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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