Local reconstruction from projections using exponential radial sampling

Proposes a new procedure for reconstructing a small region of interest (ROI) of an image from a set of its projections that are densely sampled in the ROI and coarsely sampled outside the ROI. Specifically, the radial sampling density of both the projections and the reconstructed image decreases exponentially with increasing distance from the ROI. The problem and data are reminiscent of the local tomography problem; however, the present algorithm reconstructs the ROI of the image itself, not the filtered version of it obtained using local tomography. The new algorithm has the added advantages of speed and parallelizability. Numerical examples compare the new algorithm to filtered backprojection