Novel techniques for image computation and a concomitant reduction of the x-ray dose in transmission tomography

Conventional tomographic imaging techniques are nonlocal: to reconstruct an unknown function f at a point x, one needs to know its Radon transform (RT) {cflx f} ({theta},p). Suppose that one is interested in the recovery of f only for x in some set U. The author calls U the region of interest (ROI). Define the local data as the integrals of f along the lines that intersect the ROI. He proposes algorithms for finding locations and values of jumps (sharp variations) of f from only the local data. In case of transmission tomography, this results in a reduction of the x-ray dose to a patient. The proposed algorithms can also be used in emission tomographies. They allow one: to image jumps of f with better resolution than conventional techniques; to take into account variable attenuation (if it is known); and to obtain meaningful images even if the attenuation is not known. Results of testing the proposed algorithms on the simulated and real data are presented