Abstract #2579
BRANDI: Bayesian Regularisation of Advanced Neurological Diffusion Imaging
Susan Doshi 1 , Derek Jones 2 , and Daniel Barazany 2,3
1
Computer Science and Informatics, Cardiff
University, Cardiff, Glamorgan, United Kingdom,
2
CUBRIC,
Cardiff University, Cardiff, United Kingdom,
3
Department
of Neurobiology, Tel Aviv University, Tel Aviv, Israel
We use Bayesian statistical modelling to regularise
parameter estimates in advanced diffusion imaging. By
incorporating prior knowledge (such as spatial
smoothness) during estimation, we exploit the
information more fully than applying smoothing as
post-processing. We use a Markov random field for the
prior probability. This approach allows the possibility
of non-isotropic smoothing, and for edges in one part of
the data to guide the fitting of other parts. We
demonstrate the approach with CHARMED data, using
ex-vivo porcine spinal cord as a biological phantom. The
parameter estimates in homogeneous areas are smooth
(agreeing with our prior belief), with edges preserved.
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.
