Image Quality In Digital Mammography Research Articles

Comparing eDQE and eNEQ metrics – is there an alternative approach to assessing image quality in digital mammography?

Abstract Introduction: Early detection of breast cancer requires high-quality mammographic images that have been made possible by the introduction of new technologies, such as full-field digital mammography (FFDM). In this new study, we perform extended measurements to calculate effective detective quantum efficiency (eDQE) and introduce effective noise equivalent quanta (eNEQ). Our aim was to show how these two metrics relate to the image quality of two digital mammography systems. Material and methods: Measurements were performed for a Siemens Mammomat Inspiration and a GE Senographe Pristina system. Each was equipped with an automatic exposure control (AEC) for use in a clinical setting. We used a polymethyl methacrylate (PMMA) phantom at thicknesses of 20, 30, 40 and 70 mm to cover the range of scatter conditions expected in mammography, with and without an anti-scatter grid. The Siemens system had an a-Se detector, and the GE system had an indirect-conversion detector. Measurements of Kerma were performed with Piranha Black 657 meter (RTI Electronics AB). The majority of our calculations were automated, using a modified version of our software. Results: For the two mammographic systems evaluated, we characterized physical quality parameters, such as effective modulation transfer function (eMTF), effective normalized noise power spectrum (eNNPS), eDQE and eNEQ for a wide range of exposures, phantom thicknesses, with and without an anti-scatter grid. Results are presented as a function of spatial frequency. A contrast-detail analysis was performed with a CDMAM 3.4 phantom with dedicated software (CDMAM analysis 1.5.5, NCCPM) and a set of different PMMA phantoms. Conclusions: We successfully demonstrated that the eNEQ metric can be used as a new option to evaluate image quality for images taken with and without a grid and with phantoms of different thicknesses for the Siemens and GE systems. These results were consistent with the results obtained from CDMAM.

Optimization of Image Quality in Digital Mammography with the Response of a Selenium Detector by Monte Carlo Simulation

Mammography machines must meet high requirements to ensure the quality of the generated images. On the other hand, due to the use of ionizing radiation, there is a need to minimize the dose received by patients. To optimize both of these parameters (dose and image quality), the response characteristics of image detectors and, depending on the composition of the breasts, the physical contrast of the examined structures should be considered. This study aimed to determine the optimal voltage values for a given breast thickness during imaging with the use of a selenium image detector. Analysis was carried out using the Monte Carlo simulation method with the GEANT4 code. Our results reveal that the combination of Mo anode together with Mo filtration (the system recommended in analog mammography) was the least favorable combination among those used in digital mammography machines with a selenium detector. Moreover, the use of Rh filtration instead of Mo was advantageous regardless of the thickness of the breast and resulted in a significant improvement in image quality with the same dose absorbed in the breast. The most advantageous solution was found to be an X-ray tube with a W anode. The highest values of the image quality-to-dose ratio were observed for breasts with dimensions ranging from 53 mm to 60 mm in thickness. Lower image quality was observed for breasts with smaller dimensions due to high breast glandularity, resulting in the deterioration of the physical contrast.

A statistical alternative to current measures of image quality in digital mammography

Objective. Mammogram image quality in European breast screening systems is defined by threshold gold thickness (T) assessment of the CDMAM contrast-detail phantom. Previous studies have outlined several limitations of the phantom including expense, number of images required and inter-phantom manufacturing variability. Two alternative approaches to image quality assessment for routine quality control are examined and compared to the CDMAM technique: (i) A detectability index (d′) based on a non-prewhitened model observer with an eye filter (NPWE) and (ii) A statistical estimate of contrast based on image noise levels (CSTAT ). Approach. The d′ calculation follows a previously published methodology based on the NNPS and contrast, both measured from an image of 5 cm of PMMA containing a 0.2 mm Al target, as well as the MTF measured under standard conditions. For the proposed statistical method, pixels in the centre of the same NNPS image were re-binned into a range of equivalent CDMAM target areas. For any area, the minimum contrast necessary to distinguish a signal from the background, CSTAT , is 3.29σ at a 95% level of confidence, where σ is the standard deviation of the background pixels. Theoretical analysis predicts a simple relationships between CSTAT , T and d′. Measured values of CSTAT were compared to T and d′ as a function of air kerma at the detector for ten digital mammography systems from three different manufacturers. Main Results. Theoretical relationships between CSTAT , d′ and T were demonstrated. Minimum acceptable image quality performance for 0.10 and 0.25 mm diameter discs, defined by the European Guidelines in terms of T, are equivalent to d′ values of 0.85 and 5.36 and threshold CSTAT values of 0.055 and 0.022. Significance. Strong correlations between log(T), log(d′) and log(CSTAT ) suggest that either alternative approach produces information corresponding to that obtained using the CDMAM. CSTAT should be considered as a simple, objective and cost-effective alternative to routine image quality assessment in mammography.

Validation of a candidate instrument to assess image quality in digital mammography using ROC analysis

PurposeTo validate a candidate instrument, to be used by different professionals to assess image quality in digital mammography (DM), against detection performance results. MethodsA receiver operating characteristics (ROC) study was conducted to assess the detection performance in DM images with four different image quality levels due to different quality issues. Fourteen expert breast radiologists from five countries assessed a set of 80 DM cases, containing 60 lesions (40 cancers, 20 benign findings) and 20 normal cases. A visual grading analysis (VGA) study using a previously-described candidate instrument was conducted to evaluate a subset of 25 of the images used in the ROC study. Eight radiologists that had participated in the ROC study, and seven expert breast-imaging physicists, evaluated this subset. The VGA score (VGAS) and the ROC and visual grading characteristics (VGC) areas under the curve (AUCROC and AUCVGC) were compared. ResultsNo large differences in image quality among the four levels were detected by either ROC or VGA studies. However, the ranking of the four levels was consistent: level 1 (partial AUCROC: 0.070, VGAS: 6.77) performed better than levels 2 (0.066, 6.15), 3 (0.061, 5.82), and 4 (0.062, 5.37). Similarity between radiologists’ and physicists’ assessments was found (average VGAS difference of 10 %). ConclusionsThe results from the candidate instrument were found to correlate with those from ROC analysis, when used by either observer group. Therefore, it may be used by different professionals, such as radiologists, radiographers, and physicists, to assess clinically-relevant image quality variations in DM.

Development and content validity evaluation of a candidate instrument to assess image quality in digital mammography: A mixed-method study

PurposeTo develop a candidate instrument to assess image quality in digital mammography, by identifying clinically relevant features in images that are affected by lower image quality. MethodsInterviews with fifteen expert breast radiologists from five countries were conducted and analysed by using adapted directed content analysis. During these interviews, 45 mammographic cases, containing 44 lesions (30 cancers, 14 benign findings), and 5 normal cases, were shown with varying image quality. The interviews were performed to identify the structures from breast tissue and lesions relevant for image interpretation, and to investigate how image quality affected the visibility of those structures. The interview findings were used to develop tentative items, which were evaluated in terms of wording, understandability, and ambiguity with expert breast radiologists. The relevance of the tentative items was evaluated using the content validity index (CVI) and modified kappa index (k*). ResultsTwelve content areas, representing the content of image quality in digital mammography, emerged from the interviews and were converted into 29 tentative items. Fourteen of these items demonstrated excellent CVI ≥ 0.78 (k* > 0.74), one showed good CVI < 0.78 (0.60 ≤ k* ≤ 0.74), while fourteen were of fair or poor CVI < 0.78 (k* ≤ 0.59). In total, nine items were deleted and five were revised or combined resulting in 18 items. ConclusionsBy following a mixed-method methodology, a candidate instrument was developed that may be used to characterise the clinically-relevant impact that image quality variations can have on digital mammography.

Improved Image Quality in Digital Mammography Using Anti-Scatter Grids

Curability and effective management of breast cancer are dependent on its early detection. Early diagnosis strategies focus on providing timely access to breast cancer treatments by reducing barriers and improving access to effective early diagnosis. Mammography is the screening method of choice but suffers from degrading effects of scatter photons obstructing visualization of ductal carcinoma in situ and micro-calcifications in the breast. Quantitative evaluation of scatter suppression by anti-scatter grid using the beam stopper method has been investigated. Breast tissue equivalent phantom, polymethyl methacrylate was evaluated at X-ray mammographic nominal energy ranges. The anti-scatter grid used had aluminium interspace material and carbon fibre covers with varying grid features. Transmitted scatter and primary photon to the detector was analysed and evaluated. A scintillator type detector–model a Dexela 2315 MAM of size 290.8 ×229.8 mm having a resolution of 3072×3888 pixels was used. Transmitted scatter values in the range of 0.123 to 0.243 across the polymethyl methacrylate thickness of 10 mm to 80 mm were observed, whereas transmitted primary values recorded ranged from 0.713 to 0.495. Object, anti-scatter grid and X-ray energy exposure factors influenced the scatter fraction significantly. Improved mammography images showed significant improvements with this scatter reduction method using anti-scatter grid.&#x0D; Keywords: Digital mammography; Anti-scatter grids; Breast screening; Scatter image artifacts; Beam stopper method;

Study on patient dosimetry and image quality in digital mammography

Abstract Introduction Digital mammography present many advantages in comparison to conventional mammography, such as high dynamic range and the post-processing of acquired images. One problem is that protocols may not be optimized, resulting in higher absorbed doses to patients. The objective of this work is to evaluate image quality and to estimate mean glandular doses (MGD) in patients submitted to mammography examinations with three digital systems and one screen-film system in Recife, Brazil. Methods To estimate the MGD, the parameters used to acquire images of 5475 patients, with ages between 40 and 64 years and compressed breasts between 2 and 9 cm, were registered. The MGD was calculated by multiplying the incident air kerma with conversion coefficients depending on the anode/filter, breast glandularity and half-value layer. The image quality evaluation of the digital systems was made using objective and subjective European criteria. Results The results showed MGDs in the range of 0.4-10.3 mGy and the higher values were observed with digital systems. It was also observed that in the digital systems the use of compression force is not adequate and the irradiation parameters are not optimized. The images failed to reproduce the pectoral muscle and the contrast-to-noise ratio was not adequate for one system, indicating the need to improve the patient’s positioning and the exposure parameters. Conclusion It can be concluded that the use of non-optimized irradiation parameters is causing the higher doses with digital systems, highlighting the insufficient compression force.

The threshold contrast thickness evaluated with different CDMAM phantoms and software

Abstract The image quality in digital mammography is described by specifying the thickness and diameter of disks with threshold visibility. The European Commission recommends the CDMAM phantom as a tool to evaluate threshold contrast visibility in digital mammography [1, 2]. Inaccuracy of the manufacturing process of CDMAM 3.4 phantoms (Artinis Medical System BV), as well as differences between software used to analyze the images, may lead to discrepancies in the evaluation of threshold contrast visibility. The authors of this work used three CDMAM 3.4 phantoms with serial numbers 1669, 1840, and 1841 and two mammography systems of the same manufacturer with an identical types of detectors. The images were analyzed with EUREF software (version 1.5.5 with CDCOM 1.6. exe file) and Artinis software (version 1.2 with CDCOM 1.6. exe file). The differences between the observed thicknesses of the threshold contrast structures, which were caused by differences between the CDMAM 3.4 phantoms, were not reproduced in the same way on two mammography units of the same type. The thickness reported by the Artinis software (version 1.2 with CDCOM 1.6. exe file) was generally greater than the one determined by the EUREF software (version 1.5.5 with CDCOM 1.6. exe file), but the ratio of the results depended on the phantom and diameter of the structure. It was not possible to establish correction factors, which would allow correction of the differences between the results obtained for different CDMAM 3.4 phantoms, or to correct the differences between software. Great care must be taken when results of the tests performed with different CDMAM 3.4 phantoms and with different software application are interpreted.

Effect of filter on average glandular dose and image quality in digital mammography

To determine the average glandular dose and entrance surface air kerma in both phantoms and patients to assess image quality for different target-filters (W/Rh and W/Ag) in digital mammography system. The compressed breast thickness, compression force, average glandular dose, entrance surface air kerma, peak kilovoltage and tube current time were recorded and compared between W/Rh and W/Ag target filter. The CNR and the figure of merit were used to determine the effect of target filter on image quality. The mean AGD of the W/Rh target filter was 1.75 mGy, the mean ESAK was 6.67 mGy, the mean CBT was 54.1 mm, the mean CF was 14 1bs. The mean AGD of W/Ag target filter was 2.7 mGy, the mean ESAK was 12.6 mGy, the mean CBT was 75.5 mm, the mean CF was 15 1bs. In phantom study, the AGD was 1.2 mGy at 4 cm, 3.3 mGy at 6 cm and 3.83 mGy at 7 cm thickness. The FOM was 24.6, CNR was 9.02 at thickness 6 cm. The FOM was 18.4, CNR was 8.6 at thickness 7 cm. The AGD from Digital Mammogram system with W/Rh of thinner CBT was lower than the AGD from W/Ag target filter.

SU‐C‐116‐06: Evaluation of Image Quality in Digital Mammography and Digital Breast Tomosynthesis: Phantom Observer Study From American College of Radiology Imaging Network PA 4006

Purpose: Determine contributing factors for image quality in 2D and 3D from comparison of phantom scores and image acquisition parameters. Methods: Tomosynthesis 2D and 3D quality assurance images of the ACR Mammography Accreditation Phantom were obtained as part of the American College of Radiology Imaging Network PA‐4006 trial. Imaging was performed on Selenia Dimensions (Hologic, Bedford MA) systems at the Hospital of the University of Pennsylvania (n=176) and Einstein Medical Center, Philadelphia, PA (n=70). Four trained observers scored all available images according to manufacturer's instructions; image order was randomized by case number and modality for each observer. For tomosynthesis, observers selected the slice level maximizing conspicuity. Paired t‐test compared FFDM and DBT cumulative scores by site, multivariate regression related device parameters to image quality, and multi‐observer Kappa assessed interobserver agreement. Results: Fiber scores were higher for 3D than 2D at site B (p&lt;0.001) with no difference at site A. Speck and mass scores were greater for 2D than 3D at both sites (p&lt;0.001). At site A, regression analysis demonstrated positive correlation of exposure (mAs) with 2D mass, 3D fiber and 3D mass scores; no significant correlations were found for site B. Absolute level of interobserver agreement was highest for specks, followed by fibers and masses. There was substantially lower inter‐observer agreement for specks in 3D than 2D; the agreement between 2D and 3D for fibers and masses did not change significantly. Conclusion: Phantom scores differed significantly between 2D and 3D imaging; fibers were better visualized in 3D, while specks and masses were better visualized in 2D. Observers stated that scoring specks in 3D was more challenging; this is supported by the observation that inter‐observer agreement was significantly poorer in 3D than 2D only for specks. There are many factors that influence scoring. While phantom score was partially predicted by exposure, other factors were involved.

ACR–AAPM–SIIM Practice Guidelines

The ACR–AAPM–SIIM Practice Guidelines for “Determinants of Image Quality in Digital Mammography,” “Digital Radiography,” and “Electronic Practice of Medical Imaging” represent the collaborative efforts of three organizations to develop educational tools to assist practitioners in providing appropriate radiologic care for patients. The original versions of the guidelines were developed a number of years ago when digital radiography techniques were just beginning to be widely used. They therefore contained a significant amount of basic explanations about technical details and terms in addition to the practical guidelines themselves. Given the changes in technology in recent years as well as the general sophistication of radiology regarding digital imaging technology and techniques, a decision was made in 2011 to revise these three sets of guidelines, updating them to reflect these advances and streamline them for easier use and interpretation.

ACR–AAPM–SIIM Practice Guideline for Determinants of Image Quality in Digital Mammography

This guideline was developed collaboratively by individuals with recognized expertise in breast imaging, medical physics, and imaging informatics, representing the American College of Radiology (ACR), the American Association of Physicists in Medicine (AAPM), and the Society for Imaging Informatics in Medicine (SIIM), primarily for technical guidance. It is based on a review of the clinical and physics literature on digital mammography and the experience of experts and publications from the Image Quality Collaborative Workgroup [1–3]. For purposes of this guideline, digital mammography is defined as the radiographic examination of the breast utilizing dedicated electronic detectors to record the image (rather than screen film) and having the capability for image display on computer monitors. This guideline is specific to two-dimensional (2D) digital mammography since the vast majority of digital mammography performed in the USA is 2D. Although some three-dimensional technologies are in use, they are not addressed in this guideline since they continue to evolve and are not yet in widespread clinical use. In many parts of this guideline, the level of technical detail regarding the determinants of image quality for digital mammography is advanced, and is intended to provide radiologists, qualified medical physicists, regulators, and other support personnel directly involved in clinical implementation and oversight an expanded knowledge of the issues pertinent to assessing and maintaining digital mammography image quality from the acquisition, display, and data storage aspects of the process. Where basic technical requirements for digital mammography overlap with those for digital radiography in general, users are directed to consult the referenced ACR practice guidelines [4, 5]. All interested individuals are encouraged to review the ACR digital radiography guidelines. Additionally, this guideline includes input from industry, radiologists, and other interested parties in an attempt to represent the consensus of the broader community. It was further informed by input from another working group of the Integrating the Healthcare Enterprise (IHE) Initiative [6]. Furthermore, the ACR Subcommittee on Digital Mammography is developing a quality control (QC) manual for digital mammography. Analysis of image quality has meaning primarily in the context of a particular imaging task [7]. This guideline has been developed with reference to specific imaging tasks required by mammography, using the information available in the peer-reviewed medical literature regarding digital mammography acquisition, image processing and display, storage, transmission, and retrieval. Specifically, the imaging tasks unique to mammography that determine the essential characteristics of a high-quality mammogram are its ability to visualize the following features of breast cancer: The characteristic morphology of a mass. The shape and spatial configuration of calcifications. Distortion of the normal architecture of the breast tissue. Asymmetry between images of the left and right breast. The development of anatomically definable changes when compared with prior studies. The primary goal of mammography is to detect breast cancer, if it exists, by accurately visualizing these features. At the same time, it is important that these signs of breast cancer not be falsely identified if breast cancer is not present. Two aspects of digital image quality can be distinguished: technical and clinical. It is possible to make technical measurements describing the above attributes, and it may be possible to infer a connection between these technical measures and clinical image quality. The extent to which these features are rendered optimally with a digital mammography system using current technology depends on several factors and is the major focus of this guideline.

ACR–AAPM–SIIM Technical Standard for Electronic Practice of Medical Imaging

This technical standard has been revised by the American College of Radiology (ACR), the American Association of Physicists in Medicine (AAPM), and the Society for Imaging Informatics in Medicine (SIIM). For the purpose of this technical standard, the images referred to are those that diagnostic radiologists would normally interpret, including transmission projection and cross-sectional X-ray images, ionizing radiation emission images, and images from ultrasound and magnetic resonance modalities. Research, nonhuman and visible light images (such as dermatologic, histopathologic, or endoscopic images) are out of scope, though many of the same principles are applicable. Increasingly, medical imaging and patient information are being managed using digital data during acquisition, transmission, storage, display, interpretation, and consultation. The management of these data during each of these operations may have an impact on the quality of patient care. This technical standard is applicable to any system of digital image data management, from a single-modality or single-use system to a complete picture archiving and communication system (PACS) to the electronic transmission of patient medical images from one location to another for the purposes of interpretation and/or consultation. It defines goals, qualifications of personnel, equipment guidelines, specifications of data manipulation and management, and quality control and quality improvement procedures for the use of digital image data that should result in high-quality radiological care. A glossary of commonly used terminology (Appendix A) and a reference list are included. In all cases for which an ACR practice guideline or technical standard exists for the modality being used or the specific examination being performed, that practice guideline or technical standard will continue to apply when digital image data management systems are used. Digital mammography is outside the scope of this document. For further information, see the ACR–AAPM–SIIM Practice Guideline for Determinants of Image Quality in Digital Mammography. The goals of the electronic practice of medical imaging include, but are not limited to: Initial acquisition or generation and recording of accurately labeled and identified image data. Transmission of data to an appropriate storage medium from which it can be retrieved for display for formal interpretation, review, and consultation. Retrieval of data from available prior imaging studies to be displayed for comparison with a current study. Transmission of data to remote sites for consultation, review, or formal interpretation. Appropriate compression of image data to facilitate transmission or storage, without loss of clinically significant information. Archiving of data to maintain accurate patient medical records in a form that: May be retrieved in a timely fashion Meets applicable facility, state, and federal regulations Maintains patient confidentiality Promoting efficiency and quality improvement. Providing interpreted images to referring providers. Supporting telemedicine by making medical image consultations available in medical facilities without on-site medical imaging support. Providing supervision of off-site imaging studies. Providing timely availability of medical images, image consultation, and image interpretation by: Facilitating medical image interpretations in on-call situations Providing subspecialty support as needed. Enhancing educational opportunities for practicing radiologists. Minimizing the occurrence of poor image quality. Appropriate database management procedures applicable to all of the above should be in place.

Poster — Wed Eve—08: Effectiveness RMI‐156 Mammography Accreditation Phantom in Evaluating Digital Mammography Systems

PURPOSE: Evaluation of the Effectiveness RMI‐156 Mammography Accreditation Phantom in Digital Mammography METHOD AND MATERIALS: Modulation Transfer Function (MTF) and Signal Difference‐to‐Noise Ratio (SDNR) are considered to be the quantitative image quality matrices that can be used to characterize image quality in digital mammography. However there is no established information regarding the acceptable values for these quantities. This is further complicated by the fact that different detector technologies (pixel size, DQE etc.) are used in digital mammography. Recent experience with two different digital mammography systems (incorporating different digital detectors) from the same manufacturer indicated that the RMI‐156 phantom image quality is better suited for clinical evaluation and comparison of digital mammography systems. RESULTS: SDNR values for a 1 mm disk placed on a 4 cm Lucite phantom for Siemens Novation (70 μm pixel) and Inspiration (85 μm pixel) were calculated to be 3.0 and 3.2 respectively during acceptance testing. However, the evaluation of RMI‐156 phantom image from both systems indicated the image from Inspiration system with higher SDNR had inferior image quality. The Inspiration system was therefore re‐calibrated to increase exposure factors by 33% leading to a new SDNR value of 3.5. This clearly demonstrated that SDNR and MTF measures are not yet established for digital mammography systems for acceptance testing and that RMI‐156 phantom can still be used for digital mammography system evaluations. CONCLUSION: RMI‐156 mammography accreditation phantom is found to be a better image quality indicator in comparing and calibrating digital mammography systems than SDNR and MTF estimates.

Image Quality in Digital Mammography: Image Acquisition

This paper on digital mammography image acquisition is 1 of 3 papers written as part of an intersociety effort to establish image quality standards for digital mammography. The information included in this paper is intended to support the development of an ACR guideline on image quality for digital mammography. The topics of the other 2 papers are digital mammography image display and digital mammography image storage, transmission, and retrieval. The societies represented in compiling this document were the Radiological Society of North America, the ACR, the American Association of Physicists in Medicine, and the Society for Computer Applications in Radiology. These papers describe in detail what is known to improve image quality for digital mammography and make recommendations about how digital mammography should be performed to optimize the visualization of breast cancers. Through the publication of these papers, the ACR is seeking input from industry, radiologists, and other interested parties on their contents so that the final ACR guideline for digital mammography will represent the consensus of the broader community interested in these topics.

SU‐FF‐I‐75: Geometric Magnification in Digital Mammography

Purpose: The goal of this study was to investigate the effect of varying geometric magnifications on the edge definition as well as conspicuity of calcifications in images obtained using a full field digital mammography system. Method and Materials: Images of the wax insert of an ACR mammography phantom, sandwiched between two clear acrylic slabs simulating scattering in breast tissue were obtained on flat panel FFDM system at five different geometric magnifications between 1.4 and 2.2 plus a contact image. The x‐ray technique was the same for all images. All images were viewed on a soft copy work station. Observers were asked to judge the quality of the images in terms of edge definition (largest speck group) and conspicuity (two smallest speck groups) and assign a score on a scale of 1 to 4 for each speck in the group. Additionally, the system MTF was measured using the edge method at each of the magnifications in order to correlate the system response with the observer scores. The MTF was measured in the anode‐cathode (AC) direction as well as perpendicular (LR) to it. Results: The trends in the scores indicated that the conspicuity of the smallest speck group increased with magnification. The edge definition of the largest speck group was found to increase and then decrease depending on the magnification, reaching a maximum at a magnification of 1.6. This correlates well with the measured MTF in the LR direction. Conclusion: Geometric magnification improves image quality in digital mammography. Conspicuity of the smallest calcifications improves with magnification. However, this is accompanied by a decrease in the edge definition of the larger calcifications after a magnification of 1.6. The optimum magnification depends on the specific task (edge definition or conspicuity).

Effect of area x-ray beam equalization on image quality and dose in digital mammography

In mammography, thick or dense breast regions persistently suffer from reduced contrast-to-noise ratio (CNR) because of degraded contrast from large scatter intensities and relatively high noise. Area x-ray beam equalization can improve image quality by increasing the x-ray exposure to under-penetrated regions without increasing the exposure to other breast regions. Optimal equalization parameters with respect to image quality and patient dose were determined through computer simulations and validated with experimental observations on a step phantom and an anthropomorphic breast phantom. Three parameters important in equalization digital mammography were considered: attenuator material (Z = 13–92), beam energy (22–34 kVp) and equalization level. A Mo/Mo digital mammography system was used for image acquisition. A prototype 16 × 16 piston driven equalization system was used for preparing patient-specific equalization masks. Simulation studies showed that a molybdenum attenuator and an equalization level of 20 were optimal for improving contrast, CNR and figure of merit (FOM = CNR2/dose). Experimental measurements using these parameters showed significant improvements in contrast, CNR and FOM. Moreover, equalized images of a breast phantom showed improved image quality. These results indicate that area beam equalization can improve image quality in digital mammography.

How Good is the ACR Accreditation Phantom for Assessing Image Quality in Digital Mammography?

Rationale and Objectives The purpose of this study was to evaluate the American College of Radiology (ACR) accreditation phantom for assessing image quality in digital mammography. Materials and Methods Digital images were obtained of an ACR accreditation phantom at varying mAs (constant kVp) and varying kVp (constant mAs). The average glandular dose for a breast with 50% glandularity was determined for each technique factor. Images were displayed on a 5 mega-pixel monitor, with the window width and level settings individually optimized for viewing the fibers, specks, and masses in the ACR phantom. Digital images of the ACR phantom were presented in a random manner to eight observers, each of whom indicated the number of objects visible in each image. Results Intraobserver variability was greater than interobserver variability for the detection of fibers and specks, but the reverse was true for the detection of masses. As the mAs increased, the number of fibers visible increased from less than one at 5 mAs to all six being visible at 80 mAs. The corresponding number of visible specks increased from 12 to 24, and the number of visible masses increased from 1.25 to about four. Above 26 kVp, object visibility was constant with increasing x-ray tube voltage. Reducing the x-ray tube voltage to 24 kVp, however, reduced the number of visible fibers from six to five, the number of visible specks from 24 to 21.1, and the number of visible masses from four to 3.1. Observer performance was approximately constant for average glandular doses greater than 1.6 mGy, so that the range of lesion detectability in the ACR phantom occurs at doses lower than those normally encountered in clinical practice. Conclusion The current design of the ACR phantom is unsatisfactory for assessing image quality in digital mammography.

Improved image quality in digital mammography with image processing.

The effect of image processing, specifically Bayesian image estimation (BIE), on digital mammographic images is studied. BIE is an iterative, nonlinear statistical estimation technique that has previously been used in chest radiography to reduce image scatter content and improve the contrast-to-noise ratio (CNR). We adapt this technique to digital mammography and examine its effect. Images of the American College of Radiologists (ACR) breast phantom were acquired on a calibrated digital mammography system at a normal mammographic exposure both with and without a grid. An iterative Bayesian estimation algorithm was formulated and used to process the images acquired without a grid. Quantitative scatter fractions were measured and compared for the image acquired with the grid, the image acquired without the grid, and the image acquired without the grid and processed by the Bayesian algorithm. CNR values were also computed for the four visible masses in the ACR phantom before and after processing and compared to a grid. Initial images acquired without an antiscatter grid had scatter fractions of 0.46. Processing this image with BIE reduced the scatter content to under 0.04. In comparison, the image acquired with a grid had scatter of 0.19. BIE processing accounted for CNR improvements from 29% to 219% for the masses seen in the ACR phantom as compared to the unprocessed image. Visibility of the four masses in the phantom was improved. Bayesian image estimation can be used with digital mammography to reduce scatter fractions. This technique is very useful as it can reduce scatter content effectively without introducing any adverse effects, such as grid line aliasing. Bayesian processing can also increase image CNR, which may potentially increase the visualization of subtle masses. Preliminary work shows an improvement in CNR to values greater than that provided by a standard grid.