Showing papers on "Digital mammography published in 2008"
TL;DR: The results indicate that the cancer visibility on BT is superior to DM, which suggests that BT may have a higher sensitivity for breast cancer detection.
Abstract: The main purpose was to compare breast cancer visibility in one-view breast tomosynthesis (BT) to cancer visibility in one- or two-view digital mammography (DM). Thirty-six patients were selected on the basis of subtle signs of breast cancer on DM. One-view BT was performed with the same compression angle as the DM image in which the finding was least/not visible. On BT, 25 projections images were acquired over an angular range of 50 degrees, with double the dose of one-view DM. Two expert breast imagers classified one- and two-view DM, and BT findings for cancer visibility and BIRADS cancer probability in a non-blinded consensus study. Forty breast cancers were found in 37 breasts. The cancers were rated more visible on BT compared to one-view and two-view DM in 22 and 11 cases, respectively, (p < 0.01 for both comparisons). Comparing one-view DM to one-view BT, 21 patients were upgraded on BIRADS classification (p < 0.01). Comparing two-view DM to one-view BT, 12 patients were upgraded on BIRADS classification (p < 0.01). The results indicate that the cancer visibility on BT is superior to DM, which suggests that BT may have a higher sensitivity for breast cancer detection.
351 citations
TL;DR: Screening for breast cancer by using all-digital mammography is not cost-effective, whereas density-targeted screening strategies are more costly and of uncertain value, particularly among women age 65 years or older.
Abstract: Digital mammography is more accurate than film mammography in younger women and in women with dense breasts. In a cost-effectiveness analysis, Tosteson and colleagues estimate that using digital ma...
188 citations
TL;DR: It appears that the primary imaging tool for breast cancer screening in the next decade will be high-resolution, high-contrast, anatomical x-ray imaging with or without depth information, and there is a trend towards multi-modality systems that combine anatomic with physiologic information.
Abstract: Breast imaging is largely indicated for detection, diagnosis, and clinical management of breast cancer and for evaluation of the integrity of breast implants. In this work, a prospective view of techniques for breast cancer detection and diagnosis is provided based on an assessment of current trends. The potential role of emerging techniques that are under various stages of research and development is also addressed. It appears that the primary imaging tool for breast cancer screening in the next decade will be high-resolution, high-contrast, anatomical x-ray imaging with or without depth information. MRI and ultrasonography will have an increasingly important adjunctive role for imaging high-risk patients and women with dense breasts. Pilot studies with dedicated breast CT have demonstrated high-resolution three-dimensional imaging capabilities, but several technological barriers must be overcome before clinical adoption. Radionuclide based imaging techniques and x-ray imaging with intravenously injected contrast offer substantial potential as a diagnostic tools and for evaluation of suspicious lesions. Developing optical and electromagnetic imaging techniques hold significant potential for physiologic information and they are likely to be of most value when integrated with or adjunctively used with techniques that provide anatomic information. Experimental studies with breast specimens suggest that phase-sensitive x-ray imaging techniques can provide edge enhancement and contrast improvement but more research is needed to evaluate their potential role in clinical breast imaging. From the technological perspective, in addition to improvements within each modality, there is likely to be a trend towards multi-modality systems that combine anatomic with physiologic information. We are also likely to transition from a standardized screening, where all women undergo the same imaging exam (mammography), to selection of a screening modality or modalities based an individual-risk or other classification.
149 citations
TL;DR: Tomosynthesis-based breast imaging may have great potential, but much work is needed before its optimal role in the clinical environment is known.
Abstract: OBJECTIVE. The objective of our study was to assess ergonomic and diagnostic performance–related issues associated with the interpretation of digital breast tomosynthesis–generated examinations.MATERIALS AND METHODS. Thirty selected cases were read under three different display conditions by nine experienced radiologists in a fully crossed, mode-balanced observer performance study. The reading modes included full-field digital mammography (FFDM) alone, the 11 low-dose projections acquired for the reconstruction of tomosynthesis images, and the reconstructed digital breast tomosynthesis examination. Observers rated cases under the free-response receiver operating characteristic, as well as a screening paradigm, and provided subjective assessments of the relative diagnostic value of the two digital breast tomosynthesis–based image sets as compared with FFDM. The time to review and diagnose each case was also evaluated.RESULTS. Observer performance measures were not statistically significant (p > 0.05) prima...
147 citations
TL;DR: An automated method for mammographic mass segmentation is developed and new image based features in combination with patient information are explored in order to improve the performance of mass characterization.
Abstract: Computer-aided diagnosis (CAD) for characterization of mammographic masses as malignant or benign has the potential to assist radiologists in reducing the biopsy rate without increasing false negatives. The purpose of this study was to develop an automated method for mammographic mass segmentation and explore new image based features in combination with patient information in order to improve the performance of mass characterization. Our previous CAD system, which used the active contour segmentation, and morphological, textural, and spiculation features, has achieved promising results in mass characterization. The new CAD system is based on the level set method, and includes two new types of image features related to the presence of microcalcifications with the mass and abruptness of the mass margin, and patient age. A linear discriminant analysis (LDA) classifier with stepwise feature selection was used to merge the extracted features into a classification score. The classification accuracy was evaluated using the area under the receiver operating characteristic curve. Our primary data set consisted of 427 biopsy-proven masses (200 malignant and 227 benign) in 909 regions of interest (ROIs) (451 malignant and 458 benign) from multiple mammographic views. Leave-one-case-out resampling was used for training and testing. The new CAD system based on the level set segmentation and the new mammographic feature space achieved a view-based Az value of 0.83±0.01. The improvement compared to the previous CAD system was statistically significant (p=0.02). When patient age was included in the new CAD system, view-based and case-based Az values were 0.85±0.01 and 0.87±0.02, respectively. The performance of the new CAD system was also compared to an experienced radiologist’s likelihood of malignancy rating. When patient age was used in classification, the accuracy of the new CAD system was comparable to that of the radiologist (p=0.34). The study also demonstrated the consistency of the newly developed CAD system by evaluating the statistics of the weights of the LDA classifiers in leave-one-case-out classification. Finally, an independent test on the publicly available digital database for screening mammography (DDSM) with 132 benign and 197 malignant ROIs containing masses achieved a view-based Az value of 0.84±0.02.
115 citations
TL;DR: In this paper, the authors compared the performance of full-field digital mammography with soft-copy reading to screen film mammography (SFM) used during the first prevalent 2-year round of population-based screening.
Abstract: The purpose of the study was to compare the performance of full-field digital mammography (FFDM) with soft-copy reading to screen film mammography (SFM) used during the first prevalent 2-year round of population-based screening. A total of 18,239 women aged 50-69 years were screened with FFDM as part of the Norwegian Breast Cancer Screening Programme (NBCSP). Process indicators were compared to data from 324,763 women screened with SFM using the common national database of the NBCSP. The cancer detection rates were 0.77% (140/18,239) for FFDM and 0.65% (2,105/324,763) for SFM (p = 0.058). For ductal carcinoma in situ (DCIS) alone, the results were: FFDM 0.21% (38/18,239) compared to SFM 0.11% (343/324,763) (p < 0.001). Recall rates due to positive mammography were for FFDM 4.09% (746/18,239), while for SFM 4.16% (13,520/324,764) (p = 0.645), due to technically insufficient imaging: FFDM 0.22% (40/18,239) versus SFM 0.61% (1,993/324,763) (p < 0.001). The positive predictive value (PPV) in the FFDM group was 16.6% (140/843), while 13.5% (2,105/15,537) for SFM (p = 0.014). No statistically significant differences were recorded concerning histological morphology, tumour size, or lymph node involvement. In conclusion FFDM had a significantly higher detection rate for DCIS than SFM. For invasive cancers no difference was seen. FFDM also had a significantly higher PPV and a significantly lower technical recall rate.
104 citations
TL;DR: The results showed that the detector performance is x-ray quantum noise limited at the low exposures used in each view of tomosynthesis, and the temporal performance at high frame rate (up to 2 frames per second) is adequate forTomosynthesis.
Abstract: In breast tomosynthesis a rapid sequence of N images is acquired when the x-ray tube sweeps through different angular views with respect to the breast. Since the total dose to the breast is kept the same as that in regular mammography, the exposure used for each image of tomosynthesis is 1/N. The low dose and high frame rate pose a tremendous challenge to the imaging performance of digital mammography detectors. The purpose of the present work is to investigate the detector performance in different operational modes designed for tomosynthesis acquisition, e.g., binning or full resolution readout, the range of view angles, and the number of views N. A prototype breast tomosynthesis system with a nominal angular range of +/-25 degrees was used in our investigation. The system was equipped with an amorphous selenium (a-Se) full field digital mammography detector with pixel size of 85 microm. The detector can be read out in full resolution or 2 x 1 binning (binning in the tube travel direction). The focal spot blur due to continuous tube travel was measured for different acquisition geometries, and it was found that pixel binning, instead of focal spot blur, dominates the detector modulation transfer function (MTF). The noise power spectrum (NPS) and detective quantum efficiency (DQE) of the detector were measured with the exposure range of 0.4-6 mR, which is relevant to the low dose used in tomosynthesis. It was found that DQE at 0.4 mR is only 20% less than that at highest exposure for both detector readout modes. The detector temporal performance was categorized as lag and ghosting, both of which were measured as a function of x-ray exposure. The first frame lags were 8% and 4%, respectively, for binning and full resolution mode. Ghosting is negligible and independent of the frame rate. The results showed that the detector performance is x-ray quantum noise limited at the low exposures used in each view of tomosynthesis, and the temporal performance at high frame rate (up to 2 frames per second) is adequate for tomosynthesis.
101 citations
TL;DR: Knowing the mammographic appearance and evolution of patterns of fat necrosis in patients who have undergone breast fat injection may enable imaging follow-up of these lesions, reducing the number of unnecessary biopsies or additional examinations and avoiding possible delays in the diagnosis of breast cancer.
95 citations
TL;DR: The combined system is a promising approach to improving automated mass detection on DBTs and can again be generated by varying the decision threshold on the 3D mass likelihood scores merged by backprojection.
Abstract: The authors are developing a computer-aided detection CAD system for masses on digital breast tomosynthesis mammograms DBT. Three approaches were evaluated in this study. In the first approach, mass candidate identification and feature analysis are performed in the reconstructed three-dimensional 3D DBT volume. A mass likelihood score is estimated for each mass candidate using a linear discriminant analysis LDA classifier. Mass detection is determined by a decision threshold applied to the mass likelihood score. A free response receiver operating characteristic FROC curve that describes the detection sensitivity as a function of the number of false positives FPs per breast is generated by varying the decision threshold over a range. In the second approach, prescreening of mass candidate and feature analysis are first performed on the individual two-dimensional 2D projection view PV images. A mass likelihood score is estimated for each mass candidate using an LDA classifier trained for the 2D features. The mass likelihood images derived from the PVs are backprojected to the breast volume to estimate the 3D spatial distribution of the mass likelihood scores. The FROC curve for mass detection can again be generated by varying the decision threshold on the 3D mass likelihood scores merged by backprojection. In the third approach, the mass likelihood scores estimated by the 3D and 2D approaches, described above, at the corresponding 3D location are combined and evaluated using FROC analysis. A data set of 100 DBT cases acquired with a GE prototype system at the Breast Imaging Laboratory in the Massachusetts General Hospital was used for comparison of the three approaches. The LDA classifiers with stepwise feature selection were designed with leave-one-case-out resampling. In FROC analysis, the CAD system for detection in the DBT volume alone achieved test sensitivities of 80% and 90% at average FP rates of 1.94 and 3.40 per breast, respectively. With the 2D detection approach, the FP rates were 2.86 and 4.05 per breast, respectively, at the corresponding sensitivities. In comparison, the average FP rates of the system combining the 3D and 2D information were 1.23 and 2.04 per breast, respectively, at 80% and 90% sensitivities. The difference in the detection performances between the 2D and the 3D approach, and that between the 3D and the combined approach were both statistically significant p=0.02 and 0.01, respectively as estimated by alternative FROC analysis. The combined system is a promising approach to improving automated mass detection on DBTs. © 2008 American Association of Physicists in Medicine. DOI: 10.1118/1.2968098
93 citations
TL;DR: The radiation dose to all tissues other than the breast is extremely low and the dose to the first-trimester fetus is minimal.
Abstract: Purpose: To prospectively determine the radiation dose to the organs of the body during standard bilateral two-view mammography by using Monte Carlo simulations and a phantom. Materials and Methods: A modified version of the Cristy mathematic anthropomorphic phantom was implemented in the Geant4 Monte Carlo tool kit to simulate the conditions present in screen-film and digital mammography. The breast was simulated with compression in both the craniocaudal and the mediolateral oblique views. X-rays were tracked from the source until their absorption in the body or in the detector or their exit from the simulation limits, with recording of all the intermediate interactions in the body. The simulation was performed with x-rays of energy ranging from 6 to 35 keV to obtain results for clinically relevant spectra. The ratio of dose to an organ in the body per unit glandular dose to the breast, denoted the relative organ dose (ROD), was computed. The effect of using a body protective shield was also investigated...
91 citations
TL;DR: The results show that, for a given target/filter combination, in general FOM is a slowly changing function of kVp, with stronger dependence on the choice of target/ filter combination, than the previous study, which indicated that higher tube voltages would produce no further performance improvement.
Abstract: Optimization of exposure parameters (target, filter, and kVp) in digital mammography necessitates maximization of the image signal-to-noise ratio (SNR), while simultaneously minimizing patient dose. The goal of this study is to compare, for each of the major commercially available full field digital mammography (FFDM) systems, the impact of the selection of technique factors on image SNR and radiation dose for a range of breast thickness and tissue types. This phantom study is an update of a previous investigation and includes measurements on recent versions of two of the FFDM systems discussed in that article, as well as on three FFDM systems not available at that time. The five commercial FFDM systems tested, the Senographe 2000D from GE Healthcare, the Mammomat Novation DR from Siemens, the Selenia from Hologic, the Fischer Senoscan, and Fuji’s 5000MA used with a Lorad M-IV mammography unit, are located at five different university test sites. Performance was assessed using all available x-ray target and filter combinations and nine different phantom types (three compressed thicknesses and three tissue composition types). Each phantom type was also imaged using the automatic exposure control (AEC) of each system to identify the exposure parameters used under automated image acquisition. The figure of merit (FOM) used to compare technique factors is the ratio of the square of the image SNR to the mean glandular dose. The results show that, for a given target∕filter combination, in general FOM is a slowly changing function of kVp, with stronger dependence on the choice of target∕filter combination. In all cases the FOM was a decreasing function of kVp at the top of the available range of kVp settings, indicating that higher tube voltages would produce no further performance improvement. For a given phantom type, the exposure parameter set resulting in the highest FOM value was system specific, depending on both the set of available target∕filter combinations, and on the receptor type. In most cases, the AECs of the FFDM systems successfully identified exposure parameters resulting in FOM values near the maximum ones, however, there were several examples where AEC performance could be improved.
TL;DR: The role of medical physics is covered in addressing issues directly related to patient dosimetry in radiography, fluoroscopy, mammography, and CT, which has grown into one of the most important modalities available for diagnostic imaging.
Abstract: This review covers the role of medical physics in addressing issues directly related to patient dosimetry in radiography, fluoroscopy, mammography, and CT. The sections on radiography and fluoroscopy radiation doses review the changes that have occurred during the last 50 to 60 years. A number of technological improvements have contributed to both a significant reduction in patient and staff radiation doses and improvements to the image quality during this period of time. There has been a transition from film-screen radiography with hand dip film processing to electronic digital imaging utilizing CR and DR. Similarly, fluoroscopy has progressed by directly viewing image intensifiers in darkened rooms to modern flat panel image receptor systems utilizing pulsed radiation, automated variable filtration, and digitally processed images. Mammography is one of the most highly optimized imaging procedures performed, because it is a repetitive screening procedure that results in annual radiation exposure. Mammography is also the only imaging procedure in the United States in which the radiation dose is regulated by the federal government. Consequently, many medical physicists have studied the dosimetry associated with screen-film and digital mammography. In this review, a brief history of mammography dose assessment by medical physicists is discussed. CT was introduced into clinical practice in the early 1970s, and has grown into one of the most important modalities available for diagnostic imaging. CT dose quantities and measurement techniques are described, and values of radiation dose for different types of scanner are presented. Organ and effective doses to adult patients are surveyed from the earliest single slice scanners, to the latest versions that include up to two x-ray tubes and can incorporate as many as 256 detector channels. An overview is provided of doses received by pediatric patients undergoing CT examinations, as well as methods, and results, of studies performed to assess the radiation absorbed by the conceptus of pregnant patients.
TL;DR: Preliminary analysis shows the high diagnostic quality of the PhC SR images that were acquired with equal or less delivered dose compared to the conventional ones.
20 Jul 2008
TL;DR: First results from a new automated algorithm for the volumetric measurement of the composition of breast tissue from digital mammograms are presented, which overcomes issues in previous implementations through better segmentation and use of additional information.
Abstract: We present first results from a new automated algorithm for the volumetric measurement of the composition of breast tissue from digital mammograms. The new algorithm overcomes issues in previous implementations through better segmentation and use of additional information We measure the success of the new algorithm using an overall quality metric based upon the results from a large multi-site, multi-vendor, multi-detector set of digitally acquired mammograms.
06 Mar 2008
TL;DR: Despite some limitations automated reading of CDMAM images can provide a reproducible means of assessing digital mammography systems against European Guidelines and provide a means of predicting average human performance using the automated reading software.
Abstract: European Guidelines for quality control in digital mammography specify minimum and achievable standards of image quality in terms of threshold contrast, based on readings of images of the CDMAM test object by human observers. However this is time-consuming and has large inter- and intra-observer error. To overcome these problems a software program (CDCOM) is available to automatically read CDMAM images. After some further analysis the automated measurements can be used to predict the threshold contrast for a typical observer. The results of threshold contrast determination by human observers at three different centres were compared against automated readings. These data provide a means of predicting average human performance using the automated reading software. The coefficient of variation in automatically determined threshold gold thickness was about 4% for detail sizes from 0.2 to 1.0mm when 8 images were analysed. The coefficient of variation was about 10% at a detail size of 0.1mm. Using larger numbers of images improved reproducibility for all detail sizes. A change in phantom design could greatly improve reproducibility for the smallest detail sizes. Greater consistency of phantom construction would also be desirable as one of the four phantoms tested was significantly different from the other three. Despite some limitations automated reading of CDMAM images can provide a reproducible means of assessing digital mammography systems against European Guidelines.
27 Mar 2008
TL;DR: An available mammography database developed from the union of: The Mammographic Image Analysis Society Digital Mammogram Database (MIAS), The Digital Database for Screening Mammography (DDSM), the Lawrence Livermore National Laboratory (LLNL), and routine images from the Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen is presented.
Abstract: Because of the lack of mammography databases with a large amount of codified images and identified characteristics
like pathology, type of breast tissue, and abnormality, there is a problem for the development of robust systems for
computer-aided diagnosis. Integrated to the Image Retrieval in Medical Applications (IRMA) project, we present an
available mammography database developed from the union of: The Mammographic Image Analysis Society Digital
Mammogram Database (MIAS), The Digital Database for Screening Mammography (DDSM), the Lawrence Livermore
National Laboratory (LLNL), and routine images from the Rheinisch-Westfalische Technische Hochschule (RWTH)
Aachen. Using the IRMA code, standardized coding of tissue type, tumor staging, and lesion description was developed
according to the American College of Radiology (ACR) tissue codes and the ACR breast imaging reporting and data
system (BI-RADS). The import was done automatically using scripts for image download, file format conversion, file
name, web page and information file browsing. Disregarding the resolution, this resulted in a total of 10,509 reference
images, and 6,767 images are associated with an IRMA contour information feature file. In accordance to the respective
license agreements, the database will be made freely available for research purposes, and may be used for image based
evaluation campaigns such as the Cross Language Evaluation Forum (CLEF). We have also shown that it can be
extended easily with further cases imported from a picture archiving and communication system (PACS).
TL;DR: The authors have invented a new class of linear filters, spiculated lesion filters, for the detection of converging lines or spiculations, and invented a novel technique to enhance spicules on mammograms.
Abstract: The detection of lesions on mammography is a repetitive and fatiguing task. Thus, computer-aided detection systems have been developed to aid radiologists. The detection accuracy of current systems is much higher for clusters of microcalcifications than for spiculated masses. In this article, the authors present a new model-based framework for the detection of spiculated masses. The authors have invented a new class of linear filters, spiculated lesion filters, for the detection of converging lines or spiculations. These filters are highly specific narrowband filters, which are designed to match the expected structures of spiculated masses. As a part of this algorithm, the authors have also invented a novel technique to enhance spicules on mammograms. This entails filtering in the radon domain. They have also developed models to reduce the false positives due to normal linear structures. A key contribution of this work is that the parameters of the detection algorithm are based on measurements of physical properties of spiculated masses. The results of the detection algorithm are presented in the form of free-response receiver operating characteristic curves on images from the Mammographic Image Analysis Society and Digital Database for Screening Mammography databases.
TL;DR: An algorithm for computerized detection of microcalcification clusters (MCCs) for DBT that operates on the projection views only, which does not depend on reconstruction, and is computationally efficient.
Abstract: Digital breast tomosynthesis (DBT) is a promising modality for breast imaging in which an anisotropic volume image of the breast is obtained. We present an algorithm for computerized detection of microcalcification clusters (MCCs) for DBT. This algorithm operates on the projection views only. Therefore it does not depend on reconstruction, and is computationally efficient. The algorithm was developed using a database of 30 image sets with microcalcifications, and a control group of 30 image sets without visible findings. The patient data were acquired on the first DBT prototype at Massachusetts General Hospital. Algorithm sensitivity was estimated to be 0.86 at 1.3 false positive clusters, which is below that of current MCC detection algorithms for full-field digital mammography. Because of the small number of patient cases, algorithm parameters were not optimized and one linear classifier was used. An actual limitation of our approach may be that the signal-to-noise ratio in the projection images is too low for microcalcification detection. Furthermore, the database consisted of predominantly small MCC. This may be related to the image quality obtained with this first prototype.
TL;DR: The results suggest that if a particular imaging system can handle an approximately 10% increase in total tube output and 10% decrease in detector signal, breast compression can be reduced by about 12% in terms of breast thickness with little impact on image quality or dose.
Abstract: This study analyzed how the inherent quality of diagnostic information in digital mammography could be affected by breast compression. A digital mammography system was modeled using a Monte Carlo algorithm based on the Penelope program, which has been successfully used to model several medical imaging systems. First, the Monte Carlo program was validated against previous measurements and simulations. Once validated, the Monte Carlo software modeled a digital mammography system by tracking photons through a voxelized software breast phantom, containing anatomical structures and breast masses, and following photons until they were absorbed by a selenium-based flat-panel detector. Simulations were performed for two compression conditions (standard compression and 12.5% reduced compression) and three photon flux conditions (constant flux, constant detector signal, and constant glandular dose). The results showed that reduced compression led to higher scatter fractions, as expected. For the constant photon flux condition, decreased compression also reduced glandular dose. For constant glandular dose, the SdNR for a 4 cm breast was 0.60 +/- 0.11 and 0.62 +/- 0.11 under standard and reduced compressions, respectively. For the 6 cm case with constant glandular dose, the SdNR was 0.50 +/- 0.11 and 0.49 +/- 0.10 under standard and reduced compressions, respectively. The results suggest that if a particular imaging system can handle an approximately 10% increase in total tube output and 10% decrease in detector signal, breast compression can be reduced by about 12% in terms of breast thickness with little impact on image quality or dose.
Patent•
23 Mar 2008
TL;DR: In this paper, a method and apparatus for an image preprocessing device that automatically detects chestwall laterality, removes border artifacts, and segments breast tissue and pectoral muscle from digital mammograms is described.
Abstract: A method and apparatus are disclosed for an image preprocessing device that automatically detects chestwall laterality; removes border artifacts; and segments breast tissue and pectoral muscle from digital mammograms. The algorithms in the preprocessing device utilize the computer cache, a vertical Sobel filter and a probabilistic Hough transform to detect curved edges. The preprocessing result, along with a pseudo-modality normalized image, can be used as input to a CAD (computer-aided detection) server or to a mammography image review workstation. In the case of workstation input, the preprocessing results improve the protocol for chestwall-to-chestwall image hanging, and support optimal image contrast display of each segmented region.
TL;DR: The sensitivity of the CAD system was consistently high for detection of breast cancer on initial and short-term follow-up digital mammograms and reproducibility was significantly higher for true- positive CAD marks than for false-positive CAD marks.
Abstract: Purpose: To retrospectively evaluate the sensitivity and reproducibility of a computer-aided detection (CAD) system applied to serial digital mammograms obtained in women with breast cancer, with histologic analysis as the reference standard. Materials and Methods: This study was institutional review board approved, and patient informed consent was waived. A commercially available CAD system was applied to initial and follow-up digital mammograms obtained in 93 women with breast cancer (mean age, 52 years; age range, 32–81 years). The mean interval between mammographic examinations was 23 days (range, 7–58 days). There were 119 visible lesion components (70 masses, 49 microcalcifications). Sensitivity, false-positive mark rate, and reproducibility of the CAD system were evaluated for both sets of mammograms with the t test. Results: Sensitivities of the CAD system at initial and follow-up digital mammography were 91% and 89%, respectively, for detection of masses. Sensitivity of the CAD system for detecti...
20 Oct 2008
TL;DR: A module for the segmentation of masses that can be implemented in a complete CADx (Computer Aided Diagnosis) system is proposed and a new version of the region growing algorithm specific for this kind of images is implemented for the constraints on computation time.
Abstract: Breast cancer is the most common cause of death among women and the most effective method for its diagnosis is mammography. However, this kind of analysis is very difficult to interpret and for this reason radiologists miss 20-30% of tumors. We propose a module for the segmentation of masses that can be implemented in a complete CADx (Computer Aided Diagnosis) system. In particular, we implement a new version of the region growing algorithm specific for this kind of images and for the constraints on computation time of this application.
TL;DR: To retrospectively compare the accuracy for cancer diagnosis of digital mammography with soft-copy interpretation with that of screen-film mammography for each digital equipment manufacturer, by using results of biopsy and follow-up as the reference standard.
Abstract: Our retrospective reader study, which was designed to detect differences at least as large as those postulated for the primary Digital Mammographic Imaging Screening Trial (DMIST) study, did not show statistically significant differences between soft-copy digital and film mammography for Fischer, Fuji, and GE digital systems in either the full reader sets or in the subsets of women in whom digital mammography was found to be significantly superior to film mammography in the primary DMIST study.
TL;DR: The results suggest that for either configuration, breast density can be precisely measured with dual energy imaging requiring only a small amount of additional dose to the breast.
Abstract: Breast density, the percentage of glandular breast tissue, has been identified as an important yet underutilized risk factor in the development of breast cancer. A quantitative method to measure breast density with dual energy imaging was investigated using a computer simulation model. Two configurations to measure breast density were evaluated: the usage of monoenergetic beams and an ideal detector, and the usage of polyenergetic beams with spectra from a tungsten anode x-ray tube with a detector modeled after a digital mammography system. The simulation model calculated the mean glandular dose necessary to quantify the variability of breast density to within 1/3%. The breast was modeled as a semicircle 10 cm in radius with equal homogenous thicknesses of adipose and glandular tissues. Breast thicknesses were considered in the range of 2-10 cm and energies in the range of 10-150 keV for the two monoenergetic beams, and 20-150 kVp for spectra with a tungsten anode x-ray tube. For a 4.2 cm breast thickness, the required mean glandular doses were 0.183 microGy for two monoenergetic beams at 19 and 71 keV, and 9.85 microGy for two polyenergetic spectra from a tungsten anode at 32 and 96 kVp with beam filtrations of 50 microm Rh and 300 microm Cu for the low and high energy beams, respectively. The results suggest that for either configuration, breast density can be precisely measured with dual energy imaging requiring only a small amount of additional dose to the breast. The possibility of using a standard screening mammogram as the low energy image is also discussed.
20 Jul 2008
TL;DR: R radiologists with a range of experience demonstrated improved performance using tomosynthesis in combination with digital mammography (3D+2D), as measured using recall rate reduction and AUROC metrics.
Abstract: This study reports the performance of breast tomosynthesis (3D images) combined with digital mammography (2D images), compared to digital mammography alone, as a function of the experience of the radiologist. In this trial, twelve readers analyzed 316 image sets, giving BIRADS (and other) scores first for the digital mammograms, and subsequently for the combined datasets of tomosynthesis and digital mammograms. Clinical performance was measured using two metrics: area under the ROC curve (AUROC) and recall rate, and was analyzed as a function of the experience level. The study found that all radiologists AUROC improved when using 3D+2D compared to 2D, with no correlation with experience level, increasing by 0.077 ± 0.058 (mean ± 1 standard deviation) for the 5 least experienced and increasing by 0.078 ± 0.029 for the 5 most experienced. Similarly, the use of 3D+2D compared to 2D imaging showed a mean decrease in recall rate of 39.2% for the most experienced and a decrease of 39.6% for the least experienced, again with no correlation found with experience level. In summary, radiologists with a range of experience demonstrated improved performance using tomosynthesis in combination with digital mammography (3D+2D), as measured using recall rate reduction and AUROC metrics.
TL;DR: Full-field digital mammography optimizes the lesion-background contrast and gives better sensitivity, and it is possible to see through the dense tissues by altering computer windows; this may be particularly useful in younger women with dense breasts.
Abstract: Breast imaging has made huge advances in the last decade, and along with newer techniques to diagnose primary breast cancer, many novel methods are being used and look promising in detecting distant metastasis, recurrent disease and assessing response to treatment. Full-field digital mammography optimizes the lesion-background contrast and gives better sensitivity, and it is possible to see through the dense tissues by altering computer windows; this may be particularly useful in younger women with dense breasts. The need for repeat imaging is reduced, with the added advantage of reduced radiation dose to patients. Computer-aided detection systems may help the radiologist in interpretation of both conventional and digital mammograms. MRI has a role in screening women at high risk for breast cancer. It also aids in cancer management by assessing response to treatment and can help in deciding appropriate surgery by providing accurate information on the extent of the tumor. Newer diagnostic techniques such as sestamibi scans, optical imaging and molecular diagnostic techniques look promising, but need more investigation into their use. Their roles will appear clearer in coming years, and they may prove to be of help in further investigating lesions that are indeterminate on standard imaging. Other upcoming techniques are contrast-enhanced mammography and tomosynthesis. These may give additional information in indeterminate lesions, and when used in screening they aid in reducing recall rates, as shown in recent studies. PET/computed tomography has a role in detecting local disease recurrence and distant metastasis in breast cancer patients.
TL;DR: Amongst the different noise reduction techniques evaluated in this study, the KNR method was found to be most effective in reducing the image noise and increasing the calcification visibility (or CNR), closely followed by the HE median filter technique.
Abstract: We have previously developed a dual-energy digital mammography (DEDM) technique for calcification imaging under full-field imaging conditions using a commercially available flat-panel based digital mammography system. Although dual-energy (DE) imaging could suppress the obscuration of calcifications by tissue-structure background, it also increases the intrinsic noise in the DE images. Here we report on the effects of three different noise reduction techniques on DE calcification images: a simple smoothing (boxcar) filter applied to the DE image, a median filter applied to the HE image prior to the computation of the DE image and an adaptation of the Kalender's correlated-noise reduction (KNR) technique for DEDM. We compared the different noise reduction techniques by evaluating their effects on DE calcification images of a 5 cm thick breast-tissue-equivalent slab with continuously varying glandular-tissue ratio superimposed with calcium carbonate crystals of various sizes that simulate calcifications. Evaluations of different noise reducing techniques were performed by comparison of the root-mean-square signal in background regions (no calcifications present) of the DE calcification images and the contrast-to-noise ratios (CNR) of the calcifications in the DE calcification images. Amongst the different noise reduction techniques evaluated in this study, the KNR method was found to be most effective in reducing the image noise and increasing the calcification visibility (or CNR), closely followed by the HE median filter technique. Although the simple smoothing (boxcar) filter reduced the noise, it did not improve calcification visibility. The visible calcification threshold size with DEDM over smoothly varying background at screening mammography doses, assuming a CNR threshold of 4, was estimated to be around 250 µm with both the HE median filter and the KNR techniques. The quality of DE images with noise reduction techniques based on phantom studies were verified with DE images of an animal-tissue phantom that consisted of calcifications superimposed over more realistic tissue structures.
TL;DR: Results show that the computerized analysis methods for the diagnosis of breast mass lesions on FFDM are promising, and can potentially be used to aid clinicians in the diagnostic interpretation of FFDM.
20 Jul 2008
TL;DR: Compared to standard digital mammography, stereo mammography significantly reduced false positive lesion detections by 46% (p< 0.0001), and significantly increased true positive lesions by 23% ( p<0.05).
Abstract: We report on a clinical trial comparing stereoscopic full-field digital mammography to standard (non-stereo) full-field digital mammography for detection of true breast lesions in a screening population. Each of 1458 enrolled patients received both a standard screening examination and a stereoscopic screening examination, which were read independently by different radiologists. Compared to standard digital mammography, stereo mammography significantly reduced false positive lesion detections by 46% (p< 0.0001), and significantly increased true positive lesion detections by 23% (p< 0.05).
TL;DR: It is important that the technologist, radiologist, and physicist become familiar with the spectrum of digital mammographic artifacts and pay careful attention to digital quality control procedures to ensure optimal image quality.
Abstract: The recent introduction of digital mammography represents a significant technologic advance in breast imaging. However, many radiologists and technologists are unfamiliar with artifacts that are commonly seen with this modality, and recognizing these artifacts is critical for optimizing image quality. Commonly encountered artifacts include patient-related artifacts (motion artifact, antiperspirant artifact, thin breast artifact), hardware-related artifacts (field inhomogeneity, detector-associated artifacts, collimator misalignment, underexposure, grid lines, grid misplacement, vibration artifact), and software processing artifacts ("breast-within-a-breast" artifact, vertical processing bars, loss of edge, high-density artifacts). Although some of these artifacts are similar to those seen with screen-film mammography, many are unique to digital mammography--specifically, those due to software processing errors or digital detector deficiencies. In addition, digital mammographic artifacts depend on detector technology (direct vs indirect) and therefore can be vendor specific. It is important that the technologist, radiologist, and physicist become familiar with the spectrum of digital mammographic artifacts and pay careful attention to digital quality control procedures to ensure optimal image quality.