Abstract
Uses a series-expansion approach and an operator framework to derive a new, fast and accurate, iterative tomographic reconstruction algorithm applicable for parallel-ray projections that have been collected at a finite number of arbitrary view angles and have been radially sampled at a rate high enough so that aliasing errors are small. The authors use the conjugate gradient algorithm to minimize a regularized least squares criterion, and prove that the main step in each iteration is equivalent to a 2-D discrete convolution, which can be cheaply and exactly implemented via the FFT. The proposed algorithm requires O(N/sup 2/ log N) multiplies per iteration to reconstruct an N/spl times/N image from P view angles, and requires the storage of half of a 2N/spl times/2N PSF.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
