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.     

A Technique for Simulating the Effect of Dose Reduction on Image Quality in Digital Chest Radiography

Purpose: The purpose of this study is to provide a pragmatic tool for studying the relationship between dose and image quality in clinical chest images. To achieve this, we developed a technique for simulating the effect of dose reduction on image quality of digital chest images. Materials and Methods: The technique was developed for a digital charge-coupled-device (CCD) chest unit with slot-scan acquisition. Raw pixel values were scaled to a lower dose level, and a random number representing noise to each specific pixel value was added. After adding noise, raw images were post processed in the standard way. Validation was performed by comparing pixel standard deviation, as a measure of noise, in simulated images with images acquired at actual lower doses. To achieve this, a uniform test object and an anthropomorphic phantom were used. Additionally, noise power spectra of simulated and actual images were compared. Also, detectability of simulated lesions was investigated using a model observer. Results: The mean difference in noise values between simulated and real lower-dose phantom images was smaller than 5% for relevant clinical settings. Noise power spectra appeared to be comparable on average but simulated images showed slightly higher noise levels for higher spatial frequencies and slightly lower noise levels for lower spatial frequencies. Comparable detection performance was shown in simulated and actual images with slightly worse detectability for simulated lower dose images. Conclusion: We have developed and validated a method for simulating dose reduction. Our method seems an acceptable pragmatic tool for studying the relationship between dose and image quality.     

Amorphous selenium flat panel detectors for digital mammography: validation of a NPWE model observer with CDMAM observer performance experiments

Model observers have been developed which incorporate a specific imaging task, system performance, and human observer characteristics and can potentially overcome some of the limitations in using detective quantum efficiency for optimization and comparison of detectors. In this paper, a modified nonprewhitening matched filter (NPWE) model observer was developed and validated to predict object detectability for an amorphous selenium (a-Se) direct flat-panel imager (FPI) where aliasing is severe. A preclinical a-Se digital mammography FPI with 85 microm pixel size was used in this investigation. Its physical imaging properties including modulation transfer function (MTF), noise power spectrum, and DQE were fully characterized. An observer performance study was conducted by imaging the CDMAM 3.4 contrast-detail phantom designed specifically for digital mammography and presenting these images to a panel of seven observers. X-ray attenuation and scatter due to the phantom were determined experimentally for use in development of the model observer. The observer study results were analyzed via threshold averaging and signal detection theory (SDT) based techniques to produce contrast-detail curves where threshold contrast is plotted as a function of disk diameter. Validity of the model was established using SDT analysis of the experimental data. The effect of aliasing on the detectability of small diameter disks was determined using the NPWE model observer. The signal spectrum was calculated using the presampling MTF of the detector with and without including the aliased terms. Our results indicate that the NPWE model based on Fourier domain parameters provides reasonable prediction of object detectability for the signal-known-exactly task in uniform image noise for a-Se direct FPI.     

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.     

JPEG2000有损压缩对核医学图像的压缩效果的客观定量研究

从线源图像获得线源扩展函数、调制转移函数和峰-总计数比等参数,定量研究JPEG 2000有损压缩对核医学图像的压缩效果,并与实际分辨率图像和模拟分辨率图像的压缩效果进行对比。实验发现,随压缩比的增加,峰-总计数比曲线逐步下降,且噪声水平越高,曲线下降越快;但调制转移函数曲线和线源扩展函数曲线的半高宽无明显变化;随压缩比的增加,分辨率图像上可辨认出的条纹逐渐减少。实验结果表明,在压缩比和噪声水平变化时,峰-总计数比的变化与图像质量的变化规律一致,是用于图像压缩效果评价的较好的客观定量参数。     

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.     

Dual-energy mammography: initial experimental results

Dual-energy x-ray techniques may be able to enhance the detectability of calcifications in mammographic examinations by removing the background "clutter" caused by contrast between adipose and glandular tissue. This hypothesis is examined experimentally by implementation of dual-energy imaging on a prototype digital scanned projection radiography system developed in our laboratory. A model of the propagation of signal and noise in dual-energy processing for a given radiation dose is validated by measurements from phantom images. The experimental imaging system has low spatial resolution and cannot be operated at dose-optimum energies; however, since both the single- and dual-energy images are subject to the same technical limitations, a comparison of such images allows an assessment of the benefits of dual energy. Experimental images of breast tissue specimens, showing improved detectability of calcifications when obscuring background clutter is removed, are presented. The dose required for a given signal-to-noise ratio can be reduced by smoothing the higher energy image prior to dual-energy processing. For practical implementation, it is reasonable to smooth the higher energy image such that its variance is reduced fourfold.     

Investigation of basic imaging properties in digital radiography. 13. Effect of simple structured noise on the detectability of simulated stenotic lesions

We investigated the effects of structured background noise on the detectability of stenotic lesions. Digital subtraction angiographic (DSA) images of stenotic blood vessels were simulated and superimposed onto uniform noise samples. Eighteen-alternative forced choice (18-AFC) experiments were employed to determine the detectability of the stenotic lesion in the structured-noise background of a blood vessel. In this study, the dependence of detectability on lesion size, vessel size, and incident x-ray exposure was examined. Our results indicate that the presence of structured noise in an image will reduce the detectability of a lesion. However, the relative performance of an observer when the lesion size and incident exposure were varied was the same with and without the presence of the structured background. Thus, conclusions obtained previously with regard to changes in the detectability of a lesion in the presence of uniform background noise can be applied directly to conditions in which simple structured anatomic background is present.     

Mammographic Image Denoising and Enhancement Using the Anscombe Transformation, Adaptive Wiener Filtering, and the Modulation Transfer Function

A new restoration methodology is proposed to enhance mammographic images through the improvement of contrast features and the simultaneous suppression of noise. Denoising is performed in the first step using the Anscombe transformation to convert the signal-dependent quantum noise into an approximately signal-independent Gaussian additive noise. In the Anscombe domain, noise is filtered through an adaptive Wiener filter, whose parameters are obtained by considering local image statistics. In the second step, a filter based on the modulation transfer function of the imaging system in the whole radiation field is applied for image enhancement. This methodology can be used as a preprocessing module for computer-aided detection (CAD) systems to improve the performance of breast cancer screening. A preliminary assessment of the restoration algorithm was performed using synthetic images with different levels of quantum noise. Afterward, we evaluated the effect of the preprocessing on the performance of a previously developed CAD system for clustered microcalcification detection in mammographic images. The results from the synthetic images showed an increase of up to 11.5 dB (p = 0.002) in the peak signal-to-noise ratio. Moreover, the mean structural similarity index increased up to 8.3 % (p < 0.001). Regarding CAD performance, the results suggested that the preprocessing increased the detectability of microcalcifications in mammographic images without increasing the false-positive rates. Receiver operating characteristic analysis revealed an average increase of 14.1 % (p = 0.01) in overall CAD performance when restored image sets were used.     

Impact of resolution and noise characteristics of digital radiographic detectors on the detectability of lung nodules

One of the unanswered questions in digital radiography is the connection between physical image quality metrics and clinical detection performance. In this paper, we examine the impact of two physical metrics, resolution and noise, on the detectability of nodules in a pulmonary background for specific digital radiographic detectors. A detection experiment was performed on a simulated image set using anatomical backgrounds from a high-quality lung radiograph and three different simulated nodule sizes (2-3.5 mm). The resolution and noise of the resulting images were modified using existing routines to simulate a selenium-based and a cesium iodide-based flat-panel detector at comparable exposures. A location-known-exactly (LKE) observer performance experiment was performed in which four experienced chest radiologists and three physicists specializing in chest radiology scored the images. The data from the observer experiment were analyzed by receiver operating characteristic (ROC) methodology. The detectability, as measured by the parameter Az, was higher for the selenium detector than the cesium iodide detector for all nodule sizes by an average of 8.5%. For one nodule size (2.75 mm), the difference between detectors was statistically significant (p < 0.01). The findings indicate that for the particular task studied, the superior resolution performance of the selenium-based detector provided better detectability of subtle lung nodules even though the images had greater noise than images obtained with the cesium iodide detector.     

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.     

A method for modifying the image quality parameters of digital radiographic images

A new computer simulation approach is presented that is capable of modeling several varieties of digital radiographic systems by their image quality characteristics. In this approach, the resolution and noise characteristics of ideal supersampled input images are modified according to input modulation transfer functions (MTFs) and noise power spectra (NPS). The modification process is separated into two routines-one for modification of the resolution and another for modification of the noise characteristics of the input image. The resolution modification routine blurs the input image by applying a frequency filter described by the input MTF. The resulting blurred image is then reduced to its final size to account for the sampling process of the digital system. The noise modification routine creates colored noise by filtering the frequency components of a white noise spectrum according to the input noise power. This noise is then applied to the image by a moving region of interest to account for variations in noise due to differences in attenuation. In order to evaluate the efficacy of the modification routines, additional routines were developed to assess the resolution and noise of digital images. The MTFs measured from the output images of the resolution modification routine were within 3% of the input MTF The NPS measured from the output images of the noise modification routine were within 2% of the input NPS. The findings indicate that the developed modification routines provide a good means of simulating the resolution and noise characteristics of digital radiographic systems for optimization or processing purposes.     

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.     

Assessment of the effects of pixel loss on image quality in direct digital radiography

Modern digital radiographic 'flat panel' detectors can exhibit a progressive form of image degradation arising from non-functioning pixels. The effect of these 'dead pixels' on the quantitive image quality measures of modulation transfer function (MTF), noise power spectrum (NPS) and detective quantum efficiency (DQE) is investigated by a simulated degradation of images obtained from an Hologic EPEX system. The effects on the semi-quantitive measures obtained from contrast threshold test objects and resolution gratings are also investigated. Results suggest that the contrast-detail tests often employed in quality assurance measures are not sufficient to reveal the presence of dead pixels until well beyond the recommended replacement point for the flat panel detector. However, measurements of spatial resolution using a line pairs phantom were found to be more sensitive to pixel loss. Measurement of the MTF, NPS and DQE can reveal small changes in image quality with increasing pixel loss, with a distinctive pattern in the trend of the NPS.     

A comparison of the performance of digital mammography systems

An objective analysis of image quality parameters was performed for six digital mammography systems. The presampled modulation transfer function (MTF), normalized noise power spectrum (NNPS), and detective quantum efficiency (DQE) for the systems were determined at different doses, for 28 kVp with a Mo/Mo or W/Al target/filter combination and 2 mm of additional aluminium filtration. The flat-panel units have higher MTF and DQE in the mid to high frequency range than standard CR systems. The highest DQE, over the whole dose range, is for the slit-scanning direct photon counting system. Dual-side read CR can overcome the inherent x-ray absorption and signal collection limitations of standard CR mammography, improving the low-frequency DQE by 40%, to the same level as full-field systems, but it does not improve the poor spatial resolution of phosphor.     

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.     

Effect of dose reduction on the detection of mammographic lesions: a mathematical observer model analysis

The effect of reduction in dose levels normally used in mammographic screening procedures on the detection of breast lesions were analyzed. Four types of breast lesions were simulated and inserted into clinically-acquired digital mammograms. Dose reduction by 50% and 75% of the original clinically-relevant exposure levels were simulated by adding corresponding simulated noise into the original mammograms. The mammograms were converted into luminance values corresponding to those displayed on a clinical soft-copy display station and subsequently analyzed by Laguerre-Gauss and Gabor channelized Hotelling observer models for differences in detectability performance with reduction in radiation dose. Performance was measured under a signal known exactly but variable detection task paradigm in terms of receiver operating characteristics (ROC) curves and area under the ROC curves. The results suggested that luminance mapping of digital mammograms affects performance of model observers. Reduction in dose levels by 50% lowered the detectability of masses with borderline statistical significance. Dose reduction did not have a statistically significant effect on detection of microcalcifications. The model results indicate that there is room for optimization of dose level in mammographic screening procedures.     

An improved method for simulating microcalcifications in digital mammograms

The assessment of the performance of a digital mammography system requires an observer study with a relatively large number of cases with known truth which is often difficult to assemble. Several investigators have developed methods for generating hybrid abnormal images containing simulated microcalcifications. This article addresses some of the limitations of earlier methods. The new method is based on digital images of needle biopsy specimens. Since the specimens are imaged separately from the breast, the microcalcification attenuation profile scan is deduced without the effects of over and underlying tissues. The resulting templates are normalized for image acquisition specific parameters and reprocessed to simulate microcalcifications appropriate to other imaging systems, with different x-ray, detector and image processing parameters than the original acquisition system. This capability is not shared by previous simulation methods that have relied on extracting microcalcifications from breast images. The method was validated by five experienced mammographers who compared 59 pairs of simulated and real microcalcifications in a two-alternative forced choice task designed to test if they could distinguish the real from the simulated lesions. They also classified the shapes of the microcalcifications according to a standardized clinical lexicon. The observed probability of correct choice was 0.415, 95% confidence interval (0.284, 0.546), showing that the radiologists were unable to distinguish the lesions. The shape classification revealed substantial agreement with the truth (mean kappa = 0.70), showing that we were able to accurately simulate the lesion morphology. While currently limited to single microcalcifications, the method is extensible to more complex clusters of microcalcifications and to three-dimensional images. It can be used to objectively assess an imaging technology, especially with respect to its ability to adequately visualize the morphology of the lesions, which is a critical factor in the benign versus malignant classification of a lesion detected in screening mammography.     

Image covariance and lesion detectability in direct fan-beam x-ray computed tomography

We consider noise in computed tomography images that are reconstructed using the classical direct fan-beam filtered backprojection algorithm, from both full- and short-scan data. A new, accurate method for computing image covariance is presented. The utility of the new covariance method is demonstrated by its application to the implementation of a channelized Hotelling observer for a lesion detection task. Results from the new covariance method and its application to the channelized Hotelling observer are compared with results from Monte Carlo simulations. In addition, the impact of a bowtie filter and x-ray tube current modulation on reconstruction noise and lesion detectability are explored for full-scan reconstruction.     

Étude de la faisabilité du scorage automatique de fantômes mammographiques par traitement d’image

Nowadays, X-ray mammography is one of the most effective methods for early and reliable breast cancer detection and diagnosis. Periodic quality control in mammographic facilities is necessary to provide high quality mammograms. This evaluation is done by visual observation of mammographic phantom films. To prepare Full Field Digital Mammography advent and to get rid of this subjective quality control, digital image and signal processing techniques may be used in order to make this control easier and more objective. This paper presents an automatic method for scoring mammographic phantoms using digital image processing. Phantom films were first digitized and images containing microcalcifications, masses and fibers were extracted. Theses noisy and low contrasted images were preprocessed using an adaptive contrast enhancement method and then segmented in order to extract objects embedded in phantom images. Nine digitized phantom films were studied and results show that a more objective quality control evaluation of mammographic facilities can be done using digital image processing techniques on phantom images.