In medical X-ray tomography, three dimensional structure of tissue
is reconstructed from a collection of projection images.
In many practical imaging situations only a small number of truncated
projections is available from a limited range of view.
Traditional reconstruction algorithms, such as filtered backprojection,
do not give satisfactory results when applied to such data.
More suitable reconstruction algorithms based on Bayesian
inversion are studied. In this approach, a priori information is used
to compensate for the incomplete information of the measurement data. Examples with in vitro measurements from dental radiology and surgical imaging are presented.
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