Image quality assessment in digital mammography: part II. NPWE as a validated alternative for contrast detail analysis

Assessment of image quality for digital x-ray mammography systems used in European screening programs relies mainly on contrast-detail CDMAM phantom scoring and requires the acquisition and analysis of many images in order to reduce variability in threshold detectability. Part II of this study proposes an alternative method based on the detectability index (d') calculated for a non-prewhitened model observer with an eye filter (NPWE). The detectability index was calculated from the normalized noise power spectrum and image contrast, both measured from an image of a 5 cm poly(methyl methacrylate) phantom containing a 0.2 mm thick aluminium square, and the pre-sampling modulation transfer function. This was performed as a function of air kerma at the detector for 11 different digital mammography systems. These calculated d' values were compared against threshold gold thickness (T) results measured with the CDMAM test object and against derived theoretical relationships. A simple relationship was found between T and d', as a function of detector air kerma; a linear relationship was found between d' and contrast-to-noise ratio. The values of threshold thickness used to specify acceptable performance in the European Guidelines for 0.10 and 0.25 mm diameter discs were equivalent to threshold calculated detectability indices of 1.05 and 6.30, respectively. The NPWE method is a validated alternative to CDMAM scoring for use in the image quality specification, quality control and optimization of digital x-ray systems for screening mammography.     

Dose efficiency and low-contrast detectability of an amorphous silicon x-ray detector for digital radiography

The effect of dose reduction on low-contrast detectability is investigated theoretically and experimentally for a production grade amorphous silicon (a-Si) x-ray detector and compared with a standard thoracic screen-film combination. A non-prewhitening matched filter observer model modified to include a spatial response function and internal noise for the human visual system (HVS) is used to calculate a signal-to-noise ratio (SNR) related to object detectability. Other inputs to the SNR calculation are the detective quantum efficiency (DQE) and the modulation transfer function (MTF) of the imaging system. Besides threshold detectability, the model predicts the equivalent perception dose ratio (EPDR), which is the fraction of the screen film exposure for which the digital detector provides equal detectability. Images of a contrast-detail phantom are obtained with the digital detector at dose levels corresponding to 27%, 41%, 63% and 100% of the dose used for screen-film. The images are used in a four-alternative forced choice (4-AFC) observer perception study in order to measure threshold detectability. A statistically significant improvement in contrast detectability is measured with the digital detector at 100% and 63% of the screen-film dose. There is no statistical difference between screen-film and digital at 41% of the dose. On average, the experimental EPDR is 44%, which agrees well with the model prediction of 40%.     

Automated assessment of low contrast sensitivity for CT systems using a model observer

Purpose: Low contrast sensitivity of CT scanners is regularly assessed by subjective scoring of low contrast detectability within phantom CT images. Since in these phantoms low contrast objects are arranged in known fixed patterns, subjective rating of low contrast visibility might be biased. The purpose of this study was to develop and validate a software for automated objective low contrast detectability based on a model observer.Methods: Images of the low contrast module of the Catphan 600 phantom were used for the evaluation of the software. This module contains two subregions: the supraslice region with three groups of low contrast objects (each consisting of nine circular objects with diameter 2-15 mm and contrast 0.3, 0.5, and 1.0%, respectively) and the subslice region with three groups of four circular objects each (diameter 3-9 mm; contrast 1.0%). The software method offered automated determination of low contrast detectability using a NPWE (nonprewhitening matched filter with an eye filter) model observer for the supraslice region. The model observer correlated templates of the low contrast objects with the acquired images of the Catphan phantom and a discrimination index d' was calculated. This index was transformed into a proportion correct (PC) value. In the two-alternative forced choice (2-AFC) experiments used in this study, a PC ≥ 75% was proposed as a threshold to decide whether objects were visible. As a proof of concept, influence of kVp (between 80 and 135 kV), mAs (25-200 mAs range) and reconstruction filter (four filters, two soft and two sharp) on low contrast detectability was investigated. To validate the outcome of the software in a qualitative way, a human observer study was performed.Results: The expected influence of kV, mAs and reconstruction filter on image quality are consistent with the results of the proposed automated model. Higher values for d' (or PC) are found with increasing mAs or kV values and for the soft reconstruction filters. For the highest contrast group (1%), PC values were fairly above 75% for all object diameters >2 mm, for all conditions. For the 0.5% contrast group, the same behavior was observed for object diameters >3 mm for all conditions. For the 0.3% contrast group, PC values were higher than 75% for object diameters >6 mm except for the series acquired at the lowest dose (25 mAs), which gave lower PC values. In the human observer study similar trends were found.Conclusions: We have developed an automated method to objectively investigate image quality using the NPWE model in combination with images of the Catphan phantom low contrast module. As a first step, low contrast detectability as a function of both acquisition and reconstruction parameter settings was successfully investigated with the software. In future work, this method could play a role in image reconstruction algorithms evaluation, dose reduction strategies or novel CT technologies, and other model observers may be implemented as well.     

Imaging performance of amorphous selenium based flat-panel detectors for digital mammography: characterization of a small area prototype detector

Our work is to investigate and understand the factors affecting the imaging performance of amorphous selenium (a-Se) flat-panel detectors for digital mammography. Both theoretical and experimental methods were developed to investigate the spatial frequency dependent detective quantum efficiency [DQE(f)] of a-Se flat-panel detectors for digital mammography. Since the K edge of a-Se is 12.66 keV and within the energy range of a mammographic spectrum, a theoretical model was developed based on cascaded linear system analysis with parallel processes to take into account the effect of K fluorescence on the modulation transfer function (MTF), noise power spectrum (NPS), and DQE(f) of the detector. This model was used to understand the performance of a small-area prototype detector with 85 microm pixel size. The presampling MTF, NPS, and DQE(f) of the prototype were measured, and compared to the theoretical calculation of the model. The calculation showed that K fluorescence accounted for a 15% reduction in the MTF at the Nyquist frequency (fNy) of the prototype detector, and the NPS at fNy was reduced to 89% of that at zero spatial frequency. The measurement of presampling MTF of the prototype detector revealed an additional source of blurring, which was attributed to charge trapping in the blocking layer at the interface between a-Se and the active matrix. This introduced a drop in both presampling MTF and NPS at high spatial frequency, and reduced aliasing in the NPS. As a result, the DQE(f) of the prototype detector at fNy approached 40% of that at zero spatial frequency. The measured and calculated DQE(f) using the linear system model have reasonable agreement, indicating that the factors controlling image quality in a-Se based mammographic detectors are fully understood, and the model can be used to further optimize detector imaging performance.     

A comparison between objective and subjective image quality measurements for a full field digital mammography system

This paper presents pre-sampling modulation transfer function (MTF), normalized noise power spectrum (NNPS) and detective quantum efficiency (DQE) results for an amorphous selenium (a-Se) full field digital mammography system. MTF was calculated from the image of an angled 0.5 mm thick Cu edge, acquired without additional beam filtration. NNPS data were acquired at detector air-kerma levels ranging from 9.1 microGy to 331 microGy, using a standard mammography x-ray spectrum of 28 kV, Mo/Mo target/filter combination and 4 cm of PMMA additional filtration. Prior to NNPS estimation, the image statistics were assessed using a variance image. This method was able to easily identify a detector artefact and should prove useful in routine quality assurance (QA) measurements. Detector DQE, calculated from the NNPS and MTF data, dropped to 0.3 for low detector air-kerma settings but reached an approximately constant value of 0.6 above 50 microGy at the detector. Subjective image quality data were also obtained at these detector air-kerma settings using the CDMAM contrast-detail (c-d) test object. The c-d data reflected the trend seen in DQE, with threshold contrast increasing at low detector air-kerma values. The c-d data were then compared against predictions made using two established models, the Rose model and a standard signal detection theory model. Using DQE(0), the Rose model gave results within approximately 15% on average for all the detector air-kerma values studied and for detail diameters down to 0.2 mm. Similar agreement was also found between the measured c-d data and the signal detection theory results, which were calculated using an ideal human visual response function and a system magnification of unity. The use of full spatial frequency DQE improved the agreement between the calculated and observer results for detail sizes below 0.13 mm.     

Validation of a digital mammographic unit model for an objective and highly automated clinical image quality assessment

In mammography, image quality assessment has to be directly related to breast cancer indicator (e.g. microcalcifications) detectability. Recently, we proposed an X-ray source/digital detector (XRS/DD) model leading to such an assessment. This model simulates very realistic contrast-detail phantom (CDMAM) images leading to gold disc (representing microcalcifications) detectability thresholds that are very close to those of real images taken under the simulated acquisition conditions. The detection step was performed with a mathematical observer. The aim of this contribution is to include human observers into the disc detection process in real and virtual images to validate the simulation framework based on the XRS/DD model. Mathematical criteria (contrast-detail curves, image quality factor, etc.) are used to assess and to compare, from the statistical point of view, the cancer indicator detectability in real and virtual images. The quantitative results given in this paper show that the images simulated by the XRS/DD model are useful for image quality assessment in the case of all studied exposure conditions using either human or automated scoring. Also, this paper confirms that with the XRS/DD model the image quality assessment can be automated and the whole time of the procedure can be drastically reduced. Compared to standard quality assessment methods, the number of images to be acquired is divided by a factor of eight.     

Toward objective and quantitative evaluation of imaging systems using images of phantoms

The use of imaging phantoms is a common method of evaluating image quality in the clinical setting. These evaluations rely on a subjective decision by a human observer with respect to the faintest detectable signal(s) in the image. Because of the variable and subjective nature of the human-observer scores, the evaluations manifest a lack of precision and a potential for bias. The advent of digital imaging systems with their inherent digital data provides the opportunity to use techniques that do not rely on human-observer decisions and thresholds. Using the digital data, signal-detection theory (SDT) provides the basis for more objective and quantitative evaluations which are independent of a human-observer decision threshold. In a SDT framework, the evaluation of imaging phantoms represents a "signal-known-exactly/background-known-exactly" ("SKE/ BKE") detection task. In this study, we compute the performance of prewhitening and nonprewhitening model observers in terms of the observer signal-to-noise ratio (SNR) for these "SK E/BKE" tasks. We apply the evaluation methods to a number of imaging systems. For example, we use data from a laboratory implementation of digital radiography and from a full-field digital mammography system in a clinical setting. In addition, we make a comparison of our methods to human-observer scoring of a set of digital images of the CDMAM phantom available from the internet (EUREF-European Reference Organization). In the latter case, we show a significant increase in the precision of the quantitative methods versus the variability in the scores from human observers on the same set of images. As regards bias, the performance of a model observer estimated from a finite data set is known to be biased. In this study, we minimize the bias and estimate the variance of the observer SNR using statistical resampling techniques, namely, "bootstrapping" and "shuffling" of the data sets. Our methods provide objective and quantitative evaluation of imaging systems with increased precision and reduced bias.     

X-ray imaging optimization using virtual phantoms and computerized observer modelling

This study develops and demonstrates a realistic x-ray imaging simulator with computerized observers to maximize lesion detectability and minimize patient exposure. A software package, ViPRIS, incorporating two computational patient phantoms, has been developed for simulating x-ray radiographic images. A tomographic phantom, VIP-Man, constructed from Visible Human anatomical colour images is used to simulate the scattered portion using the ESGnrc Monte Carlo code. The primary portion of an x-ray image is simulated using the projection ray-tracing method through the Visible Human CT data set. To produce a realistic image, the software simulates quantum noise, blurring effects, lesions, detector absorption efficiency and other imaging artefacts. The primary and scattered portions of an x-ray chest image are combined to form a final image for computerized observer studies and image quality analysis. Absorbed doses in organs and tissues of the segmented VIP-Man phantom were also obtained from the Monte Carlo simulations. Approximately 25,000 simulated images and 2,500,000 data files were analysed using computerized observers. Hotelling and Laguerre-Gauss Hotelling observers are used to perform various lesion detection tasks. Several model observer tasks were used including SKE/BKE, MAFC and SKEV. The energy levels and fluence at the minimum dose required to detect a small lesion were determined with respect to lesion size, location and system parameters.     

Comparison of different commercial FFDM units by means of physical characterization and contrast-detail analysis

The purpose of this study was to perform a complete evaluation of three pieces of clinical digital mammography equipment. Image quality was assessed by performing physical characterization and contrast-detail (CD) analysis. We considered three different FFDM systems: a computed radiography unit (Fuji "FCR 5000 MA") and two flat-panel units, the indirect conversion a-Si based GE "Senographe 2000D" and the direct conversion a-Si based IMS "Giotto Image MD." The physical characterization was estimated by measuring the MTF, NNPS, and DQE of the detectors with no antiscatter grid and over the clinical range of exposures. The CD analysis was performed using a CDMAM 3.4 phantom and custom software designed for automatic computation of the contrast-detail curves. The physical characterization of the three digital systems confirms the excellent MTF properties of the direct conversion flat-panel detector (FPD). We performed a relative standard deviation (RSD) analysis, for investigating the different components of the noise presented by the three systems. It turned out that the two FPDs show a significant additive component, whereas for the CR system the statistical noise is dominant. The multiplicative factor is a minor constituent for all the systems. The two FPDs demonstrate better DQE, with respect to the CR system, for exposures higher than 70 microGy. The CD analysis indicated that the three systems are not statistically different for detail objects with a diameter greater than 0.3 mm. However, the IMS system showed a statistically significant different response for details smaller than 0.3 mm. In this case, the poor response of the a-Se detector could be attributed to its high-frequency noise characteristics, since its MTF, NEQ, and DQE are not inferior to those of the other systems. The CD results were independent of exposure level, within the investigated clinical range. We observed slight variations in the CD results, due to the changes in the visualization parameters (window/level and magnification factor). This suggests that radiologists would benefit from viewing images using varied window/level and magnification.     

Computation of realistic virtual phantom images for an objective lesion detectability assessment in digital mammography

Image quality assessment is required for an optimal use of mammographic units. On the one hand, there are objective image quality assessment methods based on the measurement of technical parameters such as modulation transfer function (MTF), noise power spectrum (NPS) or detection quantum efficiency (DQE) describing performances of digital detectors. These parameters are, however, without direct relationship with lesion detectability in clinical practice. On the other hand, there are image quality assessment methods involving time consuming procedures, but presenting a direct relationship with lesion detectability. This contribution describes an X-ray source/digital detector model leading to the simulation of virtual contrast-detail phantom (CDMAM) images. The virtual image computation method requires the acquisition of only few real images and allows for an objective image quality assessment presenting a direct relationship with lesion detectability. The transfer function of the proposed model takes as input physical parameters (MTF* and noise) measured under clinical conditions on mammographic units. As presented in this contribution, MTF* is a modified MTF taking into account the effects due to X-ray scatter in the breast and magnification. Results obtained with the structural similarity index prove that the simulated images are quite realistic in terms of contrast and noise. Tests using contrast detail curves highlight the fact that the simulated and real images lead to very similar data quality in terms of lesion detectability. Finally, various statistical tests show that quality factors computed for both the simulated images and the real images are very close for the two data sets.     

Characterization of the effects of the FineView algorithm for full field digital mammography

The aim of this study was to characterize the effect of an image processing algorithm (FineView) on both quantitative image quality parameters and the threshold contrast detail response of the GE Senographe DS full-field digital mammography system. The system was characterized using signal transfer property, pre-sampling modulation transfer function (MTF), normalized noise power spectrum (NNPS) and detective quantum efficiency (DQE) of the system. An algorithmic modulation transfer function (MTF(a)) was calculated from images acquired at a reduced detector air kerma (DAK) and with the FineView algorithm enabled. Two sets of beam conditions were used: Mo/Mo/28 kV and Rh/Rh/29 kV, both with 2 mm added Al filtration at the x-ray tube. Images were acquired with and without FineView at four DAK levels from 14 to 378 μGy. The threshold contrast detail response was assessed using the CDMAM contrast-detail test object which was imaged under standard clinical conditions with and without FineView at three DAK levels from 24 to 243 μGy. The images were scored by both human observers and by automated scoring software. Results indicated an improvement of up to 125% at 5 mm?1 in MTF(a) when FineView was activated, particularly at high DAK levels. A corresponding increase of up to 425% at 5 mm?1 was also seen in the NNPS, again with the same DAK dependence. FineView did not influence DQE, an indication that the signal to noise ratio transfer of the system remained unchanged. FineView did not affect the threshold contrast detectability of the system, a result that is consistent with the DQE results.     

A clinical evaluation of contrast-detail analysis for ultrasound images

We report on the reproducibility of human observers' vanishing detection thresholds for visual targets in contrast-detail (C/D) analysis of ultrasound B-mode images. The images used in this study contain visual targets which are circular cross sections of constant-contrast conical structures in the C/D phantom. The vanishing threshold diameters for these targets vary as a function of the perceived size of the imaged target, target-to-background contrast, image noise content, and reproducibility of the decision levels of human observers for repeated observations. Our study indicates that the determination of absolute vanishing threshold diameter values for several targets of different contrast by human observers yields a high degree of error that is not predicted by existing theoretical assumptions based on a static threshold detector. We find that systematic error is introduced by the observers during the course of the experiment and that the levels of sensitivity of the observers differ widely at all times, and increase the amount of total observer error. These results suggest that, due to the large total observer error, C/D analysis may be impractical in a clinical environment, unless there is access to a team of observers specifically and extensively trained in this task. We suggest that a computer-based observer may be more reliable for the objective performance of contrast-detail analysis as a method for evaluating ultrasound image quality and comparison of imaging systems.     

Comparison of low contrast detectability between a digital amorphous silicon and a screen-film based imaging system for thoracic radiography.

Low contrast threshold detectability is investigated theoretically and experimentally for an amorphous silicon (a-Si) x-ray detector designed for digital radiography and for a standard thoracic screen-film combination. A theoretical signal-to-noise ratio is described with a human observer signal detection model. It is calculated using the detective quantum efficiency (DQE) and the modulation transfer function of the imaging system, as well as a spatial response function for the human visual system. Using a four-alternative forced choice observer perception study, the threshold contrasts of disk shaped objects ranging in size from 0.3 to 4.0 mm are determined. Significantly better contrast detectability is obtained from the digital detector, which is attributed to its higher DQE. On average, the disk shaped objects can be detected at 45% less contrast than required for screen-film. With no free parameters the experimental data agree well with those predicted by the observer model. Based upon the data, the model predicts that x-ray exposure for the a-Si detector only needs to be 30% of the exposure required to perform equally well on the contrast-detail detectability task using screen-film.     

Image quality assessment in digital mammography: part I. Technical characterization of the systems

In many European countries, image quality for digital x-ray systems used in screening mammography is currently specified using a threshold-detail detectability method. This is a two-part study that proposes an alternative method based on calculated detectability for a model observer: the first part of the work presents a characterization of the systems. Eleven digital mammography systems were included in the study; four computed radiography (CR) systems, and a group of seven digital radiography (DR) detectors, composed of three amorphous selenium-based detectors, three caesium iodide scintillator systems and a silicon wafer-based photon counting system. The technical parameters assessed included the system response curve, detector uniformity error, pre-sampling modulation transfer function (MTF), normalized noise power spectrum (NNPS) and detective quantum efficiency (DQE). Approximate quantum noise limited exposure range was examined using a separation of noise sources based upon standard deviation. Noise separation showed that electronic noise was the dominant noise at low detector air kerma for three systems; the remaining systems showed quantum noise limited behaviour between 12.5 and 380 μGy. Greater variation in detector MTF was found for the DR group compared to the CR systems; MTF at 5 mm(-1) varied from 0.08 to 0.23 for the CR detectors against a range of 0.16-0.64 for the DR units. The needle CR detector had a higher MTF, lower NNPS and higher DQE at 5 mm(-1) than the powder CR phosphors. DQE at 5 mm(-1) ranged from 0.02 to 0.20 for the CR systems, while DQE at 5 mm(-1) for the DR group ranged from 0.04 to 0.41, indicating higher DQE for the DR detectors and needle CR system than for the powder CR phosphor systems. The technical evaluation section of the study showed that the digital mammography systems were well set up and exhibiting typical performance for the detector technology employed in the respective systems.     

Identification of simulated microcalcifications in white noise and mammographic backgrounds

This work investigates human performance in discriminating between differently shaped simulated microcalcifications embedded in white noise or mammographic backgrounds. Human performance was determined through two alternative forced-choice (2-AFC) experiments. The signals used were computer-generated simple shapes that were designed such that they had equal signal energy. This assured equal detectability. For experiments involving mammographic backgrounds, signals were blurred to account for the imaging system modulation transfer function (MTF). White noise backgrounds were computer generated; anatomic background patches were extracted from normal mammograms. We compared human performance levels as a function of signal energy in the expected difference template. In the discrimination task, the expected difference template is the difference between the two signals shown. In white noise backgrounds, human performance in the discrimination task was degraded compared to the detection task. In mammographic backgrounds, human performance in the discrimination task exceeded that of the detection task. This indicates that human observers do not follow the optimum decision strategy of correlating the expected signal template with the image. Human observer performance was qualitatively reproduced by non-prewhitening with eye filter (NPWE) model observer calculations, in which spatial uncertainty was explicitly included by shifting the locations of the expected difference templates. The results indicate that human strategy in the discrimination task may be to match individual signal templates with the image individually, rather than to perform template matching between the expected difference template and the image.     

Quantification of Al-equivalent thickness of just visible microcalcifications in full field digital mammograms

Characterization of digital mammography systems is often performed by means of contrast-detail curves using a homogeneous phantom with inserts of different sizes and thicknesses. In this article, a more direct measure of the threshold contrast-detail characteristics of microcalcifications in clinical mammograms is proposed, which also takes into account routine processing and display. The proposed method scores the detectability of simulated microcalcifications with known size and aluminum-equivalent thickness. Thickness estimates, based on x-ray transmission coefficients, were first validated for Al particles. The same approach was then applied to associate Al-equivalent thickness with simulated microcalcifications. Thirty-five mammograms of patients were acquired using a full field digital mammography (FFDM) system operating under standard exposure conditions. Different microcalcifications were simulated using templates of real microcalcifications as described in Med. Phys. 30, 2234-2240 (2003). These templates were first modified such that they simulated a template of the same microcalcification for an ideally sharp detector. They were then adjusted for the imaging characteristics of the FFDM, beam quality, and breast thickness. Microcalcification sizes in the image plane ranged from 200 to 800 microm. Their peak Al-equivalent thickness varied between 70 and 1000 microm. Software phantoms were created. They consisted of 0-10 simulated microcalcifications randomly distributed in 2 cm by 2 cm frames embedded within digital mammograms. Routine processing and printing followed. Three experienced radiologists recorded the locations of the microcalcifications, and confidence ratings were given. Free response receiver operating characteristics (FROC) analysis was performed. Using a binary score, the fractions of detected microcalcifications were plotted as a function of equivalent diameter for the different Al-equivalent thicknesses. Pair-wise agreement of the detected microcalcifications was calculated for the different Al-equivalent thickness groups. The FROC curves of each radiologist indicated similar true positive fractions for a given number of false positives per image. One radiologist applied a more conservative scoring. Detected fractions for the different sizes of the microcalcifications showed the same trend for all observers. In addition, the observer with the least FP also detected less microcalcifications. The pair-wise agreement of the detected microcalcifications was good. The average detected fractions were >0.5 for microcalcifications with equivalent diameter >400 microm and Al-equivalent thickness >400 microm. An average detected fraction >0.5 was also seen for microcalcifications with equivalent diameter <400 microm and equivalent thickness >800 microm. The detected fractions of smaller microcalcifications were <0.5. The results obtained with this method indicate that it may be possible to quantify the performance of a digital mammography detector including processing and viewing for the detection of microcalcifications. We hypothesize that the FROC curves and detected fractions of simulated microcalcifications of different sizes reflect the clinical reality.     

Human observer detection experiments with mammograms and power-law noise

We determined contrast thresholds for lesion detection as a function of lesion size in both mammograms and filtered noise backgrounds with the same average power spectrum, P(f)=B/f3. Experiments were done using hybrid images with digital images of tumors added to digitized normal backgrounds, displayed on a monochrome monitor. Four tumors were extracted from digitized specimen radiographs. The lesion sizes were varied by digital rescaling to cover the range from 0.5 to 16 mm. Amplitudes were varied to determine the value required for 92% correct detection in two-alternative forced-choice (2AFC) and 90% for search experiments. Three observers participated, two physicists and a radiologist. The 2AFC mammographic results demonstrated a novel contrast-detail (CD) diagram with threshold amplitudes that increased steadily (with slope of 0.3) with increasing size for lesions larger than 1 mm. The slopes for prewhitening model observers were about 0.4. Human efficiency relative to these models was as high as 90%. The CD diagram slopes for the 2AFC experiments with filtered noise were 0.44 for humans and 0.5 for models. Human efficiency relative to the ideal observer was about 40%. The difference in efficiencies for the two types of backgrounds indicates that breast structure cannot be considered to be pure random noise for 2AFC experiments. Instead, 2AFC human detection with mammographic backgrounds is limited by a combination of noise and deterministic masking effects. The search experiments also gave thresholds that increased with lesion size. However, there was no difference in human results for mammographic and filtered noise backgrounds, suggesting that breast structure can be considered to be pure random noise for this task. Our conclusion is that, in spite of the fact that mammographic backgrounds have nonstationary statistics, models based on statistical decision theory can still be applied successfully to estimate human performance.     

A comparative contrast-detail study of five medical displays

The objective of this study was to compare the contrast-detail performance of five different commercial liquid crystal displays (LCDs) to other LCD and cathode-ray tube (CRT) displays for medical applications. A contrast-detail phantom, supplemented with 5 in. of acrylic, was imaged on a commercial digital radiographic system using techniques comparable to chest radiography. The phantom design enabled observer evaluation by a four-alternative forced choice paradigm. The acquired images were independently scored by five observers on five medical display devices: a 5 megapixel monochrome LCD, a 3 megapixel monochrome LCD, a 9 megapixel color LCD, a 5 megapixel monochrome CRT, and a mammographic-grade monochrome CRT. The data were analyzed using the method suggested by the manufacturer based on a nearest neighbor correction technique. They were further analyzed using a logistic regression response model with a natural threshold using an overall chi-square test for display type followed by pairwise comparisons for individual display performance. The differences between the display devices were small. The standard analysis of the results based on the manufacturer-recommended method did not yield any statistically discernible trend among displays. The logistic regression analysis, however, indicated that the 5 megapixel monochrome LCD was statistically significantly (p <0.0001) superior to the others, followed by the 3 megapixel monochrome LCD (p<0.0001). The three other displays exhibited lower but generally similar performance characteristics. The findings suggest that 5 and 3 megapixel monochrome LCDs provide comparable but subtly superior contrast detectability than other tested displays, with the former performing slightly better in the detection of subtle and fine details.     

Early experience in the use of quantitative image quality measurements for the quality assurance of full field digital mammography x-ray systems

Quantitative image quality results in the form of the modulation transfer function (MTF), normalized noise power spectrum (NNPS) and detective quantum efficiency (DQE) are presented for nine full field digital mammography (FFDM) systems. These parameters are routinely measured as part of the quality assurance (QA) programme for the seven FFDM units covered by our centre. Just one additional image is required compared to the standard FFDM protocol; this is the image of an edge, from which the MTF is calculated. A variance image is formed from one of the flood images used to measure the detector response and this provides useful information on the condition of the detector with respect to artefacts. Finally, the NNPS is calculated from the flood image acquired at a target detector air kerma (DAK) of 100 microGy. DQE is then estimated from these data; however, no correction is currently made for effects of detector cover transmission on DQE. The coefficient of variation (cov) of the 50% point of the MTF for five successive MTF results was 1%, while the cov for the 50% MTF point for an a-Se system over a period of 17 months was approximately 3%. For four a-Se based systems, the cov for the NNPS at 1 mm(-1) for a target DAK of 100 microGy was approximately 4%; the same result was found for four CsI based FFDM units. With regard to the stability of NNPS over time, the cov for four NNPS results acquired over a period of 12 months was also approximately 4%. The effect of acquisition geometry on NNPS was also assessed for a CsI based system. NNPS data acquired with the antiscatter grid in place showed increased noise at low spatial frequency; this effect was more severe as DAK increased. DQE results for the three detector types (a-Se, CsI and CR) are presented as a function of DAK. Some reduction in DQE was found for both the a-Se and CsI based systems at a target DAK of 12.5 microGy when compared to DQE data acquired at 100 microGy. For the CsI based systems, DQE at 1 mm(-1) fell from 0.49 at 100 microGy to 0.38 at 12.5 microGy. For the a-Se units, there was a slightly greater reduction in average DQE at 1 mm(-1), from 0.53 at 100 microGy to 0.31 at 12.5 microGy. Somewhat different behaviour was seen for the CR unit; DQE (at 1 mm(-1)) increased from 0.40 at 100 microGy to 0.49 at 12.5 microGy; however, DQE fell to 0.30 at 420 microGy. DQE stability over time was assessed using the cov of DQE at 1 mm(-1) and a target DAK of 100 microGy; the cov for data acquired over a period of 17 months for an a-Se system was approximately 7%. For comparison with conventional testing methods, the cov was calculated for contrast-detail (cd) data acquired over the same period of time for this unit. The cov for the threshold contrast results (averaged for disc diameters between 0.1 mm and 2 mm) was 6%, indicating similar stability.     

Digital mammography image simulation using Monte Carlo

Monte Carlo simulations of digital images of the contrast detail phantom and the ACR phantom are presented for two different x-ray digital mammography modalities: a synchrotron mammography system and a next-generation scanning slot clinical system. A combination of variance reduction methods made it possible to simulate accurate images using real pixel dimensions within reasonable computation times. The complete method of image simulation, including a simple detector response model, a simple noise model, and the incorporation of system effects (MTF), is presented. The simulated images of the phantoms show good agreement with images measured on the two systems.