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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