Paper
6 May 2004 Validation of complex cascaded models of medical imaging systems by Monte Carlo
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Abstract
Cascaded models have been used by a number of investigators to derive analytic expressions for the Wiener noise power spectrum (NPS) and detective quantum efficiency (DQE) based on design parameters to evaluate the performance of medical x-ray imaging systems. These analytic models are required to establish operating benchmarks and compare the performance of real detectors. Although application of the cascaded approach has had several successes, its contribution is often limited when applied to complex models. This is due to the fact that while final algebraic expressions can be relatively simple, the cascaded approach involves the manipulation of many hundreds of terms. To overcome this limitation a computational engine has been developed using Matlab's Simulink and symbolic math capabilities. Based on a recursive programming approach, this engine generates analytic expressions of NPS and DQE for cascaded models of arbitrary complexity. In order to validate the resulting expressions, a Monte Carlo (MC) simulation program has been developed that performs an analysis based on C-code generated by the computational engine for each model. The Monte Carlo code generates an incident quantum image as a Poisson distribution of quanta. This distribution is passed through appropriate serial and parallel cascades of modules representing elementary processes and is used to calculate the NPS for comparison with the analytic NPS. Results show excellent agreement between Monte Carlo and theoretical expressions. We are at the stage where complex cascaded modelling is becoming practical tool in the design of new detector systems.
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Mike Sattarivand and Ian A. Cunningham "Validation of complex cascaded models of medical imaging systems by Monte Carlo", Proc. SPIE 5368, Medical Imaging 2004: Physics of Medical Imaging, (6 May 2004); https://doi.org/10.1117/12.535649
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KEYWORDS
Imaging systems

Systems modeling

Modulation transfer functions

Monte Carlo methods

Image processing

Mathematical modeling

Medical imaging

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