A constrained conjugate gradient algorithm for computed tomography
- Lawrence Livermore National Lab., CA (United States)
Image reconstruction from projections of x-ray, gamma-ray, protons and other penetrating radiation is a well-known problem in a variety of fields, and is commonly referred to as computed tomography (CT). Various analytical and series expansion methods of reconstruction and been used in the past to provide three-dimensional (3D) views of some interior quantity. The difficulties of these approaches lie in the cases where (a) the number of views attainable is limited, (b) the Poisson (or other) uncertainties are significant, (c) quantifiable knowledge of the object is available, but not implementable, or (d) other limitations of the data exist. We have adapted a novel nonlinear optimization procedure developed at LLNL to address limited-data image reconstruction problems. The technique, known as nonlinear least squares with general constraints or constrained conjugate gradients (CCG), has been successfully applied to a number of signal and image processing problems, and is now of great interest to the image reconstruction community. Previous applications of this algorithm to deconvolution problems and x-ray diffraction images for crystallography have shown the great promise.
- Research Organization:
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- OSTI ID:
- 145986
- Report Number(s):
- CONF-9411140-Absts.; ON: DE95017252; TRN: 95:007225-0036
- Resource Relation:
- Conference: Imaging sciences workshop, Livermore, CA (United States), 15-16 Nov 1994; Other Information: PBD: 15 Nov 1994; Related Information: Is Part Of Imaging sciences workshop; Candy, J.V.; PB: 101 p.
- Country of Publication:
- United States
- Language:
- English
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