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Abstract #3380

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

Debra McGivney 1 , Anagha Deshmane 2 , Yun Jiang 2 , Dan Ma 2 , and Mark Griswold 1,2

1 Radiology, Case Western Reserve University, Cleveland, Ohio, United States, 2 Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States

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.

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