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  Data-Driven Spectral Feature Extraction in 9.4T CEST MRI data of the human brain

Schuppert, M., Deshmane, A., Herz, K., Scheffler, K., & Zaiss, M. (2019). Data-Driven Spectral Feature Extraction in 9.4T CEST MRI data of the human brain. Poster presented at 27th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2019), Montréal, QC, Canada.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-9703-F Version Permalink: http://hdl.handle.net/21.11116/0000-0003-C73C-A
Genre: Poster

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 Creators:
Schuppert, M1, 2, Author              
Deshmane, A1, 2, Author              
Herz, K1, 2, Author              
Scheffler, K1, 2, Author              
Zaiss, M1, 2, Author              
Affiliations:
1Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497796              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 Abstract: Model-based extraction of features, e.g. Lorentzian fitting of Z-spectra, in CEST MRI can be limited by the underlying model assumptions. Here we analyzed high spectral resolution Z-spectra acquired at 9.4T in five healthy subjects and one tumor patient using principal component analysis, a purely data-driven statistical procedure. Projection of Z-spectra onto principle components from a group of healthy subjects provides several relevant contrasts which reveal anatomical detail and correlate with Gadolinium uptake signatures in a brain tumor patient.

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 Dates: 2019-05
 Publication Status: Published online
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Title: 27th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2019)
Place of Event: Montréal, QC, Canada
Start-/End Date: 2019-05-11 - 2019-05-16

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Title: ISMRM 27th Annual Meeting & Exhibition
Source Genre: Proceedings
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Pages: - Volume / Issue: - Sequence Number: 3994 Start / End Page: - Identifier: -