Mt. Chan et al., BAYESIAN IMAGE-RECONSTRUCTION USING IMAGE-MODELING GIBBS PRIORS, International journal of imaging systems and technology, 9(2-3), 1998, pp. 85-98
We demonstrate that (a) classical methods of image reconstruction from
projections can be improved upon by considering the output of such a
method as a distorted version of the original image and applying a Bay
esian approach to estimate from it the original image, and (b) by sele
cting an ''image-modeling'' prior distribution (one from which random
samples are likely to share important characteristics of the images of
the application area), one can do better than using some other Gibbs
priors. Our demonstration is from the area of positron emission tomogr
aphy of the brain. We present some encouraging results obtained using
both simulated and real data. (C) 1998 John Wiley & Sons, Inc.