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Abstract #3381

A Bayesian Approach to the Partial Volume Problem in Magnetic Resonance Fingerprinting

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) can produce quantitative maps of tissue parameters such as T1 and T2 relaxation times by matching acquired signals to a predefined dictionary of signal evolutions. One inherent issue is that all voxels are assigned only one dictionary entry, even if they exhibit the partial volume effect. We apply a Bayesian statistical framework to solve the general partial volume problem for MRF without assigning in advance the specific dictionary entries that comprise a signal from one of these mixed voxels, rather, assumptions are made on the probability distributions of the mixed signals and their component signals.

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