Nonlinear Dimensionality Reduction for Magnetic Resonance Fingerprinting with 
Applications to Partial Volume

McGivney, D; Deshmane, A; Jiang, Y; Ma, D; Griswold, M

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Nonlinear Dimensionality Reduction for Magnetic Resonance Fingerprinting with Applications to Partial Volume

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http://archive.ismrm.org/2015/3380.html
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McGivney, D., Deshmane, A., Jiang, Y., Ma, D., & Griswold, M. (2015). Nonlinear Dimensionality Reduction for Magnetic Resonance Fingerprinting with Applications to Partial Volume. Poster presented at 23rd Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2015), Toronto, ON, Canada.

Cite as: https://hdl.handle.net/21.11116/0000-0006-9ACC-7

Abstract

Magnetic resonance fingerprinting (MRF) is a technique that can provide quantitative maps of tissue parameters such as T1 and T2 relaxation times through matching observed signals to a precomputed complex-valued dictionary of modeled signal evolutions. Since each dictionary entry is uniquely defined by two real parameters, specifically T1 and T2, we propose to compress the dictionary onto a real-valued manifold of three dimensions using the nonlinear dimensionality reduction technique of kernel principal component analysis. Once the compression is achieved, we explore new computational applications for MRF, namely solving the partial volume problem.