Search

Although digital breast tomosynthesis has higher sensitivity than digital mammography and at least as high specificity, digital mammography remains the most common method for conducting mammographic screening. At the same time, mammography systems are now delivered "DBT-ready" and can be used for either digital mammography or digital breast tomosynthesis. In this paper, we ask whether it is ethically permissible to use such equipment for digital mammography, given its lower sensitivity. We argue it is not, and that clinics are ethically required to use their DBT-ready equipment to screen with digital breast tomosynthesis whenever this is practically possible. Our argument relies on a comparison between digital breast tomosynthesis and a hypothesized improvement in the image quality of digital mammography. Critical relevance statement: Women may lose out on the benefits of screening with digital breast tomosynthesis when DBT-ready equipment is used to screen with digital mammography; we argue that this practice is ethically problematic. Key Points: Digital breast tomosynthesis finds more cases of breast cancer than digital mammography. Mammography equipment can often be used to screen with both digital breast tomosynthesis and digital mammography. When they can, clinics are ethically required to use existing equipment to screen with digital breast tomosynthesis instead of digital mammography. [ABSTRACT FROM AUTHOR]

The AAPM, through its members, meetings, and its flagship journal Medical Physics, has played an important role in the development and growth of x-ray tomography in the last 50 years. From a spate of early articles in the 1970s characterizing the first commercial computed tomography (CT) scanners through the “slice wars” of the 1990s and 2000s, the history of CT and related techniques such as tomosynthesis can readily be traced through the pages of Medical Physics and the annals of the AAPM and RSNA/AAPM Annual Meetings. In this article, the authors intend to give a brief review of the role of Medical Physics and the AAPM in CT and tomosynthesis imaging over the last few decades. [ABSTRACT FROM AUTHOR]

Digital breast tomosynthesis (DBT) is a 3D breast cancer screening technique that can overcome the limitations of standard 2D digital mammography. However, DBT images often suffer from artifacts stemming from acquisition conditions, a limited angular range, and low radiation doses. These artifacts have the potential to degrade the performance of automated breast tumor classification tools. Notably, most existing automated breast tumor classification methods do not consider the effect of DBT image quality when designing the classification models. In contrast, this paper introduces a novel deep learning-based framework for classifying breast tumors in DBT images. This framework combines global image quality-aware features with tumor texture descriptors. The proposed approach employs a two-branch model: in the top branch, a deep convolutional neural network (CNN) model is trained to extract robust features from the region of interest that includes the tumor. In the bottom branch, a deep learning model named TomoQA is trained to extract global image quality-aware features from input DBT images. The quality-aware features and the tumor descriptors are then combined and fed into a fully-connected layer to classify breast tumors as benign or malignant. The unique advantage of this model is the combination of DBT image quality-aware features with tumor texture descriptors, which helps accurately classify breast tumors as benign or malignant. Experimental results on a publicly available DBT image dataset demonstrate that the proposed framework achieves superior breast tumor classification results, outperforming all existing deep learning-based methods. [ABSTRACT FROM AUTHOR]

Background: The performance of digital breast tomosynthesis (DBT) alone, digital mammography (DM) plus DBT, and synthesized mammography (SM) plus DBT, in comparison to DM in breast cancer screening, remains a topic of ongoing debate. The effectiveness of these modalities in reducing interval cancer rates (ICR) is particularly contentious. Materials and methods: A database of data was searched for articles published until July 2024. Initially, the pooled sensitivity and specificity of DBT (DBT alone, DM/DBT, and SM/DBT) and DM were estimated. Additionally, the sensitivity of breast cancer screening and ICR for DBT alone, DM/DBT, and SM/DBT compared to DM. The characteristics of interval breast cancer were compared with those screening BC, alongside differences across various screening methods. Results: Eleven studies comparing DBT and DM were included. The sensitivity of DBT was higher than that of DM, with rates of 86% (95%CI: 81, 90) and 80% (95%CI: 76, 84), respectively. The specificities of both modalities were similar, recorded at 96% (95%CI: 95, 98) and 96% (95%CI: 95, 97), respectively. In comparison to DM, the screening sensitivities of DBT, DM/DBT, and SM/DBT were increased by 4.33% (95% CI: 1.52, 7.13), 6.29% (95% CI: 2.55, 10.03), and 5.22% (95% CI: 1.35, 9.10), respectively; however, the difference in the ICR was not statistically significant. Conclusion: DBT offers advantages in enhancing the sensitivity of breast cancer screening; however, its impact on ICR remains uncertain. Consequently, further research is necessary to comprehensively evaluate both the effectiveness of screening and the potential risks associated with DBT. [ABSTRACT FROM AUTHOR]

In this paper, we developed a deep convolutional neural network (CNN) for the classification of malignant and benign masses in digital breast tomosynthesis (DBT) using a multi-stage transfer learning approach that utilized data from similar auxiliary domains for intermediate-stage fine-tuning. Breast imaging data from DBT, digitized screen-film mammography, and digital mammography totaling 4039 unique regions of interest (1797 malignant and 2242 benign) were collected. Using cross validation, we selected the best transfer network from six transfer networks by varying the level up to which the convolutional layers were frozen. In a single-stage transfer learning approach, knowledge from CNN trained on the ImageNet data was fine-tuned directly with the DBT data. In a multi-stage transfer learning approach, knowledge learned from ImageNet was first fine-tuned with the mammography data and then fine-tuned with the DBT data. Two transfer networks were compared for the second-stage transfer learning by freezing most of the CNN structures versus freezing only the first convolutional layer. We studied the dependence of the classification performance on training sample size for various transfer learning and fine-tuning schemes by varying the training data from 1% to 100% of the available sets. The area under the receiver operating characteristic curve (AUC) was used as a performance measure. The view-based AUC on the test set for single-stage transfer learning was 0.85 ± 0.05 and improved significantly (${p} < 0.05$) to 0.91 ± 0.03 for multi-stage learning. This paper demonstrated that, when the training sample size from the target domain is limited, an additional stage of transfer learning using data from a similar auxiliary domain is advantageous. [ABSTRACT FROM AUTHOR]

This paper describes a new image reconstruction method for digital breast tomosynthesis (DBT). The new method incorporates detector blur into the forward model. The detector blur in DBT causes correlation in the measurement noise. By making a few approximations that are reasonable for breast imaging, we formulated a regularized quadratic optimization problem with a data-fit term that incorporates models for detector blur and correlated noise (DBCN). We derived a computationally efficient separable quadratic surrogate (SQS) algorithm to solve the optimization problem that has a non-diagonal noise covariance matrix. We evaluated the SQS-DBCN method by reconstructing DBT scans of breast phantoms and human subjects. The contrast-to-noise ratio and sharpness of microcalcifications were analyzed and compared with those by the simultaneous algebraic reconstruction technique. The quality of soft tissue lesions and parenchymal patterns was examined. The results demonstrate the potential to improve the image quality of reconstructed DBT images by incorporating the system physics model. This paper is a first step toward model-based iterative reconstruction for DBT. [ABSTRACT FROM AUTHOR]

One of the most frequently detected neoplasms in women in Italy is breast cancer, for which high-sensitivity diagnostic techniques are essential for early diagnosis in order to minimize mortality rates. As addressed in Part I of this work, we have seen how conditions such as high glandular density or limitations related to mammographic sensitivity have driven the optimization of technology and the use of increasingly advanced and specific diagnostic methodologies. While the first part focused on analyzing the use of a mammography machine from a physical and dosimetric perspective, in this paper, we will examine other techniques commonly used in breast imaging: contrast-enhanced mammography, digital breast tomosynthesis, radio imaging, and include some notes on image processing. We will also explore the differences between these various techniques to provide a comprehensive overview of breast lesion detection techniques. We will examine the strengths and weaknesses of different diagnostic modalities and observe how, with the implementation of improvements over time, increasingly effective diagnoses can be achieved. [ABSTRACT FROM AUTHOR]

Limited-angle tomography is a highly ill-posed linear inverse problem. It arises in many applications, such as digital breast tomosynthesis. Reconstructions from limited-angle data typically suffer from severe stretching of features along the central direction of projections, leading to poor separation between slices perpendicular to the central direction. In this paper, a new method is introduced, based on machine learning and geometry, producing an estimate for interfaces between regions of different X-ray attenuation. The estimate can be presented on top of the reconstruction, indicating more reliably the separation between features. The method uses directional edge detection, implemented using complex wavelets and enhanced with morphological operations. By using convolutional neural networks, the visible part of the singular support is first extracted and then extended to the full domain, filling in the parts of the singular support that would otherwise be hidden due to the lack of measurement directions. [ABSTRACT FROM AUTHOR]

Background: The Tomosynthesis Mammography Imaging Screening Trial (TMIST), EA1151 conducted by the Eastern Cooperative Oncology Group (ECOG)/American College of Radiology Imaging Network (ACRIN) is a randomized clinical trial designed to assess the effectiveness for breast cancer screening of digital breast tomosynthesis (TM) compared to digital mammography (DM). Equipment from multiple vendors is being used in the study. Purpose: For the findings of the study to be valid and capture the true capacities of the two technology types, it is important that all equipment is operated within appropriate parameters with regard to image quality and dose. A harmonized QC program was established by a core physics team. Since there are over 120 trial sites, a centralized, automated QC program was chosen as the most practical design. This report presents results of the weekly QC testing program. A companion paper will review quality monitoring based on data from the headers of the patient images. Methods: Study images are collected centrally after de‐identification using the "TRIAD" application developed by ACR. The core physics team devised and implemented a minimal set of quality control (QC) tests to evaluate the tomosynthesis and 2D mammography systems. Weekly, monthly and annual testing is performed by the site mammography technologists with images submitted directly to the physics core. The weekly physics QC tests are described: SDNR of a low‐contrast mass object, artifact spread, spatial resolution, tracking of technical factors, and in‐slice noise power spectra. Results: As of December 31, 2022 (5 years), 145 sites with 411 machines had submitted QC data. A total of 136 742 TMIST participant screening imaging studies had been performed. The 5th and 95th percentile mean glandular doses for a single tomosynthesis exposure to a 4.0 cm thick PMMA phantom ("standard breast phantom") were 1.24 and 1.68 mGy respectively. The largest sources of QC non‐conformance were: operator error, not following the QC protocol exactly, unreported software updates and preventive maintenance activities that affected QC setpoints. Noise power spectra were measured, however, standardization of performance targets across machine types and software revisions was difficult. Nevertheless, for each machine type, test measurement results were very consistent when the protocol was followed. Deviations in test results were mostly related to software and hardware changes. Conclusion: Most systems performed very consistently. Although this is a harmonized program using identical phantoms and testing protocols, it is not appropriate to apply universal threshold or target metrics across the machine types because the systems have different non‐linear reconstruction algorithms and image display filters. It was found to be more useful to assess pass/fail criteria in terms of relative deviations from baseline values established when a system is first characterized and after equipment is changed. Generally, systems which needed repair failed suddenly, but in retrospect, for a few cases, drops in SDNR and increases in mAs were observed prior to tube failure. TMIST is registered as NCT03233191 by Clinicaltrials.gov [ABSTRACT FROM AUTHOR]

Abstract: This paper describes Development of a Phantom for Tomosynthesis with Potential for Automated Analysis via the Cloud. Several studies are underway to investigate the effectiveness of Tomosynthesis Mammographic Image Screening, including the large TMIST project as funded by the National Cancer Institute https://www.cancer.gov/about-cancer/treatment/clinical-trials/nci-supported/tmist. The development of the phantom described in this paper follows initiatives from the FDA, the AAPM TG245 task group, and European Reference Organization (EUREF) for Quality Assured Breast Screening and Diagnostic Services Committee report noting, that no formal endorsement nor recommendation for use has been sought, or granted by any of these groups. This paper reports on the possibility of using this newly developed Tomosynthesis Phantom for Quality Assurance, field testing of image performance, including remote monitoring of DBT system performance, e.g., via transmission over the cloud. The phantom includes tests for: phantom positioning and alignment (important for remote analysis), scan geometry (x and y), chest wall offset, scan slice width and Slice Sensitivity Profile (SSP(z)) slice geometry (slice width), scan slice incrementation (z), z axis geometry bead, low contrast detectability using low contrast spheres, spatial resolution via Point Spread Function (PSF), Image uniformity, Signal to Noise Ratio (SNR), and Contrast to Noise Ratio (CNR) via readings over an Aluminum square. The phantom is designed for use with automated analysis via transmission of images over the cloud and the analysis package includes test of positioning accuracy (roll, pitch, and yaw). Data are shown from several commercial Tomosynthesis Scanners including Fuji, GE, Hologic, IMS‐Giotti, and Siemens; however, the focus of this paper is on phantom design, and not in general aimed at direct commercial comparisons, and wherever possible the identity of the data is anonymized. Results of automated analysis of the phantom are shown, and it is demonstrated that reliable analysis of such a phantom can be achieved remotely, including transmission of data through the cloud. [ABSTRACT FROM AUTHOR]

For example, we have learned that you should not create admixtures of obvious and obscure lesions, such that the obvious lesions are always detectable, and the obscure lesions are never detected. The 4 × 4 × 3 mm SP 3 sp elliptical lesion was specifically added to demonstrate that lesion shape and size influenced detectability, and hence highlighted the need for calibration. To the Editor: We thank Dr. Glick for his letter.[1] Dr. Glick raises several important questions related to our recently published paper.[2] We can address the point regarding the selection of lesion model I trivially. [Extracted from the article]

Background: Recent advancements in computing power and state-of-the-art algorithms have helped in more accessible and accurate diagnosis of numerous diseases. In addition, the development of de novo areas in imaging science, such as radiomics and radiogenomics, have been adding more to personalize healthcare to stratify patients better. These techniques associate imaging phenotypes with the related disease genes. Various imaging modalities have been used for years to diagnose breast cancer. Nonetheless, digital breast tomosynthesis (DBT), a state-of-the-art technique, has produced promising results comparatively. DBT, a 3D mammography, is replacing conventional 2D mammography rapidly. This technological advancement is key to AI algorithms for accurately interpreting medical images. Objective and methods: This paper presents a comprehensive review of deep learning (DL), radiomics and radiogenomics in breast image analysis. This review focuses on DBT, its extracted synthetic mammography (SM), and full-field digital mammography (FFDM). Furthermore, this survey provides systematic knowledge about DL, radiomics, and radiogenomics for beginners and advanced-level researchers. Results: A total of 500 articles were identified, with 30 studies included as the set criteria. Parallel benchmarking of radiomics, radiogenomics, and DL models applied to the DBT images could allow clinicians and researchers alike to have greater awareness as they consider clinical deployment or development of new models. This review provides a comprehensive guide to understanding the current state of early breast cancer detection using DBT images. Conclusion: Using this survey, investigators with various backgrounds can easily seek interdisciplinary science and new DL, radiomics, and radiogenomics directions towards DBT. [ABSTRACT FROM AUTHOR]

In breast tomosynthesis, multiple low-dose projections are acquired in a single scanning direction over a limited angular range to produce cross-sectional planes through the breast for three-dimensional imaging interpretation. We built a next-generation tomosynthesis system capable of multidirectional source motion with the intent to customize scanning motions around "suspicious findings". Customized acquisitions can improve the image quality in areas that require increased scrutiny, such as breast cancers, architectural distortions, and dense clusters. In this paper, virtual clinical trial techniques were used to analyze whether a finding or area at high risk of masking cancers can be detected in a single low-dose projection and thus be used for motion planning. This represents a step towards customizing the subsequent low-dose projection acquisitions autonomously, guided by the first low-dose projection; we call this technique "self-steering tomosynthesis." A U-Net was used to classify the low-dose projections into "risk classes" in simulated breasts with soft-tissue lesions; class probabilities were modified using post hoc Dirichlet calibration (DC). DC improved the multiclass segmentation (Dice = 0.43 vs. 0.28 before DC) and significantly reduced false positives (FPs) from the class of the highest risk of masking (sensitivity = 81.3% at 2 FPs per image vs. 76.0%). This simulation-based study demonstrated the feasibility of identifying suspicious areas using a single low-dose projection for self-steering tomosynthesis. [ABSTRACT FROM AUTHOR]

Purpose: The advent of three‐dimensional breast imaging systems such as digital breast tomosynthesis (DBT) has great promise for improving the detection and diagnosis of breast cancer. With these new technologies comes an essential need for testing methods to assess the resultant image quality. Although randomized clinical trials are the gold standard for assessing image quality, phantom‐based studies can provide a simpler and less burdensome approach. In this work, a complete framework is presented for task‐based evaluation of microcalcification (MCs) detection performance for DBT imaging systems. Methods: The framework consists of three parts. The first part is a realistic anthropomorphic physical breast phantom created through inkjet printing, with parchment paper and iodine‐doped ink. The second is a method for inserting realistic MCs fabricated from calcium hydroxyapatite. The reproducibility and stability of the phantom materials were investigated through multiple samples of parchment and ink over 6 months. The final part is an analysis using a four‐alternative forced choice (4AFC) reader study. To demonstrate the framework, a task‐based 4AFC study was conducted using a clinical system to compare performance from DBT, synthetic mammography (SM), and full‐field digital mammography (FFDM). Nine human observers read images containing MC clusters imaged with all three modalities and tried to correctly locate the MCs. The proportion correct (PC) was measured as the number of correctly detected clusters out of all trials. Results: Overall, readers scored the highest with FFDM, (PC = 0.95 ± 0.03) then DBT (0.85 ± 0.04), and finally SM (0.44 ± 0.06). For the parchment and ink samples, the linear attenuation properties were very stable over 6 months. In addition, little difference was found between the various parchment and ink samples, indicating good reproducibility. Conclusions: This framework presents a promising methodology for evaluating diagnostic task performance of clinical breast DBT systems. [ABSTRACT FROM AUTHOR]

Objectives: It is uncertain what the effects of introducing digital breast tomosynthesis (DBT) in the Dutch breast cancer screening programme would be on detection, recall, and interval cancers (ICs), while reading times are expected to increase. Therefore, an investigation into the efficiency and cost-effectiveness of DBT screening while optimising reading is required.The Screening Tomosynthesis trial with advanced REAding Methods (STREAM) aims to include 17,275 women (age 50–72 years) eligible for breast cancer screening in the Netherlands for two biennial DBT screening rounds to determine the short-, medium-, and long-term effects and acceptability of DBT screening and identify an optimised strategy for reading DBT. The control group will consist of 86,400 women selected from the database of the Dutch breast cancer screening programme screened with digital mammography. The intervention group will undergo DBT examinations only. Four different reading strategies will be evaluated on a subset of first-round screening exams. These four strategies will also be evaluated combined with replacing one of the two readers with AI predictions. The Microsimulation Screening Analysis (MISCAN)-Breast model will be used to estimate the long-term outcomes of DBT screening assuming the best-performing reading method.The primary outcome measure is the IC and advanced cancer rate at the second round (combined endpoint) in the DBT group compared to the control group. Secondary outcome measures are participation, recall and detection rates, positive predictive value, acceptability, reading method with the best case-based area under the curve and reading time, predicted breast cancer mortality, number of cancers overdiagnosed, and cost-effectiveness.Question The short-, medium-, and long-term effects of digital breast tomosynthesis (DBT) imaging in the Dutch breast cancer screening programme are unknown, but essential to decide about implementation.Findings This protocol paper describes the primary endpoint of the STREAM trial: the combined interval and advanced cancer detection rate at the second DBT round.Clinical relevance The STREAM trial is a prospective, non-randomised, population-based study in the Dutch breast cancer screening programme, that aims to evaluate the effects and acceptability of two rounds of DBT screening to determine if DBT can enhance the programme’s outcomes.Materials and methods: It is uncertain what the effects of introducing digital breast tomosynthesis (DBT) in the Dutch breast cancer screening programme would be on detection, recall, and interval cancers (ICs), while reading times are expected to increase. Therefore, an investigation into the efficiency and cost-effectiveness of DBT screening while optimising reading is required.The Screening Tomosynthesis trial with advanced REAding Methods (STREAM) aims to include 17,275 women (age 50–72 years) eligible for breast cancer screening in the Netherlands for two biennial DBT screening rounds to determine the short-, medium-, and long-term effects and acceptability of DBT screening and identify an optimised strategy for reading DBT. The control group will consist of 86,400 women selected from the database of the Dutch breast cancer screening programme screened with digital mammography. The intervention group will undergo DBT examinations only. Four different reading strategies will be evaluated on a subset of first-round screening exams. These four strategies will also be evaluated combined with replacing one of the two readers with AI predictions. The Microsimulation Screening Analysis (MISCAN)-Breast model will be used to estimate the long-term outcomes of DBT screening assuming the best-performing reading method.The primary outcome measure is the IC and advanced cancer rate at the second round (combined endpoint) in the DBT group compared to the control group. Secondary outcome measures are participation, recall and detection rates, positive predictive value, acceptability, reading method with the best case-based area under the curve and reading time, predicted breast cancer mortality, number of cancers overdiagnosed, and cost-effectiveness.Question The short-, medium-, and long-term effects of digital breast tomosynthesis (DBT) imaging in the Dutch breast cancer screening programme are unknown, but essential to decide about implementation.Findings This protocol paper describes the primary endpoint of the STREAM trial: the combined interval and advanced cancer detection rate at the second DBT round.Clinical relevance The STREAM trial is a prospective, non-randomised, population-based study in the Dutch breast cancer screening programme, that aims to evaluate the effects and acceptability of two rounds of DBT screening to determine if DBT can enhance the programme’s outcomes.Results: It is uncertain what the effects of introducing digital breast tomosynthesis (DBT) in the Dutch breast cancer screening programme would be on detection, recall, and interval cancers (ICs), while reading times are expected to increase. Therefore, an investigation into the efficiency and cost-effectiveness of DBT screening while optimising reading is required.The Screening Tomosynthesis trial with advanced REAding Methods (STREAM) aims to include 17,275 women (age 50–72 years) eligible for breast cancer screening in the Netherlands for two biennial DBT screening rounds to determine the short-, medium-, and long-term effects and acceptability of DBT screening and identify an optimised strategy for reading DBT. The control group will consist of 86,400 women selected from the database of the Dutch breast cancer screening programme screened with digital mammography. The intervention group will undergo DBT examinations only. Four different reading strategies will be evaluated on a subset of first-round screening exams. These four strategies will also be evaluated combined with replacing one of the two readers with AI predictions. The Microsimulation Screening Analysis (MISCAN)-Breast model will be used to estimate the long-term outcomes of DBT screening assuming the best-performing reading method.The primary outcome measure is the IC and advanced cancer rate at the second round (combined endpoint) in the DBT group compared to the control group. Secondary outcome measures are participation, recall and detection rates, positive predictive value, acceptability, reading method with the best case-based area under the curve and reading time, predicted breast cancer mortality, number of cancers overdiagnosed, and cost-effectiveness.Question The short-, medium-, and long-term effects of digital breast tomosynthesis (DBT) imaging in the Dutch breast cancer screening programme are unknown, but essential to decide about implementation.Findings This protocol paper describes the primary endpoint of the STREAM trial: the combined interval and advanced cancer detection rate at the second DBT round.Clinical relevance The STREAM trial is a prospective, non-randomised, population-based study in the Dutch breast cancer screening programme, that aims to evaluate the effects and acceptability of two rounds of DBT screening to determine if DBT can enhance the programme’s outcomes.Key Points: It is uncertain what the effects of introducing digital breast tomosynthesis (DBT) in the Dutch breast cancer screening programme would be on detection, recall, and interval cancers (ICs), while reading times are expected to increase. Therefore, an investigation into the efficiency and cost-effectiveness of DBT screening while optimising reading is required.The Screening Tomosynthesis trial with advanced REAding Methods (STREAM) aims to include 17,275 women (age 50–72 years) eligible for breast cancer screening in the Netherlands for two biennial DBT screening rounds to determine the short-, medium-, and long-term effects and acceptability of DBT screening and identify an optimised strategy for reading DBT. The control group will consist of 86,400 women selected from the database of the Dutch breast cancer screening programme screened with digital mammography. The intervention group will undergo DBT examinations only. Four different reading strategies will be evaluated on a subset of first-round screening exams. These four strategies will also be evaluated combined with replacing one of the two readers with AI predictions. The Microsimulation Screening Analysis (MISCAN)-Breast model will be used to estimate the long-term outcomes of DBT screening assuming the best-performing reading method.The primary outcome measure is the IC and advanced cancer rate at the second round (combined endpoint) in the DBT group compared to the control group. Secondary outcome measures are participation, recall and detection rates, positive predictive value, acceptability, reading method with the best case-based area under the curve and reading time, predicted breast cancer mortality, number of cancers overdiagnosed, and cost-effectiveness.Question The short-, medium-, and long-term effects of digital breast tomosynthesis (DBT) imaging in the Dutch breast cancer screening programme are unknown, but essential to decide about implementation.Findings This protocol paper describes the primary endpoint of the STREAM trial: the combined interval and advanced cancer detection rate at the second DBT round.Clinical relevance The STREAM trial is a prospective, non-randomised, population-based study in the Dutch breast cancer screening programme, that aims to evaluate the effects and acceptability of two rounds of DBT screening to determine if DBT can enhance the programme’s outcomes.Graphical Abstract: It is uncertain what the effects of introducing digital breast tomosynthesis (DBT) in the Dutch breast cancer screening programme would be on detection, recall, and interval cancers (ICs), while reading times are expected to increase. Therefore, an investigation into the efficiency and cost-effectiveness of DBT screening while optimising reading is required.The Screening Tomosynthesis trial with advanced REAding Methods (STREAM) aims to include 17,275 women (age 50–72 years) eligible for breast cancer screening in the Netherlands for two biennial DBT screening rounds to determine the short-, medium-, and long-term effects and acceptability of DBT screening and identify an optimised strategy for reading DBT. The control group will consist of 86,400 women selected from the database of the Dutch breast cancer screening programme screened with digital mammography. The intervention group will undergo DBT examinations only. Four different reading strategies will be evaluated on a subset of first-round screening exams. These four strategies will also be evaluated combined with replacing one of the two readers with AI predictions. The Microsimulation Screening Analysis (MISCAN)-Breast model will be used to estimate the long-term outcomes of DBT screening assuming the best-performing reading method.The primary outcome measure is the IC and advanced cancer rate at the second round (combined endpoint) in the DBT group compared to the control group. Secondary outcome measures are participation, recall and detection rates, positive predictive value, acceptability, reading method with the best case-based area under the curve and reading time, predicted breast cancer mortality, number of cancers overdiagnosed, and cost-effectiveness.Question The short-, medium-, and long-term effects of digital breast tomosynthesis (DBT) imaging in the Dutch breast cancer screening programme are unknown, but essential to decide about implementation.Findings This protocol paper describes the primary endpoint of the STREAM trial: the combined interval and advanced cancer detection rate at the second DBT round.Clinical relevance The STREAM trial is a prospective, non-randomised, population-based study in the Dutch breast cancer screening programme, that aims to evaluate the effects and acceptability of two rounds of DBT screening to determine if DBT can enhance the programme’s outcomes. [ABSTRACT FROM AUTHOR]

Purpose: Digital breast tomosynthesis (DBT) is a limited‐angle tomographic breast imaging modality that can be used for breast cancer screening in conjunction with full‐field digital mammography (FFDM) or synthetic mammography (SM). Currently, there are five commercial DBT systems that have been approved by the U.S. FDA for breast cancer screening, all varying greatly in design and imaging protocol. Because the systems are different in technical specifications, there is a need for a quantitative approach for assessing them. In this study, the DBT systems are assessed using a novel methodology with an inkjet‐printed anthropomorphic phantom and four alternative forced choice (4AFC) study scheme. Method: A breast phantom was fabricated using inkjet printing and parchment paper. The phantom contained 5‐mm spiculated masses fabricated with potassium iodide (KI)‐doped ink and microcalcifications (MCs) made with calcium hydroxyapatite. Images of the phantom were acquired on all five systems with DBT, FFDM, and SM modalities where available using beam settings under automatic exposure control. A 4AFC study was conducted to assess reader performance with each signal under each modality. Statistical analysis was performed on the data to determine proportion correct (PC), standard deviations, and levels of significance. Results: For masses, overall detection was highest with DBT. The difference in PC was statistically significant between DBT and SM for most systems. A relationship was observed between increasing PC and greater gantry span. For MCs, performance was highest with DBT and FFDM compared to SM. The difference between PC of DBT and PC of SM was statistically significant for all manufacturers. Conclusions: This methodology represents a novel approach for evaluating systems. This study is the first of its kind to use an inkjet‐printed anthropomorphic phantom with realistic signals to assess performance of clinical DBT imaging systems. [ABSTRACT FROM AUTHOR]

Purpose: With the advent of three‐dimensional (3D) breast imaging modalities such as digital breast tomosynthesis (DBT) and dedicated breast CT (bCT), research into new anthropomorphic breast phantoms has accelerated. These breast phantoms are important for the optimization of new breast imaging systems, assessing new regulatory submissions to prove safety and effectiveness, and for developing new approaches to acceptance and constancy testing of 3D breast imaging systems. This paper provides a review of current research investigating both digital and physical breast phantom development for use in x‐ray based imaging. Methods: Two approaches for designing anthropomorphic, digital breast phantoms are discussed, procedural model‐based phantom generation, where breast features are expressed using mathematical models, and patient‐based generation, where breast structures from tissue specimens or patient‐based breast MR or CT volumes are segmented. Following this discussion, a review of physical anthropomorphic phantoms is given, with emphasis on the advantages and disadvantages present with each approach. Conclusions: This paper provides a summary of the state‐of‐the‐art in anthropomorphic breast phantom development for x‐ray breast imaging. The primary advantage of model‐based digital phantoms is that an unlimited number of phantoms with varying size, shape, and density can be generated. Current research on model‐based breast phantoms is producing more and more realistic breast models; however, they probably are not yet able to pass the so‐called "fool the radiologist" visualization test. Empirical patient‐based breast phantoms are typically based on clinical breast CT data and look more realistic. However, clinical breast CT images have limited spatial resolution and thus do not always portray the finer details in the breast. A number of innovative solutions have been proposed for fabricating physical anthropomorphic breast phantoms based on digital phantom models; however, a number of challenges remain, including realistic modeling of x‐ray attenuation properties and accurately representing high‐frequency structures within breast. [ABSTRACT FROM AUTHOR]

Digital tomosynthesis (DTS) is an X-ray imaging technique that produces cross-sectional images with a scanning motion in narrow angular ranges. This method is promising for the inspection of internal defects in thin slab objects, such as printed circuit boards. However, this limited angular scan approach can have limited resolution in the depth direction. In addition, it can cause ghosting artifacts that originate from other planes in the reconstructed plane. In this paper, we characterized the imaging performance of a DTS method that uses the filtered backprojection for reconstruction. Various imaging parameters were examined, such as the total scan angle and the number of projections used for the reconstruction. We analyzed the signal and noise characteristics of the reconstructed images obtained for a thin tungsten wire and a thin aluminum disc. We obtained in-plane and in-depth modulation transfer functions (MTFs) from the reconstructed wire response images. From the reconstructed disc response images, we calculated the signal difference between the disc region and the adjacent background region along the depth direction, which is called the artifact spread function (ASF). A narrow angular scan enhances the in-plane MTF performance compared to that obtained from the conventional computed tomography, but it degrades the in-depth MTF performance (i.e., it decreases the depth resolution). In addition, the narrow angular scan degrades the ASF performance and causes severe out-of-plane blur artifacts. The optimal scanning angular range is, therefore, required to obtain tomographic images with high spatial resolution and less blur artifacts. The analysis method performed in this paper could be useful for this optimization. [ABSTRACT FROM PUBLISHER]

We are developing a dedicated, combined breast positron emission tomography (PET)-tomosynthesis scanner. Both the PET and digital breast tomosynthesis (DBT) scanners are integrated in a single gantry to provide spatially co-registered 3D PET-tomosynthesis images. The DBT image will be used to identify the breast boundary and breast density to improve the quantitative accuracy of the PET image. This paper explores PET attenuation correction (AC) strategies that can be performed with the combined breast PET-DBT scanner to obtain more accurate, quantitative high-resolution 3D PET images. The PET detector is comprised of a 32 × 32 array of 1.5 × 1.5 × 15 mm3 LYSO crystals. The PET scanner utilizes two detector heads separated by either 9 or 11 cm, with each detector head having a 4 × 2 arrangement of PET detectors. GEANT4 Application for Tomographic Emission simulations were performed using an anthropomorphic breast phantom with heterogeneous attenuation under clinical DBT-compression. FDG-avid lesions, each 5 mm in diameter with 8:1 uptake, were simulated at four locations within the breast. Simulations were performed with a scan time of 2 min. PET AC was performed using the actual breast simulation model as well as DBT reconstructed volumetric images to derive the breast outline. In addition to using the known breast density as defined by the breast model, we also modeled it as uniform patient-independent soft-tissue, and as a uniform patient-specific material derived from breast tissue composition. Measured absolute lesion uptake was used to evaluate the quantitative accuracy of performing AC using the various strategies. This study demonstrates that AC is necessary to obtain a closer estimate of the true lesion uptake and background activity in the breast. The DBT image dataset assists in measuring lesion uptake with low bias by facilitating accurate breast delineation as well as providing accurate information related to the breast tissue composition. While both the uniform soft-tissue and patient-specific material approaches provides a close estimate to the ground truth, <5% bias can be achieved by using a uniform patient-specific material to define the attenuation map. [ABSTRACT FROM AUTHOR]

A look back at Skeletal Radiology in 2016 reveals a sizable number of publications that significantly advanced the state of knowledge about diseases of the musculoskeletal system. This review summarizes the content of some of the most intriguing papers of the year. [ABSTRACT FROM AUTHOR]

While an accurate image reconstruction of digital breast tomosynthesis (DBT) is fundamentally impossible due to its limited data, the DBT is increasingly used in clinics for its rich image information at a relatively low dose. One of the dominant image artifacts in DBT that hinders a faithful diagnosis is high-density object artifact in conjunction with a limited angle problem. In this paper, we developed a very efficient method for reconstructing DBT images with much reduced high-density object artifacts. The method is based on backprojection filtration reconstruction algorithm, voting strategy, and image blending. Data derivatives were backprojected with appropriate weights to reduce ripple artifacts by use of the voting strategy. We generated another differentiated backprojection volume, where the edges of high-density objects are replaced by the background. After Hilbert transform, we blended the two images to reduce undershoot artifacts. Physical phantoms were scanned and we compared conventional filtered backprojection, filtered backprojection with weighted backprojection, and our proposed method. Ripple artifacts were dramatically suppressed and undershoot artifacts were also greatly suppressed in the proposed method. [ABSTRACT FROM AUTHOR]

This document is a correction to an article titled "Scatter radiation intensities in horizontal and vertical planes about digital breast tomosynthesis system" published in the journal Radiation Protection Dosimetry. The authors have identified a minor error in the calculated thicknesses provided in the article. The corrected thicknesses should be 45.6 mm and 40.3 mm, with a difference of 5.3 mm. The correction does not change the overall message of the paper. [Extracted from the article]

Active matrix, flat-panel imagers (AMFPIs) suffer from decreased detective quantum efficiency under conditions of low dose per image frame (such as for digital breast tomosynthesis, fluoroscopy and cone-beam CT) due to low signal compared to the additive electronic noise. One way to address this challenge is to introduce a high-gain x-ray converter called particle-in-binder mercuric iodide (PIB HgI2) which exhibits 3–10 times higher x-ray sensitivity compared to that of a-Se and CsI:Tl converters employed in commercial AMFPI systems. However, a remaining challenge for practical implementation of PIB HgI2 is the high level of image lag, which is believed to largely originate from the trapping of holes. Towards addressing this challenge, this paper reports a theoretical investigation of the use of a Frisch grid structure embedded in the converter to suppress hole signal—which would be expected to reduce image lag. The grid acts as a third electrode sandwiched between a continuous top electrode and pixelated bottom electrodes having a 100 μm pitch. Signal properties of such a detector are investigated as a function of VDR (the ratio of the voltage difference between the electrodes in the region below the grid to that above the grid), grid pitch (the center-to-center distance between two neighboring grid wires) and RGRID (the ratio of grid wire width to grid pitch) for mammographic x-ray energies. The results show that smaller grid pitch suppresses hole signal to a higher degree (up to ∼96%) while a larger gap between grid wires and higher VDR provide minimally impeded electron transport. Examination of the tradeoff between maximizing electron signal and minimizing hole signal indicates that a grid design having a grid pitch of 20 μm with RGRID of 50% and 65% provides hole signal suppression of ∼93% and ∼95% for VDR of 1 and 3, respectively. [ABSTRACT FROM AUTHOR]

Purpose: This paper studies the abilities of a twin‐robotic x‐ray slot‐scanning system for orthopedic imaging to reduce dose by scatter rejection compared to conventional digital radiography. Methods: We investigate the dose saving capabilities, especially in terms of the signal‐ and the contrast‐to‐noise ratio, as well as the scatter‐to‐primary ratio of the proposed slot‐scanning method in comparison to the state‐of‐the‐art method for length‐extended imaging. As a baseline, we use x‐ray parameters of two clinically established acquisition protocols that provide the same detector entrance dose but are profoundly different in patient dose. To obtain an estimate of the photon‐related noise directly from an x‐ray image, we implement a Poisson‐Gaussian noise model. This model is used to compare the dose efficiency of two settings and combined with the well‐known KSNR to determine the transmission parameters. We present a method with an associated measurement protocol, utilizing the robotic capabilities of the used system to automatically obtain quasi‐scatter‐free ground‐truth data with exact geometric correspondence to full‐field and slot acquisitions. In total, we investigate two body regions (thoracic spine and lumbar spine) in anterior‐posterior view with two patient sizes (BMI = 22 and 30) in two acquisition modes (conventional and slot scan with a flat‐panel detector) with and without anti‐scatter grid using an anthropomorphic upper‐body phantom. Results: We have shown that it is feasible to combine the proposed approach with the KSNR for the determination of scatter rejection parameters. The use of an anti‐scatter grid is indicated for full‐field acquisitions allowing for dose savings up to 46% compared to their gridless counterparts. When changing the acquisition mode to the investigated slot scan, the use of an anti‐scatter grid has no major impact on the image quality in terms of dose efficiency, in particular for patients with a BMI of 22. However, an increased contrast improvement factor was found. For normal‐sized patients, up to 53% of dose can be saved additionally in comparison to full‐field acquisitions with grid. Moreover, we could demonstrate that a slot size of 5 cm and air gap of 10 cm is sufficient to achieve scatter‐to‐primary ratios, which are equal or better compared to those of the full‐field acquisitions with a grid. Conclusions: We have shown, that the slot‐scanning approach is always superior to the conventional full‐field acquisition in terms of signal‐to‐noise and scatter‐to‐primary ratios. Compared to the state‐of‐the‐art acquisition protocols with a grid, dose savings up to 53% are possible due to the scatter rejection without compromising the SNR. Hence, the use of the slot‐scanning method is indicated, especially when it comes to regularly carried‐out follow‐up acquisitions, for example, in the case of scoliosis monitoring. [ABSTRACT FROM AUTHOR]

Iodinated contrast-enhanced X-ray imaging of the breast has been studied with various modalities, including full-field digital mammography (FFDM), digital breast tomosynthesis (DBT), and dedicated breast CT. Contrast imaging with breast CT has a number of advantages over FFDM and DBT, including the lack of breast compression, and generation of fully isotropic 3-D reconstructions. Nonetheless, for breast CT to be considered as a viable tool for routine clinical use, it would be desirable to reduce radiation dose. One approach for dose reduction in breast CT is spectral shaping using X-ray filters. In this paper, two high atomic number filter materials are studied, namely, gadolinium (Gd) and erbium (Er), and compared with Al and Cu filters currently used in breast CT systems. Task-based performance is assessed by imaging a cylindrical poly(methyl methacrylate) phantom with iodine inserts on a benchtop breast CT system that emulates clinical breast CT. To evaluate detectability, a channelized hoteling observer (CHO) is used with sums of Laguerre–Gauss channels. It was observed that spectral shaping using Er and Gd filters substantially increased the dose efficiency (defined as signal-to-noise ratio of the CHO divided by mean glandular dose) as compared with kilovolt peak and filter settings used in commercial and prototype breast CT systems. These experimental phantom study results are encouraging for reducing dose of breast CT, however, further evaluation involving patients is needed. [ABSTRACT FROM PUBLISHER]

This paper proposes a new method of simulating dose reduction in digital breast tomosynthesis, starting from a clinical image acquired with a standard radiation dose. It considers both signal-dependent quantum and signal-independent electronic noise. Furthermore, the method accounts for pixel crosstalk, which causes the noise to be frequency-dependent, thus increasing the simulation accuracy. For an objective assessment, simulated and real images were compared in terms of noise standard deviation, signal-to-noise ratio (SNR) and normalized noise power spectrum (NNPS). A two-alternative forced-choice (2-AFC) study investigated the similarity between the noise strength of low-dose simulated and real images. Six experienced medical physics specialists participated on the study, with a total of 2 160 readings. Objective assessment showed no relevant trends with the simulated noise. The relative error in the standard deviation of the simulated noise was less than 2% for every projection angle. The relative error of the SNR was less than 1.5%, and the NNPS of the simulated images had errors less than 2.5%. The 2-AFC human observer experiment yielded no statistically significant difference ( p =0.84) in the perceived noise strength between simulated and real images. Furthermore, the observer study also allowed the estimation of a dose difference at which the observer perceived a just-noticeable difference (JND) in noise levels. The estimated JND value indicated that a change of 17% in the current-time product was sufficient to cause a noticeable difference in noise levels. The observed high accuracy, along with the flexible calibration, make this method an attractive tool for clinical image-based simulations of dose reduction. [ABSTRACT FROM AUTHOR]