquisition time and written to a third stream in memory. Output is interpreted as the activation reference function and treated as the covariate of interest in the I/OWA implementation of the general linear model. Statistical parametric maps are computed and displayed to the I/OWA user interface in less than the time between successive image acquisitions.

Paper Details

Date Published: 24 April 2002
PDF: 8 pages
Proc. SPIE 4683, Medical Imaging 2002: Physiology and Function from Multidimensional Images, (24 April 2002); doi: 10.1117/12.463614
Show Author Affiliations
Brent L. Eaton, Univ. of Iowa College of Medicine (United States)
Randall J. Frank, Lawrence Livermore National Lab. (United States)
Lizann Bolinger, Univ. of Iowa College of Medicine (United States)
Thomas J. Grabowski, Univ. of Iowa College of Medicine (United States)


Published in SPIE Proceedings Vol. 4683:
Medical Imaging 2002: Physiology and Function from Multidimensional Images
Anne V. Clough; Chin-Tu Chen, Editor(s)

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