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Results for “Digital breast tomosynthesis”

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At least 19 recordsLinked to original sources

PubMed2006

Voting strategy for artifact reduction in digital breast tomosynthesis.

Artifacts are observed in digital breast tomosynthesis (DBT) reconstructions due to the small number of projections and the narrow angular range that are typically employed in tomosynthesis imaging. In this work, we investigate the reconstruction artifacts that are caused by high-attenuation features in breast and develop several artifact reduction methods based on a "voting strategy." The voting strategy identifies the projection(s) that would introduce artifacts to a voxel and rejects the projection(s) when reconstructing the voxel. Four approaches to the voting strategy were compared, including projection segmentation, maximum contribution deduction, one-step classification, and iterative classification. The projection segmentation method, based on segmentation of high-attenuation features from the projections, effectively reduces artifacts caused by metal and large calcifications that can be reliably detected and segmented from projections. The other three methods are based on the observation that contributions from artifact-inducing projections have higher value than those from normal projections. These methods attempt to identify the projection(s) that would cause artifacts by comparing contributions from different projections. Among the three methods, the iterative classification method provides the best artifact reduction; however, it can generate many false positive classifications that degrade the image quality. The maximum contribution deduction method and one-step classification method both reduce artifacts well from small calcifications, although the performance of artifact reduction is slightly better with the one-step classification. The combination of one-step classification and projection segmentation removes artifacts from both large and small calcifications.

Algorithms↗

PubMed2006

Computerized mass detection for digital breast tomosynthesis directly from the projection images.

Digital breast tomosynthesis (DBT) has recently emerged as a new and promising three-dimensional modality in breast imaging. In DBT, the breast volume is reconstructed from 11 projection images, taken at source angles equally spaced over an arc of 50 degrees. Reconstruction algorithms for this modality are not fully optimized yet. Because computerized lesion detection in the reconstructed breast volume will be affected by the reconstruction technique, we are developing a novel mass detection algorithm that operates instead on the set of raw projection images. Mass detection is done in three stages. First, lesion candidates are obtained for each projection image separately, using a mass detection algorithm that was initially developed for screen-film mammography. Second, the locations of a lesion candidate are backprojected into the breast volume. In this feature volume, voxel intensities are a combined measure of detection frequency (e.g., the number of projections in which a given lesion candidate was detected), and a measure of the angular range over which a given lesion was detected. Third, features are extracted after reprojecting the three-dimensional (3-D) locations of lesion candidates into projection images. Features are combined using linear discriminant analysis. The database used to test the algorithm consisted of 21 mass cases (13 malignant, 8 benign) and 15 cases without mass lesions. Based on this database, the algorithm yielded a sensitivity of 90% at 1.5 false positives per breast volume. Algorithm performance is positively biased because this dataset was used for development, training, and testing, and because the number of algorithm parameters was approximately the same as the number.of patient cases. Our results indicate that computerized mass detection in the sequence of projection images for DBT may be effective despite the higher noise level in those images.

Algorithms↗

PubMed2004

Computerized detection of mass lesions in digital breast tomosynthesis images using two- and three dimensional radial gradient index segmentation.

Initial results for a computerized mass lesion detection scheme for digital breast tomosynthesis (DBT) images are presented. The algorithm uses a radial gradient index feature for the initial lesion detection and for segmentation of lesion candidates. A set of features is extracted for each segmented partition. Performance of two- and three dimensional features was compared. For gradient features, the additional dimension provided no improvement in classification performance. For shape features, classification using 3D features was improved compared to the 2D equivalent features. The preliminary overall performance was 76% sensitivity at 11 false positives per exam, estimated based on DBT image data of 21 masses. A larger database will allow for further development and improvement in our computer aided detection scheme.

Breast Neoplasms↗

PubMed2006

Experimental phantom lesion detectability study using a digital breast tomosynthesis prototype system.

PURPOSE: To compare the sensitivity of conventional two-dimensional (2D) projection imaging with tomosynthesis with respect to the detectability of mammographic phantom lesions. MATERIALS AND METHODS: Using a breast tomosynthesis prototype based on a commercial FFDM system (Siemens MAMMOMAT Novation), but modified for a wide angle tube motion and equipped with a fast read-out amorphous selenium detector, we acquired standard 2D images and tomosynthesis series of projection views. We used the Wisconsin mammographic random phantom, model RMI 152A. The anode filter combinations Mo/Mo and W/Rh at two different doses were used as typical radiographic techniques. Slice images through the phantom parallel to the detector were reconstructed with a distance of 1 mm employing a filtered back-projection algorithm. The image data sets were read by five radiologists and evaluated with respect to the detectability of the phantom details. RESULTS: For all studied radiographic techniques, the detection rate in the tomosynthesis mode was 100 %, i. e. 75 true positive findings out of 75 possible hits. In contrast, the conventional projection mode yielded a detection rate between 80 and 93 % (corresponding to 60 and 70 detected details) depending on the dose and X-ray spectrum. CONCLUSION: Tomosynthesis has the potential to increase the sensitivity of digital mammography. Overlapping structures from out-of-plane tissue can be removed in the tomosynthesis reconstruction process, thereby enhancing the diagnostic accuracy.

Algorithms↗

PubMed1998

Digital breast imaging: tomosynthesis and digital subtraction mammography.

Advances in the computer technology and the introduction of new digital imaging detectors offer the potential for digital image acquisition and several new mammography techniques, such as tomosynthesis and digital subtraction mammography. Tomosynthesis is a method of obtaining tomographic images of a breast. In tomosynthesis, any number of tomographic planes may be reconstructed from a set of images obtained as the X-ray source is moved in an arc above the breast. By shifting and adding the information obtained at different source positions, any plane of the breast can be brought into a sharp focus, while structures outside this selected plane are blurred. This may lead to improved lesion detection, especially in dense breast tissue. Thus, tomosynthesis may play a role in improving breast cancer screening and lesion characterization. Digital subtraction mammography is a method of breast angiography. It is performed by obtaining a digital radiographic image before, and one or more digital radiographic images after the injection of a contrast agent such as iodine. The pre- and post-contrast images are subtracted, resulting in an image of the vascular structures in the breast. Because breast cancer lesions have increased vascularity, digital subtraction mammography may play an important role in improving lesion detection, characterizing lesions, monitoring response to therapy, and determining lesion extent.Thus, both of these new digital techniques have the potential to address the major limitation of conventional mammography, namely the difficulty in detecting cancer in radiographically dense breasts.

Journal Article↗

PubMed2005

Computer-aided detection system for breast masses on digital tomosynthesis mammograms: preliminary experience.

The purpose of the study was to design a computer-aided detection (CAD) system for breast mass detection on digital breast tomosynthesis (DBT) mammograms and to perform a preliminary evaluation of the performance of this system. Twenty-six patients were imaged with a prototype DBT system. Institutional review board approval and written informed patient consent were obtained. Use of the data set in this study was HIPAA compliant. The CAD system first screened the three-dimensional volume of the mass candidates by means of gradient-field analysis. Each mass candidate was segmented from the structured background, and its image features were extracted. A feature classifier was designed to differentiate true masses from normal tissues. The CAD system was trained and tested by using a leave-one-case-out method. The classifier calculated a mean area under the test receiver operating characteristic curve of 0.91 +/- 0.03 (standard error of mean). The CAD system achieved a sensitivity of 85%, with 2.2 false-positive objects per case. The results demonstrate the feasibility of the authors' approach to the development of a CAD system for DBT mammography.

Adult↗

PubMed1997

Digital tomosynthesis in breast imaging.

PURPOSE: To describe and evaluate a method of tomosynthesis breast imaging with a full-field digital mammographic system. MATERIALS AND METHODS: In this tomosynthesis method, low-radiation-dose images were acquired as the x-ray source was moved in an arc above the stationary breast and digital detector. A step-and-expose method of imaging was used. Breast tomosynthesis and conventional images of two imaging phantoms and four mastectomy specimens were obtained. Three experienced readers scored the relative lesion visibility, lesion margin visibility, and confidence in the classification of six lesions. RESULTS: Tomosynthesis image-reconstruction algorithms allow tomographic imaging of the entire breast from a single arc of the x-ray source and at a radiation dose comparable with that in single-view mammography. Except for images of a large mass in a fatty breast, the tomosynthesis images were superior to the conventional images. CONCLUSION: Digital mammographic systems make breast tomosynthesis possible. Tomosynthesis may improve the specificity of mammography with improved lesion margin visibility and may improve early breast cancer detection, especially in women with radiographically dense breasts.

Female↗

PubMed2006

A comparative study of limited-angle cone-beam reconstruction methods for breast tomosynthesis.

Digital tomosynthesis mammography (DTM) is a promising new modality for breast cancer detection. In DTM, projection-view images are acquired at a limited number of angles over a limited angular range and the imaged volume is reconstructed from the two-dimensional projections, thus providing three-dimensional structural information of the breast tissue. In this work, we investigated three representative reconstruction methods for this limited-angle cone-beam tomographic problem, including the backprojection (BP) method, the simultaneous algebraic reconstruction technique (SART) and the maximum likelihood method with the convex algorithm (ML-convex). The SART and ML-convex methods were both initialized with BP results to achieve efficient reconstruction. A second generation GE prototype tomosynthesis mammography system with a stationary digital detector was used for image acquisition. Projection-view images were acquired from 21 angles in 3 degrees increments over a +/- 30 degrees angular range. We used an American College of Radiology phantom and designed three additional phantoms to evaluate the image quality and reconstruction artifacts. In addition to visual comparison of the reconstructed images of different phantom sets, we employed the contrast-to-noise ratio (CNR), a line profile of features, an artifact spread function (ASF), a relative noise power spectrum (NPS), and a line object spread function (LOSF) to quantitatively evaluate the reconstruction results. It was found that for the phantoms with homogeneous background, the BP method resulted in less noisy tomosynthesized images and higher CNR values for masses than the SART and ML-convex methods. However, the two iterative methods provided greater contrast enhancement for both masses and calcification, sharper LOSF, and reduced interplane blurring and artifacts with better ASF behaviors for masses. For a contrast-detail phantom with heterogeneous tissue-mimicking background, the BP method had strong blurring artifacts along the x-ray source motion direction that obscured the contrast-detail objects, while the other two methods can remove the superimposed breast structures and significantly improve object conspicuity. With a properly selected relaxation parameter, the SART method with one iteration can provide tomosynthesized images comparable to those obtained from the ML-convex method with seven iterations, when BP results were used as initialization for both methods.

Algorithms↗

PubMed2006

A computer simulation study comparing lesion detection accuracy with digital mammography, breast tomosynthesis, and cone-beam CT breast imaging.

Although conventional mammography is currently the best modality to detect early breast cancer, it is limited in that the recorded image represents the superposition of a three-dimensional (3D) object onto a 2D plane. Recently, two promising approaches for 3D volumetric breast imaging have been proposed, breast tomosynthesis (BT) and CT breast imaging (CTBI). To investigate possible improvements in lesion detection accuracy with either breast tomosynthesis or CT breast imaging as compared to digital mammography (DM), a computer simulation study was conducted using simulated lesions embedded into a structured 3D breast model. The computer simulation realistically modeled x-ray transport through a breast model, as well as the signal and noise propagation through a CsI based flat-panel imager. Polyenergetic x-ray spectra of Mo/Mo 28 kVp for digital mammography, Mo/Rh 28 kVp for BT, and W/Ce 50 kVp for CTBI were modeled. For the CTBI simulation, the intensity of the x-ray spectra for each projection view was determined so as to provide a total average glandular dose of 4 mGy, which is approximately equivalent to that given in conventional two-view screening mammography. The same total dose was modeled for both the DM and BT simulations. Irregular lesions were simulated by using a stochastic growth algorithm providing lesions with an effective diameter of 5 mm. Breast tissue was simulated by generating an ensemble of backgrounds with a power law spectrum, with the composition of 50% fibroglandular and 50% adipose tissue. To evaluate lesion detection accuracy, a receiver operating characteristic (ROC) study was performed with five observers reading an ensemble of images for each case. The average area under the ROC curves (Az) was 0.76 for DM, 0.93 for BT, and 0.94 for CTBI. Results indicated that for the same dose, a 5 mm lesion embedded in a structured breast phantom was detected by the two volumetric breast imaging systems, BT and CTBI, with statistically significant higher confidence than with planar digital mammography, while the difference in lesion detection between BT and CTBI was not statistically significant.

Algorithms↗

PubMed2026

Fibromatosis-Like Metaplastic Triple-Negative Breast Cancer: A Case Report.

Fibromatosis-like metaplastic carcinoma (FLMC) is an extremely rare subtype of metaplastic breast carcinoma that closely resembles desmoid-type fibromatosis histologically, making it one of the most diagnostically challenging breast lesions. In contrast to most triple-negative breast cancers, FLMC follows a relatively indolent clinical course, though local recurrence is well documented, and because so few cases have been reported, no established treatment guidelines exist and the role of chemotherapy remains uncertain. We present the case of a 63-year-old woman recalled from routine screening digital breast tomosynthesis for an irregular, spiculated mass in the right breast, confirmed on biopsy to be FLMC, and treated with breast-conserving surgery and adjuvant radiation therapy without chemotherapy after two medical oncologists gave opposing recommendations regarding systemic treatment. She has remained without evidence of disease at two-year follow-up. This case adds to the limited literature on FLMC, supports surgery and radiation alone as a potentially effective treatment strategy in carefully selected patients, and highlights the importance of recognizing FLMC as a biologically distinct entity that should not be managed the same way as conventional triple-negative breast cancer, though longer-term follow-up is needed given the limited data on treatment outcomes for this rare tumor.

breast conservation↗

PubMed2026

Artificial intelligence-supported double reading in European population breast cancer screening: A systematic review and meta-analysis of prospective programs.

BACKGROUND: Most European population mammography screening programs rely on double reading with arbitration, a model that delivers mortality benefit but is increasingly challenged by radiologist workload, variable specificity, and interval cancers. Artificial intelligence (AI) is being evaluated to support or optimize these established European screening pathways. PURPOSE: To synthesize prospective or program-embedded evaluations of AI conducted within European-style population screening programs and to estimate exploratory program-level absolute risk differences (RDs) per 1000 examinations for cancer detection rate (CDR) and recall. MATERIALS AND METHODS: We performed a prespecified, focused evidence synthesis of three large studies embedded within routine population screening programs operating under European-relevant workflows: MASAI (randomized AI-supported risk triage within a national program), ScreenTrustCAD (prospective paired-reader evaluation with AI as an independent reader in a double-reading framework), and PRAIM (nationwide decision-referral implementation). Outcomes were harmonized as AI-control RDs per 1000 examinations. Random-effects pooling used Hartung-Knapp-Sidik-Jonkman models. For the paired-reader design, sensitivity analyses applied a Kish effective sample-size approach across plausible within-examination correlations (ρ = 0.3-0.8). Positive predictive value (PPV) and workflow/time outcomes were summarized descriptively. RESULTS: Across 597,419 examinations, the pooled CDR RD was +0.9 per 1000 (95% CI -0.0 to +1.8; I2 ≈ 12%), consistent with a modest directional increase with borderline statistical uncertainty. The pooled recall RD was -0.6 per 1000 (95% CI -3.1 to +2.1; I2 ≈ 41-43%), indicating no consistent recall increase across screening programs. Where reported, PPV was higher with AI-supported screening. Efficiency signals included 44.3% fewer total readings in MASAI and shorter reading times for AI-normal examinations in PRAIM; in PRAIM, a program-level safety-net mechanism recovered 204 cancers that would otherwise have been missed. CONCLUSION: In European population screening programs characterized by double reading and arbitration, prospective program-embedded evidence suggests that AI integration may yield a small absolute increase in cancer detection (≈1/1000) without a consistent increase in recall, alongside improved PPV and efficiency signals. These findings suggestAI primarily as a complementary reader within European screening workflows, with implementation requiring explicit quality assurance and monitoring of interval cancers and stage distribution.

Humans↗

PubMed2003

Circular tomosynthesis: potential in imaging of breast and upper cervical spine--preliminary phantom and in vitro study.

Phantom and in vitro studies were performed to evaluate the potential application of digital circular tomosynthesis in imaging of the breast and upper cervical spine. A prototype volumetric x-ray system was used to image a mammographic phantom, a fresh mastectomy specimen, and a head phantom containing the upper cervical spine. Results show that breast tissue visualization is improved by the ability to produce sectional images that blur overlying structures and yield three-dimensional information about calcification clusters. In upper cervical spine imaging, digital circular tomosynthesis effectively blurs overlying jaw and skull structures so that C1 and C2 can be visualized in a standard anteroposterior view.

Cervical Vertebrae↗

PubMed2004

Normalized glandular dose (DgN) coefficients for flat-panel CT breast imaging.

The development of new digital mammography techniques such as dual-energy imaging, tomosynthesis and CT breast imaging will require investigation of optimal camera design parameters and optimal imaging acquisition parameters. In optimizing these acquisition protocols and imaging systems it is important to have knowledge of the radiation dose to the breast. This study presents a methodology for estimating the normalized glandular dose to the uncompressed breast using the geometry proposed for flat-panel CT breast imaging. The simulation uses the GEANT 3 Monte Carlo code to model x-ray transport and absorption within the breast phantom. The Monte Carlo software was validated for breast dosimetry by comparing results of the normalized glandular dose (DgN) values of the compressed breast to those reported in the literature. The normalized glandular dose was then estimated for a range of breast diameters from 10 cm to 18 cm using an uncompressed breast model with a homogeneous composition of adipose and glandular tissue, and for monoenergetic x-rays from 10 keV to 120 keV. These data were fit providing expressions for the normalized glandular dose. Using these expressions for the DgN coefficients and input variables such as the diameter, height and composition of the breast phantom, the mean glandular dose for any spectra can be estimated. A computer program to provide normalized glandular dose values has been made available online. In addition, figures displaying energy deposition maps are presented to better understand the spatial distribution of dose in CT breast imaging.

Body Burden↗

PubMed2000

Digital mammography, sestamibi breast scintigraphy, and positron emission tomography breast imaging.

Digital mammography allows for the separate optimization of image acquisition and display. Through this technology, and the application of image processing and computer aided diagnosis, breast cancer detection and breast lesion diagnosis might be improved. Besides the obvious data storage, retrieval, and transmission advantages that digital mammography will allow, additional advances such as tomosynthesis, dual energy mammography and digital subtraction mammography are in development. The possible future utility of Sestamibi breast scintigraphy and breast imaging with positron emission tomography is also discussed.

Breast↗

PubMed2005

Abnormal mammographic findings with short-interval follow-up recommendation.

An abnormal Breast Imaging and Reporting Data System (BIRADS) category 3 mammogram with a short interval follow-up recommendation is a common finding seen in approximately 40% of women for each decade of screening. Factors associated with category 3 mammograms include mammography examination features, tendencies of the interpreting physician, and features of the country's health care system and the screened population including age, family history, previous biopsies, obesity, and menopausal hormone therapy. Recently, the degree to which a BIRADS category 3 mammographic result provides differential breast cancer risk compared with normal mammographic categories (BIRADS category 1 or 2) has been questioned. The yield of category 3 mammographic results could potentially be increased by more uniform performance of additional imaging workup (additional views and/or ultrasonography). In addition, other strategies to more accurately characterize the risk of breast cancer in women with category 3 mammographic results are under evaluation and including magnetic resonance imaging, computer-aided classification systems, and digital tomosynthesis. Given the potential psychologic impact of abnormal mammographic results, studies attempting to more accurately relate screening mammography findings to breast cancer risk are a priority.

Breast Diseases↗

PubMedDate not supplied

Digital Mammography: An Update.

Mammography remains important for early detection and diagnosis of malignant breast diseases. In recent years, digital imaging technology has been able to address the challenges inherent in film-screen mammography and improve many aspects of care. Promising techniques include needle biopsies guided by digital mammography, computer-assisted diagnosis, tomosynthesis, contrast-enhanced mammography and scintimammography. Although it is unlikely that digital mammography will revolutionize how breast cancer is treated, it continues to enhance and help provide timely and accurate detection and diagnosis.

Journal Article↗

PubMed1998

What's new in mammography.

Early diagnosis of breast cancer plays the leading role in reducing mortality rates and improving the patients' prognosis: mammography is the most sensitive technique currently available for the detection of nonpalpable lesions and therefore the method of choice. However, mammography has some limitations and the technique must be improved with technological devices without affecting image quality. This could be the target to increase diagnostic accuracy. Mammography sensitivity and specificity are now improved with the digital computer assisted technique, teleradiology, digital tomosynthesis or digital angiography--used to study microvascularization--3D imaging or synchrotron light, and laser mammography. Such other technological devices as Mammospot reduce breast thickness and provide better breast compression. Digital mammography can be carried out with film or direct digitization. The advantages of the digital technique are a shorter examination time, less storage space, electronic image recording, with image 'adjustments' made by the radiologist, and especially computerized analysis. The computer aided diagnosis can be defined as the diagnosis made by the radiologist who considers the results of computerized analysis as a 'second opinion'. In this way incidental mistakes made by radiologists, can be corrected by the computer analysis. Computers are a basic element also in teleradiology, which needs immediate and simultaneous admittance to the patient's history and permits radiology optimization in rural areas too. As for tomosynthesis, it permits to study a single slice of the breast without glandular tissue overlapping, which is useful in dense breasts where the diagnosis can be made with a lower X-ray dose. Moreover, this method fits the current mammographic systems easily. 3D imaging is still a work in progress. Synchrotron mammography is used only on surgery specimens, where it exhibits high resolution and contrast, depicting structures and details missed by conventional mammography. Breast DSA allows the study of vessels < 0.20 mm in diameter and of fine microvascular details; it can also demonstrate neoangiogenesis. Laser mammography permits bilateral examinations of the breast in 10-15 mins and is currently used also for breast cancer therapy, although only in animal trials. To conclude, after reviewing new techniques and evaluating the real cost/benefit ratio for each of them, conventional mammography remains the most sensitive tool for breast cancer diagnosis.

Angiography, Digital Subtraction↗

PubMedDate not supplied

What every surgical oncologist should know about digital mammography.

This article reviews the available information on digital mammography for surgeons who care for patients with breast cancer. The limitations of the current film-based technology and why digital mammography promises to improve breast cancer detection and breast lesion diagnosis are described. The basics of digital imaging technology are reviewed, including a description of image contrast and spatial resolution and its variance from currently available clinical digital mammography systems. The results of clinical trials completed to date are reported. An upcoming large screening trial for digital mammography, sponsored by the National Cancer Institute, is described. Future technological developments, including improvements in softcopy display, image processing, computer-aided detection and diagnosis (CADD), tomosynthesis, and digital subtraction mammography (DSM), are briefly discussed.

Breast Neoplasms↗