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  Prediction and correction of physiological noise in fMRI using machine learning

Ash, T., Suckling, J., Walter, M., Ooi, C., Tempelmann, C., Carpenter, A., et al. (2011). Prediction and correction of physiological noise in fMRI using machine learning. Poster presented at 19th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2011), Montréal, Canada.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0002-4C3C-7 Version Permalink: https://hdl.handle.net/21.11116/0000-0002-4C3D-6
Genre: Poster

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https://www.ismrm.org/11/EPF2.htm (Abstract)
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 Creators:
Ash, T, Author
Suckling, J, Author
Walter, M1, Author           
Ooi, C, Author
Tempelmann, C, Author
Carpenter, A, Author
Williams, G, Author
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1Department of Psychiatry, University of Magdeburg, ou_persistent22              

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 Abstract: We present a support vector machine based technique for recreation of partially or fully absent physiological recording data, to allow detrending of physiological noise to occur even in the absence of complete recordings of the physiological cycles. The technique uses a multi-class SVM to predict phase of each physiological cycle from fMRI image data, after training on prior data. Using these predicted phase values as inputs to physiological detrending tool RETROICOR show similar impact on Fourier transforms of the data as using recorded values, showing that they are accurate enough for use as inputs to detrending tools.

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 Dates: 2011-05
 Publication Status: Issued
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Title: 19th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2011)
Place of Event: Montréal, Canada
Start-/End Date: 2011-05-07 - 2011-05-13

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Title: 19th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2011)
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: 3633 Start / End Page: - Identifier: -