Detective quantum efficiency of imaging systems with amplifying and scattering mechanisms.

A number of complementary metrics are available to assess the performance of digital X-ray imaging systems. However, the sensitivity of these metrics to changes in the electro-optical imaging chain is poorly understood. Some of the commonly used metrics include Contrast to Noise ratio (CNR), limiting spatial resolution, Modulation Transfer Function (MTF), Noise Power Spectrum (NPS) and the Detective Quantum Efficiency (DQE). We evaluated the utility of these metrics in characterizing the imaging plate, imaging system optics and electronic components of computed radiography (CR) systems. We developed practical and easy to use test objects (phantoms) and implemented software to aid in calculating each metric. The results of this research will facilitate the characterization of differences in CR systems using the appropriate metrics.

Until recently, advanced image quality metrics such as the frequency-dependent detective quantum efficiency, DQE (f), have been employed by a relatively small cadre of expert imaging scientists to compare the performance of digital radiography systems, often using differing methods. To address the potential for differences in measured DQE(f) arising from differences in test methodology, in 2003 the International Electro-technical Commission (IEC) published the first in a series of performance standards to define techniques for measuring the DQE(f) and its constitutent metrics, the modulation transfer function, MTF(f), and noise power spectrum, NPS(f). More recently, the increasing prevalence of digital radiographic systems in the clinical environment and availability of both commercial and public-domain resources have made DQE(f) evaluations both more accessible and increasingly relevant to the clinical medical physicist. This course will review the measurement and analysis methods for DQE evaluations of both general digital radiography and digital mammography systems, using the relevant IEC standard method as a guide. Learning Objectives: 1. Review the definition of the DQE(f) and its constitutent metrics the MTF(f) and NPS(f). 2. Gain an understanding of the IEC standard method for measuring detective quantum efficiency, for both general digital radiography and digital mammography imaging systems. 3. Learn which commercial and public-domain resources are available for measuring the DQE(f) of digital radiographic imaging systems.

Medical images are acquired for the purpose of gathering information regarding the disease state of a patient. The quality of the image should be a measure of how a specified task may be performed given the resulting image. Image quality is task dependent; one imaging system may provide images that allow better task performance than another system's for one task, but worse for another task. Determining the quality of an imaging system involves specifying the task of interest, the range of objects that will be imaged, and the observer (human or machine reader) who will perform the task. In radiography it has become common to use Fourier measures to describe image quality through a combination of Fourier‐based measures of system resolution and noise. The Line Spread Function, when Fourier transformed, yields the Modulation Transfer Function (MTF) as a measure of system resolution. Use of the MTF to describe a system's resolution is appropriate when the imaging system is shift‐invariant, that is, the line spread function is independent of position. Image noise is described in the Fourier domain by the Noise Power Spectrum (NPS). We can use the NPS to describe the noise in an imaging system whenever the noise properties are stationary, meaning that the variance and correlations between fluctuations in different locations are not dependent on their absolute location. These Fourier measures of system resolution and noise are commonly combined to give an overall figure of merit known as the Detective Quantum Efficiency (DQE). The DQE describes the signal‐to‐noise transfer characteristics of an imaging system as long as those assumptions of shift‐invariance and noise stationarity are valid. In digital systems the image of an object is affected by the location of the object with respect to the pixel grid. In other words, the detector pixels cause the system to be shift‐variant. In this case the MTF does not describe the resolution properties completely. This is particularly the case for tomographic systems, where the point‐response function is quite position‐dependent. Moreover, in real imaging systems the noise properties will depend on position, most notably because real patients always have structure. So, the Fourier‐domain noise concept of Noise Power Spectrum does not capture all the properties of noise for real imaging systems and tasks. In this talk we will consider a general treatment of the imaging system as a mapping of information from the patient to the data, without the assumptions inherent in Fourier analysis. We will discuss approaches to the characterization of system resolution and noise in terms of position‐dependent parameters. Once such position‐dependent imaging properties of a system are determined, we can use them to calculate measures of image quality that summarize the usefulness of the images for the performance of various visual tasks.

The detective quantum efficiency (DQE) is a system parameter that can be used to accurately describe image noise transfer characteristics through many imaging systems. A simpler approach used by some investigators, particularly when evaluating new ideas and system designs, is to describe the system as a series of cascaded stages. Each stage may correspond to either an increase in the number of quanta (e.g., conversion from x-ray to optical quanta in a radiographic screen), or a loss (a detection or coupling probability). The number of secondary quanta at each stage per incident primary quantum is given by the product of all preceding gains, and can be displayed graphically for convenient interpretation. The stage with the fewest quanta is called the "quantum sink," limiting the pixel signal-to-noise ratio to less than the square root of the number of quanta per pixel. This conventional zero-spatial-frequency "quantum accounting diagram" (QAD), however, neglects the spatial spreading of secondary quanta and can seriously underestimate image noise. It is shown that this problem is avoided with the introduction of a spatial-frequency dependent QAD, expressed as the product of the gains and squared modulation-transfer functions (MTF) of each stage. A generalized expression is developed for the DQE of a cascaded imaging system that is dependent only on the gain, gain Poisson excess (related to the variance), and MTF, of each stage. A direct relationship is then shown to exist between the DQE and values in the QAD. The QAD of a hypothetical system consisting of a charge-coupled device camera and a scintillating screen is evaluated as an illustrative example. The conventional zero-frequency analysis suggests two quantum sinks occur with approximately equal importance: one in the number of x rays, and one in the number of optical quanta. The spatial-frequency dependent analysis, however, shows the optical quantum sink becomes severe and dominates at nonzero frequencies. The necessary increase in gain or optical numerical aperture required to prevent the optical quantum sink for spatial frequencies of interest is determined from the QAD analysis. The visual impact of this nonzero spatial-frequency quantum sink is shown in images generated using a Monte Carlo simulation of the cascading process.

The ability to improve visualization of structural information in digital radiography without increasing radiation exposures requires improved image quality across all spatial frequencies, especially at high frequencies. The detective quantum efficiency (DQE) as a function of spatial frequency quantifies image quality given by an x-ray detector. We present a method of increasing DQE at high spatial frequencies by improving the modulation transfer function (MTF) and reducing noise aliasing. The Apodized Aperature Pixel (AAP) design uses a detector with micro-elements to synthesize desired pixels and provide higher DQE than conventional detector designs. A cascaded system analysis (CSA) that incorporates x-ray interactions is used for comparison of the theoretical MTF, noise power spectrum (NPS), and DQE. Signal and noise transfer through the converter material is shown to consist of correlated an uncorrelated terms. The AAP design was shown to improve the DQE of both material types that have predominantly correlated transfer (such as CsI) and predominantly uncorrelated transfer (such as Se). Improvement in the MTF by 50% and the DQE by 100% at the sampling cut-off frequency is obtained when uncorrelated transfer is prevalent through the converter material. Optimizing high-frequency DQE results in improved image contrast and visualization of small structures and fine-detail.

There are many ways in which imaging systems can be evaluated. The aim of the present paper is to provide an overview of a number of selected approaches to evaluating imaging systems, often encountered by the medical physicist, and discuss their validity and reliability. Specifically, it will cover (i) characterisation of an imaging system in terms of its detective quantum efficiency using linear-systems analysis; (ii) attempts to calculate relevant measures directly in images using the Rose model and the pixel signal-to-noise ratio; (iii) task-based methods incorporating human observers such as receiver-operating characteristics and (iv) visual grading-based methods using experienced radiologists as observers.

Digital imaging systems require offset and gain calibration to normalize the behavior of individual pixels. This normalization corrects for imperfections in the system and also external variables that have effects on uniformity. Imaging metrics like Detective Quantum Efficiency (DQE) and Modulation Transfer Function (MTF) define how sensitivity and resolution are transferred through the system. Gain calibration can result in a loss of DQE due to the noise associated with its application. The typical technique to minimize this noise is to average several gain calibration procedures so that the introduced noise is minimized. This paper discuses the effects of gain calibration on DQE. It measures DQE as a function of the number of gain calibration procedures averaged and contrasts it with a novel technique that uses a single filtered gain calibration. It demonstrates that noise filter techniques, applied to a single gain calibration, regains the loss in DQE without any degradation in resolution. This paper also compares imaging performance of a system using a filtered gain map against a system that has many gain calibrations averaged. The technique is demonstrated using a Thin-Film-Transistor (TFT)-based large area medical imaging system.

A high resolution x-ray imaging system designated the solid state x-ray image intensifier (SSXII) is being developed as a potential replacement of conventional lower-resolution x-ray detectors for neurovascular interventions. The SSXII consists of a CsI(Tl) phosphor to convert x-rays to light, a fiber optic taper to extend the field-of-view and to couple the phosphor to an Electron-Multiplying CCD (EMCCD) which has a built-in gain to boost the signal above the read-out noise floor. A parallel cascade linear-system model is explored and applied to quantitatively analyze the signal and noise transfer at each stage, and optimize the detector performance. The modulation transfer function (MTF), detective quantum efficiency (DQE) and frequency dependent instrumentation noise equivalent exposure (INEE) can be obtained from the theoretical model and experimental measurement. Agreement of these results validates the effectiveness of the model. Finally, compared with the traditional flat-panel detector (FPD), the SSXII exhibits better performance with higher spatial resolution, higher DQE and lower INEE.

The effect of incident x-ray beam quality on the measured detective quantum efficiency (DQE) of a computed radiography system was investigated. The incident x-ray beams used had peak tube potentials of 70, 95, and 120 kVp, were filtered with various thicknesses of a "patient equivalent phantom" (PEP), aluminum, and copper, and provided a consistent exposure to the storage phosphor. For each peak tube potential and filter combination, the one-dimensional modulation transfer function and noise power spectrum were measured and the square of the incident signal-to-noise ratio was estimated. The spatial frequency dependent DQE was calculated from these data. The DQE was integrated to provide an overall estimate of the efficiency and frequency response of the computed radiography system for the various x-ray beams. There was found to be a wide range of integral DQE (IDQE) values for the peak tube potential and filter combinations used. For example, the IDQE ranged from 3.0 to 0.9 mm(-2) using the peak tube potential and filter combinations 70 kVp with 5.1 cm PEP and 120 kVp with 30.3 cm PEP, respectively. Finally, peak tube potential and filter combinations 70 kVp with 10.2 cm PEP and 120 kVp with 20.2 cm PEP were chosen as standard x-ray beams that will be used at our facility to measure the DQE of digital radiographic imaging systems for evaluation and acceptance testing.

Abstract. As remote sensing and variable-rate technology are becoming more available for aerial applicators, practical methodologies for effective integration of these technologies are needed for site-specific aerial applications of crop production and protection materials. The objectives of this study were to demonstrate how to integrate an airborne multispectral imaging system and an IntelliStar variable-rate aerial application system for site-specific management of the winter weed henbit. The airborne imaging system was used to acquire natural color and near-infrared (NIR) images of a fallow field near College Station, Texas, for mapping the infestation of henbit shortly before herbicide application. The images were then rectified, classified, and converted to a binary prescription map with the full application rate on infested areas and the zero rate on non-infested areas. The variable-rate aerial application system mounted on an agricultural aircraft was used to apply glyphosate over the field based on the prescription map. Airborne imagery was collected 14 days after the herbicide application. The as-applied map along with the prescription map and the post-application imagery were used to assess the performance of the site-specific application. Spatial and statistical analysis results showed that the imaging system was effective for mapping henbit infestations and for assessing the performance of site-specific herbicide application, and that the variable-rate system accurately delivered the product at the desired rate to the prescribed areas for effective control of the weed. The methodology and results from this study will be useful for aerial applicators to incorporate airborne imaging and variable-rate application systems into their aerial application business to increase their capabilities and profits. Keywords: Aerial application, As-applied map, Henbit, Imaging system, Prescription map, Variable-rate technology.

Photon counting detectors (PCDs) are an emerging technology with applications in spectral and low-dose radiographic and tomographic imaging. This paper develops an analytical model of PCD imaging performance, including the system gain, modulation transfer function (MTF), noise-power spectrum (NPS), and detective quantum efficiency (DQE). A cascaded systems analysis model describing the propagation of quanta through the imaging chain was developed. The model was validated in comparison to the physical performance of a silicon-strip PCD implemented on an experimental imaging bench. The signal response, MTF, and NPS were measured and compared to theory as a function of exposure conditions (70 kVp, 1-7 mA), detector threshold, and readout mode (i.e., the option for coincidence detection). The model sheds new light on the dependence of spatial resolution, charge sharing, and additive noise effects on threshold selection and was used to investigate the factors governing PCD performance, including the fundamental advantages and limitations of PCDs in comparison to energy-integrating detectors (EIDs) in the linear regime for which pulse pileup can be ignored. The detector exhibited highly linear mean signal response across the system operating range and agreed well with theoretical prediction, as did the system MTF and NPS. The DQE analyzed as a function of kilovolt (peak), exposure, detector threshold, and readout mode revealed important considerations for system optimization. The model also demonstrated the important implications of false counts from both additive electronic noise and charge sharing and highlighted the system design and operational parameters that most affect detector performance in the presence of such factors: for example, increasing the detector threshold from 0 to 100 (arbitrary units of pulse height threshold roughly equivalent to 0.5 and 6 keV energy threshold, respectively), increased the f50 (spatial-frequency at which the MTF falls to a value of 0.50) by ∼30% with corresponding improvement in DQE. The range in exposure and additive noise for which PCDs yield intrinsically higher DQE was quantified, showing performance advantages under conditions of very low-dose, high additive noise, and high fidelity rejection of coincident photons. The model for PCD signal and noise performance agreed with measurements of detector signal, MTF, and NPS and provided a useful basis for understanding complex dependencies in PCD imaging performance and the potential advantages (and disadvantages) in comparison to EIDs as well as an important guide to task-based optimization in developing new PCD imaging systems.

Cascaded linear systems based modeling techniques have been used in the past to predict important system parameters that have a direct impact on image quality. Such models are also useful in optimizing system parameters to improve image quality. In this work, detailed analysis of a solid-state fluoroscopic imaging system intended for high-resolution angiography is presented with the use of such a model. The imaging system analyzed through this model uses four 8 x 8 cm three-side buttable interlined charge-coupled devices (CCDs) specifically designed for high-resolution angiography and tiled in a seamless fashion to achieve a field of view (FOV) of 16 x 16 cm. Larger FOVs can be achieved by tiling more CCDs in a similar manner. The system employs a CsI:Tl scintillator coupled to the CCDs by straight (nontapering) fiberoptics and can potentially be operated in 78, 156, or 234 microm pixel pitch modes. The system parameters analyzed through this model include presampling modulation transfer function, noise power spectrum, and detective quantum efficiency (DQE). The results of the simulations performed indicate that DQE(0) in excess of 0.6 is achievable, with the imager operating at 156 microm pixel pitch, 30 frames/s, and employing a 450-microm-thick CsI:Tl scintillator, even at a low fluoroscopic exposure rate of 1 microR/frame. Further, at a nominal fluoroscopic exposure rate of 2.5 microR/frame there was no noticeable degradation of the DQE even at the 78 microm pixel pitch mode suggesting that it is feasible to perform high-resolution angiography hitherto unattainable in clinical practice.

This paper describes a performance metric for the evaluation of active coherent imaging systems. This metric can be determined for any system using analytical considerations or measured using standard targets. It has implications for comparison of different imaging systems, optimization of imaging systems, and identification of areas in which significant improvements in particular systems can be realized. The imaging system performance metric described here is suitable for analysis of unconventional imaging systems in which the image is sensed by an array of discrete detectors or in which the image is produced by manipulation of arrays of data. The implications of this formula for determination of photon-efficient and optimized systems are discussed. An important result of this paper will be to show that the efficient use of photons is only part of the story. An efficient system must still be optimized to make best use of the imaging hardware.© (1992) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Effective detective quantum efficiency (eDQE) describes the resolution and noise properties of an imaging system along with scatter and primary transmission, all measured under clinically appropriate conditions. Effective dose efficiency (eDE) is the eDQE normalised to mean glandular dose and has been proposed as a useful metric for the optimisation of clinical imaging systems. The aim of this study was to develop a methodology for measuring eDQE and eDE on a Philips microdose mammography (MDM) L30 photon counting scanning system, and to compare performance with two conventional flat panel systems.A custom made lead-blocker was manufactured to enable the accurate determination of dose measurements, and modulation transfer functions were determined free-in-air at heights of 2, 4 and 6 cm above the breast support platform. eDQE were calculated for a Philips MDM L30, Hologic Dimensions and Siemens Inspiration digital mammography system for 2, 4 and 6 cm thick poly(methyl methacrylate) (PMMA). The beam qualities (target/filter and kilovoltage) assessed were those selected by the automatic exposure control, and anti-scatter grids were used where available.Measurements of eDQE demonstrate significant differences in performance between the slit- and scan-directions for the photon counting imaging system. MTF has been shown to be the limiting factor in the scan-direction, which results in a rapid fall in eDQE at mid-to-high spatial frequencies. A comparison with two flat panel mammography systems demonstrates that this may limit image quality for small details, such as micro-calcifications, which correlates with a more conventional image quality assessment with the CDMAM phantom. eDE has shown the scanning photon counting system offers superior performance for low spatial frequencies, which will be important for the detection of large low contrast masses. Both eDQE and eDE are proposed as useful metrics that should enable optimisation of the Philips MDM L30.

Purpose: The purpose of this study is to introduce a method for quantifying the accuracy of linac and imaging system isocenters and demonstrate its use by measuring the targeting of two kilovoltage (kV) imaging systems, and one gantry mounted megavoltage Electronic Portal Imaging Device (EPID). One is the kV imaging system (OBI) integrated with the linear accelerator which supports radiographic and cone beam CT imaging. The other system is the BrainLab Exac Trac stand alone fixed to the treatment room ceiling and floor. Method and Materials: A cylindrical Lucite phantom was designed and fabricated to perform this study. The cylindrical phantom is 20 cm in diameter and 20 cm long. The phantom contains 13 radio‐opaque fiducial markers. A 5 mm spherical marker is positioned at the center of the cylinder. The phantom was imaged and the CT data sets were imported to two planning systems. Treatment beams were placed and both 3D data sets and isocenter information were transferred to the Linac delivery and imaging workstations. For each imaging system, phantom was initially positioned according to machine cross‐hair. Then images were acquired and measured shifts were applied to precisely position the phantom. Then, Winston‐Lutz test was performed for different gantry and collimator configuration. The radiation beam was collimated by a tray‐mounted 30 mm diameter circular collimator. Results: The radius of spherical volume in which all isocenter intersect or lie was measured. For EPID, OBI, CBCT, and Exac Trac, the radius is: 0.54 mm, 1.4 mm, 1.7 mm and 1.46 mm respectively. From the Winston‐Lutz analysis, the average isocenter deviation from all angles is 1.18 ± 0.28 mm. Conclusion: The error in mechanical, radiation and imaging isocenter is about one millimeter. Regardless what imaging system is used for patient setup, it is important to incorporate this systematic error in any planning margins.