Methodology to measure fundamental performance parameters of x-ray detectors

Busse, Falko; Ruetten, Walter; Wischmann, H.‐A.; Geiger, Bernhard; Spahn, M.; Bastiaens, Raoul J. M.; Ducourant, Thierry

doi:10.1117/12.430859

Cited by 13 publications

(17 citation statements)

References 9 publications

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“…Due to the high resistivity of PbO, the dark current shot noise contributes only 300 electrons/pixel to the pixel noise. All noise power spectra were corrected for lag effects [17].…”

Section: Large Plates: Imaging Performancementioning

confidence: 99%

Analysis of lead oxide (PbO) layers for direct conversion X-ray detection

Simon

Ford

Franklin

et al. 2005

IEEE Trans. Nucl. Sci.

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Lead oxide (PbO) is a candidate direct conversion material for medical X-ray applications. We produced various samples and detectors with thick PbO layers. X-ray performance data such as dark current, charge generation yield and temporal behavior were evaluated on small samples. The influence of the metal contacts was studied in detail. We also covered large a-Si thin-film transistor (TFT)-plates with PbO. Imaging results from a large detector with an active area of 18 cm × 20 cm are presented. The detector has 960 × 1080 pixels with a pixel pitch of 184 ?m. The modulation transfer function at the Nyquist frequency of 2.72 linepairs/mm is 50%. Finally, a full size X-ray image is presented

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“…Due to the high resistivity of PbO, the dark current shot noise contributes only 300 electrons/pixel to the pixel noise. All noise power spectra were corrected for lag effects [17].…”

Section: Large Plates: Imaging Performancementioning

confidence: 99%

Analysis of lead oxide (PbO) layers for direct conversion X-ray detection

Simon

Ford

Franklin

et al. 2005

IEEE Trans. Nucl. Sci.

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“…The results of their studies were dependent on the subjective performance of observers using a contrast-detail method to evaluate the relationship between contrast resolution and dose. Busse et al (2001) provided a quantitative discussion of various system noise sources in dynamic imaging detectors and developed metrics to compare detector technologies based on signal and electronic noise levels and their impact on DQE. In later years, studies on indirect flat-panel detectors (Roos et al 2004) have used the fact that the detector x-ray entrance exposure at which the instrumentation noise and the quantum noise are equal has an SNR that was reduced by a factor of

\sqrt{2}

due to the instrumentation noise.…”

Section: Introductionmentioning

confidence: 99%

A practical exposure-equivalent metric for instrumentation noise in x-ray imaging systems

et al. 2008

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The performance of high-sensitivity x-ray imagers may be limited by additive instrumentation noise rather than by quantum noise when operated at the low exposure rates used in fluoroscopic procedures. The equipment-invasive instrumentation noise measures (in terms of electrons) are generally difficult to make and are potentially not as helpful in clinical practice as would be a direct radiological representation of such noise that may be determined in the field. In this work, we define a clinically relevant representation for instrumentation noise in terms of noise-equivalent detector entrance exposure, termed the instrumentation noise-equivalent exposure (INEE), which can be determined through experimental measurements of noise-variance or signal-to-noise ratio (SNR). The INEE was measured for various detectors, thus demonstrating its usefulness in terms of providing information about the effective operating range of the various detectors. A simulation study is presented to demonstrate the robustness of this metric against post-processing, and its dependence on inherent detector blur. These studies suggest that the INEE may be a practical gauge to determine and compare the range of quantum-limited performance for clinical x-ray detectors of different design, with the implication that detector performance at exposures below the INEE will be instrumentation-noise limited rather than quantum-noise limited.

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“…For both measurements, proper gain calibration and compensation of low-frequency drop effects by means of deconvolution was required in order to produce reasonably accurate results using this procedure. Determination of the low-frequency drop characteristics was carried out using a technique based on lead disc measurements 21 .…”

Section: Test Bench Experimentsmentioning

confidence: 99%

Scatter correction for cone-beam computed tomography using simulated object models

2006

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Scattered radiation is a major source of artifacts in flat detector based cone-beam computed tomography. In this paper, a novel software-based method for retrospective scatter correction is described and evaluated. The method is based on approximation of the imaged object by a simple geometric model (e.g., a homogeneous water-like ellipsoid) that is estimated from the set of acquired projections. This is achieved by utilizing a numerical optimization procedure to determine the model parameters for which there is maximum correspondence between the measured projections and the projections of the model. Monte-Carlo simulations of this model are used for calculation of scatter estimates for the acquired projections. Finally, using the scatter-corrected projections, tomographic reconstruction is conducted by means of cone-beam filtered back-projection. The correction method is evaluated using simulated and experimentally acquired projection data sets of geometric and physical head phantoms. It is found that the method is able to accurately estimate mean scatter levels in X-ray projections, allowing to significantly reduce scatter-caused artifacts in 3D reconstructed images.

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Methodology to measure fundamental performance parameters of x-ray detectors

Cited by 13 publications

References 9 publications

Analysis of lead oxide (PbO) layers for direct conversion X-ray detection

Analysis of lead oxide (PbO) layers for direct conversion X-ray detection

A practical exposure-equivalent metric for instrumentation noise in x-ray imaging systems

Scatter correction for cone-beam computed tomography using simulated object models

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