Abstract
Iterative convolution, a technique for limited-data tomographic reconstruction, is based on constrained iteration between a source function’s estimated image and its Radon transform. For certain source functions, the estimated image diverges from the source function. This divergence is shown to result from the algorithm’s failure to enforce consistency between the estimated image and its measured Radon transform. To correct for this, consistency can be enforced by using a routine based on the direct-inversion formula and on decomposing the image into components generated from its measured and missing Radon-transform integrals. This routine was used to develop a new iterative scheme that converges absolutely for three test objects for which simple iterative convolution diverges.
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.
