SVD for imaging systems with discrete rotational symmetry

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

The singular value decomposition (SVD) of an imaging system is a computationally intensive calculation for tomographic imaging systems due to the large dimensionality of the system matrix. The computation often involves memory and storage requirements beyond those available to most end users. We have developed a method that reduces the dimension of the SVD problem towards the goal of making the calculation tractable for a standard desktop computer. In the presence of discrete rotational symmetry we show that the dimension of the SVD computation can be reduced by a factor equal to the number of collection angles for the tomographic system. In this paper we present the mathematical theory for our method, validate that our method produces the same results as standard SVD analysis, and finally apply our technique to the sensitivity matrix for a clinical CT system. The ability to compute the full singular value spectra and singular vectors could augment future work in system characterization, image-quality assessment and reconstruction techniques for tomographic imaging systems.

Original languageEnglish (US)
Pages (from-to)25306-25320
Number of pages15
JournalOptics Express
Volume18
Issue number24
DOIs
StatePublished - Nov 22 2010

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decomposition
symmetry
matrices
requirements
sensitivity

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics

Cite this

SVD for imaging systems with discrete rotational symmetry. / Clarkson, Eric W; Palit, Robin; Kupinski, Matthew A.

In: Optics Express, Vol. 18, No. 24, 22.11.2010, p. 25306-25320.

Research output: Contribution to journalArticle

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