Literature DB >> 15609560

Spectrum reconstruction from dose measurements as a linear inverse problem.

Benjamin Armbruster1, Russell J Hamilton, Arthur K Kuehl.   

Abstract

There are three ways to determine the spectrum of a clinical photon beam: direct measurement, modelling the source and reconstruction from ion-chamber measurements. We focus on reconstruction because the necessary equipment is readily available and it provides independent confirmation of source models for a given machine. Reconstruction methods involve measuring the dose in an ion chamber after the beam passes through an attenuator. We gain information about the spectrum from measurements using attenuators of differing compositions and thicknesses since materials have energy dependent attenuation. Unlike the procedures used in other papers, we do not discretize or parametrize the spectrum. With either of these two approximations, reconstruction is a least squares problem. The forward problem of going from a spectrum to a series of dose measurements is a linear operator, with the composition and thickness of the attenuators as parameters. Hence the singular value decomposition (SVD) characterizes this operator. The right singular vectors form a basis for the spectrum, and, at first approximation, only those corresponding to singular values above a threshold are measurable. A more rigorous error analysis shows with what confidence different components of the spectrum can be measured. We illustrate this theory with simulations and an example utilizing six sets of dose measurements with water and lead as attenuators.

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Year:  2004        PMID: 15609560     DOI: 10.1088/0031-9155/49/22/005

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  3 in total

1.  Reconstructing population exposures to environmental chemicals from biomarkers: challenges and opportunities.

Authors:  Panos G Georgopoulos; Alan F Sasso; Sastry S Isukapalli; Paul J Lioy; Daniel A Vallero; Miles Okino; Larry Reiter
Journal:  J Expo Sci Environ Epidemiol       Date:  2008-03-26       Impact factor: 5.563

2.  Estimating the spectrum in computed tomography via Kullback-Leibler divergence constrained optimization.

Authors:  Wooseok Ha; Emil Y Sidky; Rina Foygel Barber; Taly Gilat Schmidt; Xiaochuan Pan
Journal:  Med Phys       Date:  2018-12-13       Impact factor: 4.071

3.  Semiempirical, parameterized spectrum estimation for x-ray computed tomography.

Authors:  Paul FitzGerald; Stephen Araujo; Mingye Wu; Bruno De Man
Journal:  Med Phys       Date:  2021-03-16       Impact factor: 4.071

  3 in total

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