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Abstract #4287

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

1 Radiology, Case Western Reserve University, Cleveland, Ohio, United States, 2 Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, United States, 3 Siemens 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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