  The image reconstruction problem consists in finding an approximation of a
function f starting from its Radon transform Rf. This problem arises in the
ambit of medical imaging when one tries to reconstruct the internal structure
of the body, starting from its X-ray tomography. The classical approach to this
problem is based on the Back-Projection Formula. This formula gives an
analytical inversion of the Radon transform, provided that all the values of Rf
are known. In applications only a discrete set of values of Rf is given, thus,
one can only obtain an approximation of f. Another class of methods, called
ART, can be used to solve the reconstruction problem. Following the ideas
contained in ART, we try to apply the Hermite-Birkhoff interpolation to the
reconstruction problem. It turns out that, since the Radon transform of a
kernel basis function can be infinity, a regularization technique is needed.
The method we present here is then based on positive definite kernel functions
and it is very flexible thanks to the possibility to choose different kernels
and parameters. We study the behavior of the methods and compare them with
classical algorithms.
