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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.

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