The scientific community has generally adopted use of the modulation transfer function (MTF) and detective
quantum efficiency (DQE) as primary measures of performance of radiographic detectors. However, measurement
of these parameters is generally restricted to experts in laboratory environments due to the required x-ray
physics knowledge, specialized instrumentation and computational analyses. We have developed a prototype
instrument that automates both the physical measurement and subsequent image analysis to determine the
MTF, noise power spectrum (NPS) and DQE of radiographic and mammographic systems. The instrument
is placed in the x-ray path directly in front of the detector. A series of images are acquired, saved in "raw"
DICOM format and then used to determine the MTF (using the slanted-edge method) and NPS. The number of
incident quanta is calculated from measurements of the incident exposure including corrections for air temperature
and pressure and ionization chamber spectral response. The primary sources of error are backscatter from
the detector and scatter generated within the instrument. These have been minimized to achieve an incident
exposure measurement within 2% of a calibrated electrometer and chamber in free space. The MTF and DQE
of a commercial CsI-based flat-panel detector were measured over a range of incident exposures from 20 uR
to 20 mR per image. Results agreed with both our own laboratory measurements and previously published
measurements performed elsewhere with a similar detector within 2% for the MTF and 5% for the DQE. A
complete DQE analysis of a clinical digital flat-panel detector is completed in 30 minutes and requires no system
modifications.
KEYWORDS: Sensors, Modulation transfer functions, Monte Carlo methods, Fiber optics, Signal detection, Interference (communication), Digital imaging, Fiber optics sensors, Imaging systems, Medical imaging
Cascaded-systems analyses have been used successfully by many investigators to describe signal and noise transfer in quantum-based x-ray detectors in medical imaging. However, the Fourier-based linear-systems approach is only valid when assumptions of linearity and shift invariance are satisfied. Digital detectors, in which a bounded image signal is spatially integrated in discrete detector elements, are not shift invariant in their response. In addition, many detectors make use of fiber optics or structured phosphors such as CsI to pass light to a photodetector-both of which have a shift-variant response. These issues raise serious concerns regarding the validity of Fourier-based approaches for describing the signal and noise performance of these detectors.
We have used a Monte Carlo approach to compare the image Wiener noise power spectrum (NPS) with that predicted using a Fourier-based approach when these assumptions fail. It is shown that excellent agreement is obtained between Monte Carlo results and those obtained using a Fourier-based wide-sense cyclostationary analysis, including the description of noise aliasing. A simple model of a digital detector coupled to a fiber optic bundle is described using a novel cascaded cyclostationary approach in which image quanta are integrated over fiber elements and then randomly re-distributed at the fiber output. While the image signal sometimes contains significant non-stationary beating artifacts, the Monte Carlo results generally show good agreement with Fourier models of the NPS when noise measurements are made over a sufficiently large region of interest.
KEYWORDS: Imaging systems, Systems modeling, Monte Carlo methods, Modulation transfer functions, Image processing, Medical imaging, Mathematical modeling, Performance modeling, Signal processing, Interference (communication)
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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