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Experimental correlation-based identification of X-ray CT point spread function. Part 2: simulation and design of input signal

  • S. Doré
  • R. E. Kearney
  • J. A. De Guise
Imaging

Abstract

The preferred signals for non-parametric correlation-based point spread function identification are white noise or pseudo-random binary sequences (PRBSs). Given the difficulty of building a phantom based on either of these signals, a new input is devised that corresponds to pseudo-randomly located holes. The positions of the holes correspond to zeros in a 2-D PRBS. To optimise the design of the phantom and to ensure proper imaging procedure, a number of simulations are conducted. The effects of the following parameters on identification quality are investigated: the size of the holes and their minimum separation, the period of the PRBS, input-output translational and rotational mis-registration, pixel size and the presence of cupping. The factors affecting identification quality the most are rotational alignment, hole size and separation, as well as sequence length. During simulations, a point spread function offering characteristics similar to the Philips Tomoscan CX is identified. Optimal results are obtained when the signal consists of 0·6 mm holes, separated by 0·9 mm, whose position is based on a 32×32 PRBS generated with a ten-stage shift-register. When adequate rotational alignment is provided, it is shown that the pseudo-randomly located holes signal is a good substitute for a purely white signal when identifying the PSF of a CT scanner.

Keywords

CT scanner Identification Input signal Non-parametric Pixel size Point spread function Registration 

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References

  1. Bracewell, R. N. (1977): ‘Correction for collimator width (restoration) in reconstructive x-ray tomography,’J. Comput. Assist. Tomogr.,1, pp. 6–15CrossRefGoogle Scholar
  2. Doré, S. (1992): ‘Experimental identification of X-ray CT system characteristics for an improved understanding of image processing.’ PhD Thesis, McGill University, Montréal.Google Scholar
  3. Doré, S., Kearney, R. E. andDe Guise, J. A. (1997): ‘Experimental correlation-based identification of X-ray CT point spread function. Part 1: method and experimental results,’Med. Biol. Eng. Comput., 1997,35, pp. 2–8Google Scholar
  4. Godfrey, K. R. (1969): ‘The theory of the correlation method of dynamic analysis and its application to industrial processes and nuclear power plant,’Meas. Control,2, pp. T65-T72Google Scholar
  5. Joseph, P. M., Spital, R. D. andStockman, C. D. (1980): ‘The effects of sampling on CT images,”Comput. Tomogr.,4, pp. 189–206CrossRefGoogle Scholar
  6. Kijewski, M. F. andJudy, P. F. (1983): ‘The effects of misregistration of the projections on the spatial resolution of CT scanners,’Med. Phys.,10, pp. 1153–1160CrossRefGoogle Scholar
  7. Norton, J. P. (1986): ‘An Introduction to identification.’ (London: Academic Press)MATHGoogle Scholar
  8. Verly, J. G. andBracewell, R. N. (1979): ‘Blurring in tomograms made with X-ray beams of finite width,’J. Comput. Assist. Tomogr.,3, pp. 662–678CrossRefGoogle Scholar

Copyright information

© IFMBE 1997

Authors and Affiliations

  1. 1.Génie mécaniqueÉcole de technologie supérieureMontréalCanada
  2. 2.Department of Biomedical EngineeringMcGill UniversityMontéalCanada
  3. 3.Génie de la production automatiséeÉcole de technologie supérieureMontréalCanada

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