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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Click here for more information on becoming a member.
