Singular Value Decomposition for Magnetic Resonance Fingerprinting in the Time Domain
Debra F. McGivney 1 , Dan Ma 2 , Haris Saybasili 3 , Yun Jiang 2 , and Mark A. Griswold 1,2
Radiology, Case Western Reserve University,
Cleveland, Ohio, United States,
Engineering, Case Western Reserve University, Cleveland,
Ohio, United States,
Healthcare, Chicago, Illinois, United States
Magnetic resonance fingerprinting is a technique that
can provide quantitative maps of tissue parameters (T1,
T2, and off-resonance) through matching observed signals
to a precomputed dictionary of modeled signal
evolutions. To retrieve the parameters, the inner
product between the signal and each dictionary entry is
computed to find the entry corresponding to the maximum.
We propose to compress the size of the dictionary and
observed signals in the time domain by applying the
singular value decomposition (SVD), thereby reducing the
number of computations required while retaining the most
relevant information from the dictionary.
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