Efficient and Accurate Llikelihood for Iterative Image Reconstruction in X-Ray Computed Tomography
dc.contributor.author | Elbakri, Idris A. | en_US |
dc.contributor.author | Fessler, Jeffrey A. | en_US |
dc.date.accessioned | 2011-08-18T18:21:07Z | |
dc.date.available | 2011-08-18T18:21:07Z | |
dc.date.issued | 2003-02-17 | en_US |
dc.identifier.citation | (2003). "Efficient and Accurate Llikelihood for Iterative Image Reconstruction in X-Ray Computed Tomograph."Proc. Of SPIE. Medical Imaging: Image Processing 5032: 1839-1850. <http://hdl.handle.net/2027.42/85924> | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/85924 | |
dc.description.abstract | We report a novel approach for statistical image reconstruction in X-ray CT. Statistical image reconstruction depends on maximizing a likelihood derived from a statistical model for the measurements. Traditionally, the measurements are assumed to be statistically Poisson, but more recent work has argued that CT measurements actually follow a compound Poisson distribution due to the polyenergetic nature of the X-ray source. Unlike the Poisson distribution, compound Poisson statistics have a complicated likelihood that impedes direct use of statistical reconstruction. Using a generalization of the saddle-point integration method, we derive an approximate likelihood for use with iterative algorithms. In its most realistic form, the approximate likelihood we derive accounts for polyenergetic X-rays and Poisson light statistics in the detector scintillator, and can be extended to account for electronic additive noise. The approximate likelihood is closer to the exact likelihood than is the conventional Poisson likelihood, and carries the promise of more accurate reconstruction, especially in low X-ray dose situations. | en_US |
dc.publisher | SPIE | en_US |
dc.title | Efficient and Accurate Llikelihood for Iterative Image Reconstruction in X-Ray Computed Tomography | en_US |
dc.type | Article | en_US |
dc.subject.hlbsecondlevel | Biomedical Engineering | en_US |
dc.subject.hlbtoplevel | Engineering | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Electrical Engineering and Computer Science Department | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/85924/1/Fessler182.pdf | |
dc.identifier.doi | 10.1117/12.480302 | en_US |
dc.identifier.source | Proc. Of SPIE. Medical Imaging: Image Processing | en_US |
dc.owningcollname | Electrical Engineering and Computer Science, Department of (EECS) |
Files in this item
Remediation of Harmful Language
The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.
Accessibility
If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.