Breast tomosynthesis: state-of-the-art and review of the literature

Optimizing angular range in digital breast tomosynthesis: A phantom study investigating lesion detection across varied breast density and thickness

PURPOSE

To determine the optimal angular range (AR) for digital breast tomosynthesis (DBT) systems that provides highest lesion visibility across various breast densities and thicknesses.

METHOD

A modular DBT phantom, consisting of tissue-equivalent adipose and glandular modules, along with a module embedded with test objects (speckles, masses, fibers), was used to create combinations simulating different breast thicknesses, densities, and lesion locations. A prototype DBT system operated at four ARs (AR±7.5°, AR±12.5°, AR±19°, and AR±25°) to acquire 11 projection images for each combination, with separate fixed doses for thin and thick combinations. Three blinded radiologists independently assessed lesion visibility in reconstructed images; assessments were averaged and compared using linear mixed models.

RESULTS

Speckle visibility was highest with AR±7.5° or AR±12.5°, decreasing with wider ARs in all density and thickness combinations. The difference between AR±7.5° and AR±12.5° was not statistically significant, except for the tube-side speckles in thin-fatty combinations (5.83 [AR±7.5°] vs. 5.39 [AR±12.5°], P = 0.019). Mass visibility was not affected by AR in thick combinations, while AR±12.5° exhibited the highest mass visibility for both thin-fatty and thin-dense combinations (P = 0.032 and 0.007, respectively). Different ARs provided highest fiber visibility for different combinations; however, AR±12.5° consistently provided highest or comparable visibility. AR±12.5° showed highest overall lesion visibility for all density and thickness combinations.

CONCLUSIONS

AR±12.5° exhibited the highest overall lesion visibility across various phantom thicknesses and densities using a projection number of 11.

Convolutional neural network-based model observer for signal known statistically task in breast tomosynthesis images

BACKGROUND

Since human observer studies are resource-intensive, mathematical model observers are frequently used to assess task-based image quality. The most common implementation of these model observers assume that the signal information is exactly known. However, these tasks cannot thoroughly represent situations where the signal information is not exactly known in terms of size and shape.

PURPOSE

Considering the limitations of the tasks for which signal information is exactly known, we proposed a convolutional neural network (CNN)-based model observer for signal known statistically (SKS) and background known statistically (BKS) detection tasks in breast tomosynthesis images.

METHODS

A wide parameter search was conducted from six different acquisition angles (i.e., 10°, 20°, 30°, 40°, 50°, and 60°) within the same dose level (i.e., 2.3 mGy) under two separate acquisition schemes: (1) constant total number of projections, and (2) constant angular separation between projections. Two different types of signals: spherical (i.e., SKE tasks) and spiculated (i.e., SKS tasks) were used. The detection performance of the CNN-based model observer was compared with that of the Hotelling observer (HO) instead of the IO. Pixel-wise gradient-weighted class activation mapping (pGrad-CAM) map was extracted from each reconstructed tomosynthesis image to provide an intuitive understanding of the trained CNN-based model observer.

RESULTS

The CNN-based model observer achieved a higher detection performance compared to that of the HO for all tasks. Moreover, the improvement in its detection performance was greater for SKS tasks compared to that for SKE tasks. These results demonstrated that the addition of nonlinearity improved the detection performance owing to the variation of the background and signal. Interestingly, the pGrad-CAM results effectively localized the class-specific discriminative region, further supporting the quantitative evaluation results of the CNN-based model observer. In addition, we verified that the CNN-based model observer required fewer images to achieve the detection performance of the HO.

CONCLUSIONS

In this work, we proposed a CNN-based model observer for SKS and BKS detection tasks in breast tomosynthesis images. Throughout the study, we demonstrated that the detection performance of the proposed CNN-based model observer was superior to that of the HO.

Avoiding Tissue Overlap in 2D Images: Single-Slice DBT Classification Using Convolutional Neural Networks

Breast cancer was the most diagnosed cancer around the world in 2020. Screening programs, based on mammography, aim to achieve early diagnosis which is of extreme importance when it comes to cancer. There are several flaws associated with mammography, with one of the most important being tissue overlapping that can result in both lesion masking and fake-lesion appearance. To overcome this, digital breast tomosynthesis takes images (slices) at different angles that are later reconstructed into a 3D image. Having in mind that the slices are planar images where tissue overlapping does not occur, the goal of the work done here was to develop a deep learning model that could, based on the said slices, classify lesions as benign or malignant. The developed model was based on the work done by Muduli et. al, with a slight change in the fully connected layers and in the regularization done. In total, 77 DBT volumes-39 benign and 38 malignant-were available. From each volume, nine slices were taken, one where the lesion was most visible and four above/below. To increase the quantity and the variability of the data, common data augmentation techniques (rotation, translation, mirroring) were applied to the original images three times. Therefore, 2772 images were used for training. Data augmentation techniques were then applied two more times-one set used for validation and one set used for testing. Our model achieved, on the testing set, an accuracy of 93.2% while the values of sensitivity, specificity, precision, F1-score, and Cohen's kappa were 92%, 94%, 94%, 94%, and 0.86, respectively. Given these results, the work done here suggests that the use of single-slice DBT can compare to state-of-the-art studies and gives a hint that with more data, better augmentation techniques and the use of transfer learning might overcome the use of mammograms in this type of studies.

Breast Cancer Screening in Women With Dense Breasts: Current Status and Future Directions for Appropriate Risk Stratification and Imaging Utilization

Breast density continues to be a prevailing topic in the field of breast imaging, with continued complexities contributing to overall confusion and controversy among patients and the medical community. In this article, we explore the current status of breast cancer screening in women with dense breasts including breast density legislation. Risk-based approaches to supplemental screening may be more financially cost-effective. While all advanced imaging modalities detect additional primarily invasive, node-negative cancers, the degree to which this occurs can vary by density category. Future directions include expanding the use of density-inclusive risk models with appropriate risk stratification and imaging utilization. Further research is needed, however, to better understand how to optimize population-based screening programs with knowledge of patients' individualized risk, including breast density assessment, to improve the benefit-to-harm ratio of breast cancer screening.

Artificial intelligence assistance for women who had spot compression view: reducing recall rates for digital mammography

Background

Mammography yields inevitable recall for indeterminate findings that need to be confirmed with additional views.

Purpose

To explore whether the artificial intelligence (AI) algorithm for mammography can reduce false-positive recall in patients who undergo the spot compression view.

Material and Methods

From January to December 2017, 236 breasts from 225 women who underwent the spot compression view due to focal asymmetry, mass, or architectural distortion on standard digital mammography were included. Three readers who were blinded to the study purpose, patient information, previous mammograms, following spot compression views, and any clinical or pathologic reports retrospectively reviewed 236 standard mammograms and determined the necessity of patient recall and the probability of malignancy per breast, first without and then with AI assistance. The performances of AI and the readers were evaluated with the recall rate, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy.

Results

Among 236 examinations, 8 (3.4%) were cancers and 228 (96.6%) were benign. The recall rates of all three readers significantly decreased with AI assistance ( P < 0.05). The reader-averaged recall rates significantly decreased with AI assistance regardless of breast composition (fatty breasts: 32.7% to 24.1%m P = 0.002; dense breasts: 33.6% to 21.2%, P < 0.001). The reader-averaged AUC increased with AI assistance and was comparable to that of standalone AI (0.835 vs. 0.895; P = 0.234). The reader-averaged specificity (71.2% to 79.8%, P < 0.001) and accuracy (71.3% to 79.7%, P  < 0.001) significantly improved with AI assistance.

Conclusion

AI assistance significantly reduced false-positive recall without compromising cancer detection in women with focal asymmetry, mass, or architectural distortion on standard digital mammography regardless of mammographic breast density.

Comparative evaluation of tomosynthesis, computed tomography, and magnetic resonance imaging findings for metacarpophalangeal joints from equine cadavers

OBJECTIVE

To describe the technique and assess the diagnostic potential and limitations of tomosynthesis for imaging of the metacarpophalangeal joint (MCPJ) of equine cadavers; compare the tomosynthesis appearance of pathological lesions with their conventional radiographic, CT, and MRI appearances; and evaluate all imaging findings with gross lesions of a given MCPJ.

SAMPLE

Distal portions of 4 forelimbs from 4 equine cadavers.

PROCEDURES

The MCPJs underwent radiography, tomosynthesis (with a purpose-built benchtop unit), CT, and MRI; thereafter, MCPJs were disarticulated and evaluated for the presence of gross lesions. The ability to identify pathological lesions on all images was assessed, followed by semiobjective scoring for quality of the overall image and appearance of the subchondral bone, articular cartilage, periarticular margins, and adjacent trabecular bone of the third metacarpal bone, proximal phalanx, and proximal sesamoid bones of each MCPJ.

RESULTS

Some pathological lesions in the subchondral bone of the third metacarpal bone were detectable with tomosynthesis but not with radiography. Overall, tomosynthesis was comparable to radiography, but volumetric imaging modalities were superior to tomosynthesis and radiography for imaging of subchondral bone, articular cartilage, periarticular margins, and adjacent bone.

CONCLUSIONS AND CLINICAL RELEVANCE

With regard to the diagnostic characterization of equine MCPJs, tomosynthesis may be more accurate than radiography for identification of lesions within subchondral bone because, in part, of its ability to reduce superimposition of regional anatomic features. Tomosynthesis may be useful as an adjunctive imaging technique, highlighting subtle lesions within bone, compared with standard radiographic findings.

Comparing the diagnostic efficacy of full field digital mammography with digital breast tomosynthesis using BIRADS score in a tertiary cancer care hospital

Introduction

Breast cancer is one of the leading cancers in females worldwide, and its incidence has been rising at an exponential pace in the last 10 years even in India. Mammography has been the mainstay for detection of breast cancer over decades and has gradually advanced from screen film to full-field digital mammography. Recently, tomosynthesis has evolved as an advanced imaging investigation for early diagnosis of breast lesions in both diagnostic and screening settings.

Aim of Study

To compare and evaluate the impact of digital breast tomosynthesis (DBT) compared to full-field digital mammography (FFDM) in the interpretation of BIRADS score in both diagnostic and screening settings.

Settings and Design

A 1-year prospective longitudinal study was conducted in the Department of Radio-diagnosis in our institute using Hologic Selenia Dimensions for mammography as well as tomosynthesis.

Materials and Methods

One hundred women known or suspected (opportunistic screening) for breast cancer were evaluated either with FFDM alone or both FFDM and DBT. Sensitivity, specificity, positive predictive value, negative predictive value, and P value were used to assess the various diagnostic criteria in our study.

Results

Addition of DBT to FFDM results in a statistically significant increase in the sensitivity, specificity, and positive predictive value, and a statistically significant decrease in the false positive rates. Similar results were noted in both diagnostic and screening cases. It was observed that, in most cases, i.e. a total of 47, DBT did not change the BIRADS scoring; however, its addition increased the diagnostic confidence. BIRADS was upgraded and downgraded in 14 and 31 cases, respectively, with the addition of DBT to FFDM. New lesions were seen with addition of DBT to FFDM in 8 cases.

Conclusion

Addition of DBT to FFDM results in increase in sensitivity, specificity, positive predictive value, and a statistically significant decrease in false positive rates in both diagnostic and screening cases. As addition of tomosynthesis results in a significant decrease in recall rate, it should be added, at least, in all screening mammography programs.

Development and preclinical evaluation of a patient-specific high energy x-ray phase sensitive breast tomosynthesis system

BACKGROUND

This article reports the first x-ray phase sensitive breast tomosynthesis (PBT) system that is aimed for direct translation to clinical practice for the diagnosis of breast cancer.

PURPOSE

To report the preclinical evaluation and comparison of the newly built PBT system with a conventional digital breast tomosynthesis (DBT) system.

METHODS AND MATERIALS

The PBT system is developed based on a comprehensive inline phase contrast theoretical model. The system consists of a polyenergetic microfocus x-ray source and a flat panel detector mounted on an arm that is attached to a rotating gantry. It acquires nine projections over a 15° angular span in a stop-and-shoot manner. A dedicated phase retrieval algorithm is integrated with a filtered back-projection method that reconstructs tomographic slices. The American College of Radiology (ACR) accreditation phantom, a contrast detail (CD) phantom and mastectomy tissue samples were imaged at the same glandular dose levels by both the PBT and a standard of care DBT system for image quality characterizations and comparisons.

RESULTS

The PBT imaging scores with the ACR phantom are in good to excellent range and meet the quality assurance criteria set by the Mammography Quality Standard Act. The CD phantom image comparison and associated statistical analyses from two-alternative forced-choice reader studies confirm the improvement offered by the PBT system in terms of contrast resolution, spatial resolution, and conspicuity. The artifact spread function (ASF) analyses revealed a sizable lateral spread of metal artifacts in PBT slices as compared to DBT slices. Signal-to-noise ratio values for various inserts of the ACR and CD phantoms further validated the superiority of the PBT system. Mastectomy sample images acquired by the PBT system showed a superior depiction of microcalcifications vs the DBT system.

CONCLUSION

The PBT imaging technology can be clinically employed for improving the accuracy of breast cancer screening and diagnosis.

Deep Learning: A Breakthrough in Medical Imaging

Deep learning has attracted great attention in the medical imaging community as a promising solution for automated, fast and accurate medical image analysis, which is mandatory for quality healthcare. Convolutional neural networks and its variants have become the most preferred and widely used deep learning models in medical image analysis. In this paper, concise overviews of the modern deep learning models applied in medical image analysis are provided and the key tasks performed by deep learning models, i.e. classification, segmentation, retrieval, detection, and registration are reviewed in detail. Some recent researches have shown that deep learning models can outperform medical experts in certain tasks. With the significant breakthroughs made by deep learning methods, it is expected that patients will soon be able to safely and conveniently interact with AI-based medical systems and such intelligent systems will actually improve patient healthcare. There are various complexities and challenges involved in deep learning-based medical image analysis, such as limited datasets. But researchers are actively working in this area to mitigate these challenges and further improve health care with AI.

Evaluation of reconstruction algorithms for a stationary digital breast tomosynthesis system using a carbon nanotube X-ray source array

Breast cancer is the most frequently diagnosed cancer in women worldwide. Digital breast tomosynthesis (DBT), which is based on limited-angle tomography, was developed to solve tissue overlapping problems associated with traditional breast mammography. However, due to the problems associated with tube movement during the process of data acquisition, stationary DBT (s-DBT) was developed to allow the X-ray source array to stay stationary during the DBT scanning process. In this work, we evaluate four widely used and investigated DBT image reconstruction algorithms, including the commercial Feldkamp-Davis-Kress algorithm (FBP), the simultaneous iterative reconstruction technique (SIRT), the simultaneous algebraic reconstruction technique (SART) and the total variation regularized SART (SART-TV) for an s-DBT imaging system that we set up in our own laboratory for studies using a semi-elliptical digital phantom and a rubber breast phantom to determine the most superior algorithm for s-DBT image reconstruction among the four algorithms. Several quantitative indexes for image quality assessment, including the peak signal-noise ratio (PSNR), the root mean square error (RMSE) and the structural similarity (SSIM), are used to determine the best algorithm for the imaging system that we set up. Image resolutions are measured via the calculation of the contrast-to-noise ratio (CNR) and artefact spread function (ASF). The experimental results show that the SART-TV algorithm gives reconstructed images with the highest PSNR and SSIM values and the lowest RMSE values in terms of image accuracy and similarity, along with the highest CNR values calculated for the selected features and the best ASF curves in terms of image resolution in the horizontal and vertical directions. Thus, the SART-TV algorithm is proven to be the best algorithm for use in s-DBT image reconstruction for the specific imaging task in our study.

Effect of source blur on digital breast tomosynthesis reconstruction

PURPOSE

Most digital breast tomosynthesis (DBT) reconstruction methods neglect the blurring of the projection views caused by the finite size or motion of the x-ray focal spot. This paper studies the effect of source blur on the spatial resolution of reconstructed DBT using analytical calculation and simulation, and compares the influence of source blur over a range of blurred source sizes.

METHODS

Mathematically derived formulas describe the point spread function (PSF) of source blur on the detector plane as a function of the spatial locations of the finite-sized source and the object. By using the available technical parameters of some clinical DBT systems, we estimated the effective source sizes over a range of exposure time and DBT scan geometries. We used the CatSim simulation tool (GE Global Research, NY) to generate digital phantoms containing line pairs and beads at different locations and imaged with sources of four different sizes covering the range of potential source blur. By analyzing the relative contrasts of the test objects in the reconstructed images, we studied the effect of the source blur on the spatial resolution of DBT. Furthermore, we simulated a detector that rotated in synchrony with the source about the rotation center and calculated the spatial distribution of the blurring distance in the imaged volume to estimate its influence on source blur.

RESULTS

Calculations demonstrate that the PSF is highly shift-variant, making it challenging to accurately implement during reconstruction. The results of the simulated phantoms demonstrated that a typical finite-sized focal spot (~0.3 mm) will not affect the reconstructed image resolution if the x-ray tube is stationary during data acquisition. If the x-ray tube moves during exposure, the extra blur due to the source motion may degrade image resolution, depending on the effective size of the source along the direction of the motion. A detector that rotates in synchrony with the source does not reduce the influence of source blur substantially.

CONCLUSIONS

This study demonstrates that the extra source blur due to the motion of the x-ray tube during image acquisition substantially degrades the reconstructed image resolution. This effect cannot be alleviated by rotating the detector in synchrony with the source. The simulation results suggest that there are potential benefits of modeling the source blur in image reconstruction for DBT systems using continuous-motion acquisition mode.

Eye Movements in Medical Image Perception: A Selective Review of Past, Present and Future

The eye movements of experts, reading medical images, have been studied for many years. Unlike topics such as face perception, medical image perception research needs to cope with substantial, qualitative changes in the stimuli under study due to dramatic advances in medical imaging technology. For example, little is known about how radiologists search through 3D volumes of image data because they simply did not exist when earlier eye tracking studies were performed. Moreover, improvements in the affordability and portability of modern eye trackers make other, new studies practical. Here, we review some uses of eye movements in the study of medical image perception with an emphasis on newer work. We ask how basic research on scene perception relates to studies of medical 'scenes' and we discuss how tracking experts' eyes may provide useful insights for medical education and screening efficiency.

Transfer Learning From Convolutional Neural Networks for Computer-Aided Diagnosis: A Comparison of Digital Breast Tomosynthesis and Full-Field Digital Mammography

RATIONALE AND OBJECTIVES

With the growing adoption of digital breast tomosynthesis (DBT) in breast cancer screening, we compare the performance of deep learning computer-aided diagnosis on DBT images to that of conventional full-field digital mammography (FFDM).

MATERIALS AND METHODS

In this study, we retrospectively collected FFDM and DBT images of 78 biopsy-proven lesions from 76 patients. A region of interest was selected for each lesion on FFDM, synthesized 2D, and DBT key slice images. Features were extracted from each lesion using a pretrained convolutional neural network (CNN) and served as input to a support vector machine classifier trained in the task of predicting likelihood of malignancy.

RESULTS

From receiver operating characteristic (ROC) analysis of all 78 lesions, the synthesized 2D image performed best in both the cradiocaudal view (area under the ROC curve [AUC] = 0.81, SE = 0.05) and mediolateral oblique view (AUC = 0.88, SE = 0.04) in the task of lesion characterization. When cradiocaudal and mediolateral oblique data of each lesion were merged through soft voting, DBT key slice image performed best (AUC = 0.89, SE = 0.04). When only masses and architectural distortions (ARDs) were considered, DBT performed significantly better than FFDM (p = 0.024).

CONCLUSION

DBT performed significantly better than FFDM in the merged view classification of mass and ARD lesions. The increased performance suggests that the information extracted by the CNN from DBT images may be more relevant to lesion malignancy status than the information extracted from FFDM images. Therefore, this study provides supporting evidence for the efficacy of computer-aided diagnosis on DBT in the evaluation of mass and ARD lesions.

Is contralateral breast ultrasound worthwhile in preoperative staging of breast cancer?

BACKGROUND

Women with invasive breast cancer are at higher risk of contralateral synchronous cancer. This study aimed at determining if contralateral breast ultrasound (CBUS) examination should be routinely performed in the preoperative evaluation of breast cancer patients.

METHODS

This is a retrospective study of preoperative CBUS examinations performed between January 2012 and April 2015. The charts of patients presenting for biopsy of a Breast Imaging Reporting and Data System (BIRADS) 5 lesion and who had undergone a concomitant contralateral breast US examination were reviewed. Index tumor, lymph node status, American College of Radiology (ACR) breast density on mammogram, total scanning time, and results of CBUS were recorded.

RESULTS

Of the 3007 patients who underwent breast biopsies during the study period, 360 patients met the inclusion criteria. Index mass size was 19 ± 10 mm. CBUS examination led to 76 biopsies, of which 12 were positive in 11 patients. Detection rate for mammographically occult contralateral invasive cancers was 3.1% (11/360). Contralateral lesion size was 13 ± 10 mm. Breast density was rated ACR C/D for nine women and ACR B for two. In the ACR C/D subgroup (82%), the contralateral cancer detection rate was 4.1%. Average additional scanning time spent required to perform CBUS examination was 3.1 ± 4.9 min. Patients diagnosed with contralateral invasive breast cancer underwent surgery and/or chemotherapy. The treatment strategy was changed in all 11 patients after the detection of a second primary cancer.

CONCLUSION

Preoperative CBUS is effective and most beneficial with women presenting ACR C/D breast density. Given its impact on decreasing future morbidity, its routine use should be considered to improve quality healthcare for women diagnosed with breast cancer.

Assessment of scatter radiation dose and absorbed doses in eye lens and thyroid gland during digital breast tomosynthesis

Digital breast tomosynthesis (DBT) is an alternative tool for breast cancer screening; however, the magnitude of peripheral organs dose is not well known. This study aimed to measure scattered dose and estimate organ dose during mammography under conventional (CM) and Tomo (TM) modes in a specific DBT system. Optically stimulated luminescence dosimeters (OSLDs), whose responses were corrected using a parallel-plate ionization chamber, were pasted on the surface of custom-made polymethyl methacrylate (PMMA) and RANDO phantoms to measure entrance surface air kerma (ESAK). ESAK measurements were also acquired with a 4.5-cm thick breast phantom for a standard mammogram. Organ dose conversion factors (CFD ) were determined as ratio of air kerma at a specific depth to that at the surface for the PMMA phantom and multiplied by the ratio of mass energy absorption coefficients of tissue to air. Normalized eye lens and thyroid gland doses were calculated using the RANDO phantom by multiplying CFD and ESAK values. Maximum variability in OSLD response to scatter radiation from the DBT system was 33% in the W/Rh spectrum and variations in scattered dose distribution were observed between CM and TM. The CFD values for eye lens and thyroid gland ranged between 0.58 to 0.66 and 0.29 to 0.33, respectively. Mean organ doses for two-view unilateral imaging were 0.24 (CM) and 0.18 (TM) μGy/mAs for the eye lens and 0.24 (CM) and 0.25 (TM) μGy/mAs for the thyroid gland. Higher organ doses were observed during TM compared to CM as the automatic exposure control (AEC) system resulted in greater total mAs values in TM.

Factors Affecting Breast Cancer Detectability on Digital Breast Tomosynthesis and Two-Dimensional Digital Mammography in Patients with Dense Breasts

Objective

To compare digital breast tomosynthesis (DBT) and conventional full-field digital mammography (FFDM) in the detectability of breast cancers in patients with dense breast tissue, and to determine the influencing factors in the detection of breast cancers using the two techniques.

Materials and Methods

Three blinded radiologists independently graded cancer detectability of 300 breast cancers (288 women with dense breasts) on DBT and conventional FFDM images, retrospectively. Hormone status, histologic grade, T stage, and breast cancer subtype were recorded to identify factors affecting cancer detectability. The Wilcoxon signed-rank test was used to compare cancer detectability by DBT and conventional FFDM. Fisher's exact tests were used to determine differences in cancer characteristics between detectability groups. Kruskal-Wallis tests were used to determine whether the detectability score differed according to cancer characteristics.

Results

Forty breast cancers (13.3%) were detectable only with DBT; 191 (63.7%) breast cancers were detected with both FFDM and DBT, and 69 (23%) were not detected with either. Cancer detectability scores were significantly higher for DBT than for conventional FFDM (median score, 6; range, 0–6; p < 0.001). The DBT-only cancer group had more invasive lobular-type breast cancers (22.5%) than the other two groups (i.e., cancer detected on both types of image [both-detected group], 5.2%; cancer not detected on either type of image [both-non-detected group], 7.3%), and less detectability of ductal carcinoma in situ (5% vs. 16.8% [both-detected group] vs. 27.5% [both-non-detected group]). Low-grade cancers were more often detected in the DBT-only group than in the both-detected group (22.5% vs. 10%, p = 0.026). Human epidermal growth factor receptor-2 (HER-2)-negative cancers were more often detected in the DBT-only group than in the both-detected group (92.3% vs. 70.5%, p = 0.004). Cancers surrounded by mostly glandular tissue were detected less often in the DBT only group than in the both-non-detected group (10% vs. 31.9%, p = 0.016). DBT cancer detectability scores were significantly associated with cancer type (p = 0.012), histologic grade (p = 0.013), T and N stage (p = 0.001, p = 0.024), proportion of glandular tissue surrounding lesions (p = 0.013), and lesion type (p < 0.001).

Conclusion

Invasive lobular, low-grade, or HER-2-negative cancer is more detectable with DBT than with conventional FFDM in patients with dense breasts, but cancers surrounded by mostly glandular tissue might be missed with both techniques.

Diagnostic Performance of Digital Breast Tomosynthesis for Breast Suspicious Calcifications From Various Populations: A Comparison With Full-field Digital Mammography

The diagnostic performance difference between digital breast tomosynthesis (DBT) and conventional full-field digital mammography (FFDM) for breast suspicious calcifications from various populations is unclear. The objective of this study is to determine whether DBT exhibits the diagnostic advantage for breast suspicious calcifications from various populations compared with FFDM. Three hundred and five patients were enrolled (of which seven patients with bilateral lesions) and 312 breasts images were retrospectively analyzed by three radiologists independently. The postoperative pathology of breast calcifications was the gold standard. Breast cancer was diagnosed utilizing DBT and FFDM with sensitivities of 92.9% and 88.8%, specificities of 87.9% and 75.2%, positive predictive values of 77.8% and 62.1%, negative predictive values of 96.4% and 93.6%, respectively. DBT exhibited significantly higher diagnostic accuracy for benign calcifications compared with FFDM (87.9% vs 75.2%), and no advantage in the diagnosis of malignant calcifications. DBT diagnostic accuracy was notably higher than FFDM in premenopausal (88.4% vs 78.8%), postmenopausal (90.2% vs 77.2%), and dense breast cases (89.4% vs 81.9%). There was no significant difference in non-dense breast cases. In our study, DBT exhibited a superior advantage in dense breasts and benign calcifications cases compared to FFDM, while no advantage was observed in non-dense breasts or malignant calcifications cases. Thus, in the breast cancer screening for young women with dense breasts, DBT may be recommended for accurate diagnosis. Our findings may assist the clinicians in applying the optimal techniques for different patients and provide a theoretical basis for the update of breast cancer screening guideline.

Breast tomosynthesis for the clarification of mammographic BI-RADS 3 lesions can decrease follow-up examinations and enables immediate cancer diagnosis

Background The limited sensitivity of mammography in case of a high breast density often produces unclear or false-positive findings, so-called BI-RADS 3 lesions, which have to be followed up to prove benignity. Digital breast tomosynthesis (DBT) was developed to reduce such summation effects. Purpose To evaluate the influence of an additional DBT on the management of mammographic BI-RADS 3 findings and whether DBT can decrease the time to definitive diagnosis or not. Material and Methods We analyzed 87 patients with a mammographic non-calcified BI-RADS 3 lesion who underwent an additional DBT of the affected breast. A follow-up two-dimensional (2D) examination or a histological result of the lesion had to be available. The images were analyzed especially for the BI-RADS category and incremental diagnostic accuracy. Moreover, the inter-reader reliability and the radiation dose were evaluated. Results The BI-RADS category has been changed by the addition of DBT: 57.1% were assessed as BI-RADS 1 or 2, 4.6% as BI-RADS 4, and only 38.3% remained as BI-RADS 3. The intraclass correlation coefficient for the three readers showed a good agreement for inter-reader reliability. No false-negative examination was found in the follow-ups. Nine lesions were biopsied (seven benign, two malignant). Both malignant lesions were suspicious in the DBT (BI-RADS 4). A significant higher glandular dose was necessary for the DBT. Conclusion DBT has the potential to reduce the recall-rate of BI-RADS 3 lesions and to find and diagnose malignant lesions earlier than 2D mammography alone.

Impact of Addition of Digital Breast Tomosynthesis to Digital Mammography in Lesion Characterization in Breast Cancer Patients

Context:

Digital breast tomosynthesis (DBT) is a new development in mammography technology which reduces the effect of overlapping tissue.

Aims:

The aim is to interrogate whether addition of DBT to digital mammography (DM) helps in better characterization of mammographic abnormalities in breast cancer patients in general and in different breast compositions.

Settings and Design:

Retrospective, analytical cross-sectional study.

Subjects and Methods:

Mammographic findings in 164 patients with 170 pathologically proven lesions were evaluated by using first DM alone and thereafter with addition of DBT to DM. The perceived utility of adjunct DBT was scored using a rating of 0–2. A score of 0 indicating that DM plus DBT was comparable to DM alone, 1 indicating that DM plus DBT was slightly better, and 2 indicating that DM plus DBT was definitely better.

Statistical Analysis:

McNemar Chi-squares test, Fisher's exact test.

Results:

On DM, 149 lesions were characterized mass with or without calcifications, 18 asymmetries with or without calcifications, 2 as architectural distortion, and 1 as microcalcification alone. Adjunct DBT helped in better morphological characterization of 17 lesions, with revelation of underlying masses in 16 asymmetries and one architectural distortion. Adjunct DBT was perceived to be slightly better than DM alone in 44.7% lesions, and definitely better in 22.9% lesions. Lesions showing score 1 or 2 improvement were significantly higher in heterogeneously and extremely dense breasts (P < 0.001).

Conclusions:

Adjunct DBT improves morphological characterization of lesions in patients with breast cancer. It highlights more suspicious features of lesions that indicate the presence of cancer, particularly in dense breasts.

Implementation of Upright Digital Breast Tomosynthesis-guided Stereotactic Biopsy

With growing adoption of digital breast tomosynthesis, an increasing number of imaging abnormalities are being identified only by tomosynthesis. Upright digital breast tomosynthesis-guided stereotactic biopsy is a proven method for sampling these abnormalities as well as abnormalities traditionally evaluated using conventional stereotactic biopsy. In this article, we describe the technique of upright digital breast tomosynthesis-guided stereotactic biopsy and outline a systematic operational approach to implementation of this technique in clinical radiology practices.

A micro-CL system and its applications

The computed laminography (CL) method is preferable to computed tomography for the non-destructive testing of plate-like objects. A micro-CL system is developed for three-dimensional imaging of plate-like objects. The details of the micro-CL system are described, including the system architecture, scanning modes, and reconstruction algorithm. The experiment results of plate-like fossils, insulated gate bipolar translator module, ball grid array packaging, and printed circuit board are also presented to demonstrate micro-CL's ability for 3D imaging of flat specimens and universal applicability in various fields.

Digital breast tomosynthesis (DBT): recommendations from the Italian College of Breast Radiologists (ICBR) by the Italian Society of Medical Radiology (SIRM) and the Italian Group for Mammography Screening (GISMa)

This position paper, issued by ICBR/SIRM and GISMa, summarizes the evidence on DBT and provides recommendations for its use. In the screening setting, DBT in adjunct to digital mammography (DM) increased detection rate by 0.5-2.7‰ and decreased false positives by 0.8-3.6% compared to DM alone in observational and double-testing experimental studies. The reduction in recall rate could be less prominent in those screening programs which already have low recall rates with DM. The increase in radiation exposure associated with DM/DBT protocols has been solved by the introduction of synthetic mammograms (sDM) reconstructed from DBT datasets. Thus, whenever possible, sDM/DBT should be preferred to DM/DBT. However, before introducing DBT as a routine screening tool for average-risk women, we should wait for the results of randomized controlled trials and for a statistically significant and clinically relevant reduction in the interval cancer rate, hopefully associated with a reduction in the advanced cancer rate. Otherwise, a potential for overdiagnosis and overtreatment cannot be excluded. Studies exploring this issue are ongoing. Screening of women at intermediate risk should follow the same recommendations, with particular protocols for women with previous BC history. In high-risk women, if mammography is performed as an adjunct to MRI or in the case of MRI contraindications, sDM/DBT protocols are suggested. Evidence exists in favor of DBT usage in women with clinical symptoms/signs and asymptomatic women with screen-detected findings recalled for work-up. The possibility to perform needle biopsy or localization under DBT guidance should be offered when DBT-only findings need characterization or surgery.

Added Value of Digital Breast Tomosynthesis Combined with Digital Mammography According to Reader Agreement: Changes in BI-RADS Rate and Follow-Up Management

BACKGROUND

The aim of this study was to evaluate the added value of digital breast tomosynthesis (DBT) when combined with digital mammography (DM) in BI-RADS assessment and follow-up management.

METHODS

From February 2014 to January 2015, 214 patients underwent DM and DBT, acquired with a Siemens Mammomat Inspiration unit. 2 expert readers independently reviewed the studies in 2 steps: DM and DM+DBT, according to BI-RADS rate. Patients with BI-RADS 0, 3, 4, and 5 were recalled for work-up. Inter-reader agreement for BI-RADS rate and work-up rate were evaluated using Cohen's kappa.

RESULTS

Inter-reader agreement (κ value) for BI-RADS classification was 0.58 for DM and 0.8 for DM+DBT. DM+DBT increased the number of BI-RADS 1, 2, 4, 5 and reduced the number of BI-RADS 0 and 3 for both readers compared to DM alone. Regarding work-up rate agreement, κ was poor for DM and substantial (0.7) for DM+DBT. DM+DBT also reduced the work-up rate for both Reader 1 and Reader 2.

CONCLUSION

DM+DBT increased the number of negative and benign cases (BI-RADS 1 and 2) and suspicious and malignant cases (BI-RADS 4 and 5), while it reduced the number of BI-RADS 0 and 3. DM+DBT also improved inter-reader agreement and reduced the overall recall for additional imaging or short-interval follow-up.

Risk-based Breast Cancer Screening: Implications of Breast Density

The approach to breast cancer screening has changed over time from a general approach to a more personalized, risk-based approach. Women with dense breasts, one of the most prevalent risk factors, are now being informed that they are at increased risk of developing breast cancer and should consider supplemental screening beyond mammography. This article reviews the current evidence regarding the impact of breast density relative to other known risk factors, the evidence regarding supplemental screening for women with dense breasts, supplemental screening options, and recommendations for physicians having shared decision-making discussions with women who have dense breasts.

The wisdom of crowds for visual search

Decision-making accuracy typically increases through collective integration of people's judgments into group decisions, a phenomenon known as the wisdom of crowds. For simple perceptual laboratory tasks, classic signal detection theory specifies the upper limit for collective integration benefits obtained by weighted averaging of people's confidences, and simple majority voting can often approximate that limit. Life-critical perceptual decisions often involve searching large image data (e.g., medical, security, and aerial imagery), but the expected benefits and merits of using different pooling algorithms are unknown for such tasks. Here, we show that expected pooling benefits are significantly greater for visual search than for single-location perceptual tasks and the prediction given by classic signal detection theory. In addition, we show that simple majority voting obtains inferior accuracy benefits for visual search relative to averaging and weighted averaging of observers' confidences. Analysis of gaze behavior across observers suggests that the greater collective integration benefits for visual search arise from an interaction between the foveated properties of the human visual system (high foveal acuity and low peripheral acuity) and observers' nonexhaustive search patterns, and can be predicted by an extended signal detection theory framework with trial to trial sampling from a varying mixture of high and low target detectabilities across observers (SDT-MIX). These findings advance our theoretical understanding of how to predict and enhance the wisdom of crowds for real world search tasks and could apply more generally to any decision-making task for which the minority of group members with high expertise varies from decision to decision.

Assessing task performance in FFDM, DBT, and synthetic mammography using uniform and anthropomorphic physical phantoms

PURPOSE

The purpose of this study is to quantify the differences in detectability between full field digital mammography (FFDM), digital breast tomosynthesis (DBT), and synthetic mammography (SM) for challenging, low contrast signals, in the context of both a uniform and an anthropomorphic, textured phantom.

METHODS

Images of the phantoms were acquired using a Hologic Selenia Dimensions system. Images were taken at 50%, 100%, and 200% of the dose delivered under automatic exposure control (AEC). Low-contrast disks, created using an inkjet printer with iodine-doped ink, were inserted into the phantom. The disks varied in diameter from 210 to 630 μm, and in local contrast from 1.1% to 2.8% in regular increments. Human observers located the disks in a 4 alternative forced choice experiment. Proportion correct (PC) was computed as the number of correct localizations out of the total number of tries.

RESULTS

Overall, scores from FFDM and DBT were consistently greater than scores from SM. At an exposure corresponding to the AEC setting, mean PC scores for the largest disks with the uniform phantom were 0.80 for FFDM, 0.83 for DBT, and 0.66 for SM, with the same rank ordering at other doses. Scores were similar but lower for the nonuniform background. At an exposure twice the AEC setting, however, the difference between uniform and nonuniform scores was most pronounced for DBT alone. Differences between scores for FFDM and SM were statistically significant, while those between FFDM and DBT were not. Scores were used to compute the minimum contrast level needed to reach 62.5% detection rate. The minimum contrast for SM was 36%-81% higher compared to FFDM or DBT, in either background.

CONCLUSIONS

This study shows that an anthropomorphic phantom and lesions inserts may be used to conduct a reader study. Detectability was significantly lower for synthetic mammography than for FFDM or DBT, for all conditions. Additionally, observer performance was consistently lower for the anthropomorphic phantom, indicating the greater challenge due to anatomical background. Because of this, it may be important to use realistic phantoms in observer studies in order to draw conclusions that are more clinically relevant.

The role of extra-foveal processing in 3D imaging

The field of medical image quality has relied on the assumption that metrics of image quality for simple visual detection tasks are a reliable proxy for the more clinically realistic visual search tasks. Rank order of signal detectability across conditions often generalizes from detection to search tasks. Here, we argue that search in 3D images represents a paradigm shift in medical imaging: radiologists typically cannot exhaustively scrutinize all regions of interest with the high acuity fovea requiring detection of signals with extra-foveal areas (visual periphery) of the human retina. We hypothesize that extra-foveal processing can alter the detectability of certain types of signals in medical images with important implications for search in 3D medical images. We compare visual search of two different types of signals in 2D vs. 3D images. We show that a small microcalcification-like signal is more highly detectable than a larger mass-like signal in 2D search, but its detectability largely decreases (relative to the larger signal) in the 3D search task. Utilizing measurements of observer detectability as a function retinal eccentricity and observer eye fixations we can predict the pattern of results in the 2D and 3D search studies. Our findings: 1) suggest that observer performance findings with 2D search might not always generalize to 3D search; 2) motivate the development of a new family of model observers that take into account the inhomogeneous visual processing across the retina (foveated model observers).

Optimization of digital breast tomosynthesis (DBT) acquisition parameters for human observers: effect of reconstruction algorithms

We showed in our earlier work that the choice of reconstruction methods does not affect the optimization of DBT acquisition parameters (angular span and number of views) using simulated breast phantom images in detecting lesions with a channelized Hotelling observer (CHO). In this work we investigate whether the model-observer based conclusion is valid when using humans to interpret images. We used previously generated DBT breast phantom images and recruited human readers to find the optimal geometry settings associated with two reconstruction algorithms, filtered back projection (FBP) and simultaneous algebraic reconstruction technique (SART). The human reader results show that image quality trends as a function of the acquisition parameters are consistent between FBP and SART reconstructions. The consistent trends confirm that the optimization of DBT system geometry is insensitive to the choice of reconstruction algorithm. The results also show that humans perform better in SART reconstructed images than in FBP reconstructed images. In addition, we applied CHOs with three commonly used channel models, Laguerre-Gauss (LG) channels, square (SQR) channels and sparse difference-of-Gaussian (sDOG) channels. We found that LG channels predict human performance trends better than SQR and sDOG channel models for the task of detecting lesions in tomosynthesis backgrounds. Overall, this work confirms that the choice of reconstruction algorithm is not critical for optimizing DBT system acquisition parameters.

A novel pre-processing technique for improving image quality in digital breast tomosynthesis

PURPOSE

Nonlinear pre-reconstruction processing of the projection data in computed tomography (CT) where accurate recovery of the CT numbers is important for diagnosis is usually discouraged, for such a processing would violate the physics of image formation in CT. However, one can devise a pre-processing step to enhance detectability of lesions in digital breast tomosynthesis (DBT) where accurate recovery of the CT numbers is fundamentally impossible due to the incompleteness of the scanned data. Since the detection of lesions such as micro-calcifications and mass in breasts is the purpose of using DBT, it is justified that a technique producing higher detectability of lesions is a virtue.

METHODS

A histogram modification technique was developed in the projection data domain. Histogram of raw projection data was first divided into two parts: One for the breast projection data and the other for background. Background pixel values were set to a single value that represents the boundary between breast and background. After that, both histogram parts were shifted by an appropriate amount of offset and the histogram-modified projection data were log-transformed. Filtered-backprojection (FBP) algorithm was used for image reconstruction of DBT. To evaluate performance of the proposed method, we computed the detectability index for the reconstructed images from clinically acquired data.

RESULTS

Typical breast border enhancement artifacts were greatly suppressed and the detectability of calcifications and masses was increased by use of the proposed method. Compared to a global threshold-based post-reconstruction processing technique, the proposed method produced images of higher contrast without invoking additional image artifacts.

CONCLUSIONS

In this work, we report a novel pre-processing technique that improves detectability of lesions in DBT and has potential advantages over the global threshold-based post-reconstruction processing technique. The proposed method not only increased the lesion detectability but also reduced typical image artifacts pronounced in conventional FBP-based DBT.

Replacing single-view mediolateral oblique (MLO) digital mammography (DM) with synthesized mammography (SM) with digital breast tomosynthesis (DBT) images: Comparison of the diagnostic performance and radiation dose with two-view DM with or without MLO-DBT

OBJECTIVES

To evaluate the diagnostic performance and radiation dose of single view cranio-caudal (CC) digital mammography (DM) plus mediolateral oblique (MLO) digital breast tomosynthesis (DBT) combined with synthesized mammography (SM) in comparison with two-view DM with or without DBT.

MATERIAL AND METHODS

This study was approved by our institutional review board, and informed consent was obtained from 130 women. Paired two-view DM and single MLO-DBT with SM images were acquired, and four independent retrospective reading sessions of different combinations of DM, SM and DBT were performed for the presence of malignant tumors using jackknife alternative free-response receiver operator curve (JAFROC) methods. The diagnostic performances and average glandular dose (AGD) were compared between different combinations of DM, SM and DBT.

RESULTS

Of 159 lesions in 130 patients, 27 were malignant. When using MLO-DBT with SM instead of MLO-DM, a significantly higher sensitivity (P=0.016) and specificity (P=0.012) were noted than with two-view DM, and comparable figure of merit (FOM), sensitivity, and specificity to two-view DM with DBT were noted. The mean AGD of CC-DM plus MLO-DBT with SM was 5.78mGy±1.06 per patient, which was significantly lower than that with two-view DM with MLO-DBT (8.45mGy±1.32; P <0.001) and slightly higher than that with two-view DM (5.30mGy±0.63).

CONCLUSIONS

The combined use of CC-DM plus MLO-DBT with SM showed higher sensitivity and specificity to two-view DM with a smaller AGD increment and comparable diagnostic performance to that of two-view DM with MLO-DBT with a significantly lower mean AGD.

Population of 224 realistic human subject-based computational breast phantoms

PURPOSE

To create a database of highly realistic and anatomically variable 3D virtual breast phantoms based on dedicated breast computed tomography (bCT) data.

METHODS

A tissue classification and segmentation algorithm was used to create realistic and detailed 3D computational breast phantoms based on 230 + dedicated bCT datasets from normal human subjects. The breast volume was identified using a coarse three-class fuzzy C-means segmentation algorithm which accounted for and removed motion blur at the breast periphery. Noise in the bCT data was reduced through application of a postreconstruction 3D bilateral filter. A 3D adipose nonuniformity (bias field) correction was then applied followed by glandular segmentation using a 3D bias-corrected fuzzy C-means algorithm. Multiple tissue classes were defined including skin, adipose, and several fractional glandular densities. Following segmentation, a skin mask was produced which preserved the interdigitated skin, adipose, and glandular boundaries of the skin interior. Finally, surface modeling was used to produce digital phantoms with methods complementary to the XCAT suite of digital human phantoms.

RESULTS

After rejecting some datasets due to artifacts, 224 virtual breast phantoms were created which emulate the complex breast parenchyma of actual human subjects. The volume breast density (with skin) ranged from 5.5% to 66.3% with a mean value of 25.3% ± 13.2%. Breast volumes ranged from 25.0 to 2099.6 ml with a mean value of 716.3 ± 386.5 ml. Three breast phantoms were selected for imaging with digital compression (using finite element modeling) and simple ray-tracing, and the results show promise in their potential to produce realistic simulated mammograms.

CONCLUSIONS

This work provides a new population of 224 breast phantoms based on in vivo bCT data for imaging research. Compared to previous studies based on only a few prototype cases, this dataset provides a rich source of new cases spanning a wide range of breast types, volumes, densities, and parenchymal patterns.

Digital Mammography and Digital Breast Tomosynthesis

Breast imaging technology has advanced significantly from the 1930s until the present. American women have a 1 in 8 chance of developing breast cancer. Mammography has been proven in multiple clinical trials to reduce breast cancer mortality. Although a mainstay of breast imaging and improved from film-screen mammography, digital mammography is not a perfect examination. Overlapping obscuring breast tissue limits mammographic interpretation. Breast digital tomosynthesis reduces and/or eliminates overlapping obscuring breast tissue. Although there are some disadvantages with digital breast tomosynthesis, this relatively lost-cost technology may be used effectively in the screening and diagnostic settings.

Digital Breast Tomosynthesis: State of the Art

This topical review on digital breast tomosynthesis (DBT) is provided with the intent of describing the state of the art in terms of technology, results from recent clinical studies, advanced applications, and ongoing efforts to develop multimodality imaging systems that include DBT. Particular emphasis is placed on clinical studies. The observations of increase in cancer detection rates, particularly for invasive cancers, and the reduction in false-positive rates with DBT in prospective trials indicate its benefit for breast cancer screening. Retrospective multireader multicase studies show either noninferiority or superiority of DBT compared with mammography. Methods to curtail radiation dose are of importance. (©) RSNA, 2015.

False positives in breast cancer screening with one-view breast tomosynthesis: An analysis of findings leading to recall, work-up and biopsy rates in the Malmö Breast Tomosynthesis Screening Trial

Objectives

To analyse false positives (FPs) in breast cancer screening with tomosynthesis (BT) vs. mammography (DM).

Methods

The Malmö Breast Tomosynthesis Screening Trial (MBTST) is a prospective population-based study comparing one-view BT to DM in screening. This study is based on the first half of the MBTST population (n = 7,500). Differences in FP recall rate, findings leading to recall, work-up and biopsy rate between cases recalled on BT alone, DM alone and BT+DM were analysed.

Results

The FP recall rate was 1.7 % for BT alone (n = 131), 0.9 % for DM alone (n = 69) and 1.1 % for BT + DM (n = 81). The FP recall rate for BT alone was halved after the initial phase of the trial, stabilising at 1.5 %. BT doubled the recall of stellate distortions compared to DM (n = 64 vs. n = 33). There were fewer fibroadenomas and cysts, and the biopsy rate was slightly lower for FP recalled on BT alone compared to DM alone (15.3 % vs. 27.6 %: p = 0.037 and 33.8 % vs. 36.2 %; p = 0.641, respectively).

Conclusions

FPs increased with BT screening mainly due to the recall of stellate distortions. The FP recall rate was still well within the European guidelines and showed evidence of a learning curve. Characterisation of rounded lesions was improved with BT.

Key Points

• Tomosynthesis screening gave a higher false-positive recall rate than mammography

• There was a decline in the false-positive recall rate for tomosynthesis

• The recall due to stellate distortions simulating malignancy was doubled with tomosynthesis

• Tomosynthesis found more radial and postoperative scar tissue than mammography

• Tomosynthesis is better at characterising rounded lesions

Breast tomosynthesis using the multiple projection algorithm adapted for stationary detectors

OBJECTIVE

The aim of this study is to investigate the validity of using the Multiple Projection Algorithm (MPA) for Breast Tomosynthesis (BT) using real projection images acquired with phantoms at a clinical setting.

METHODS

The CIRS-BR3D phantom with ranging thicknesses between 3 cm and 6 cm was used for all image quality evaluations. Five sets of measurements were acquired, each comprised of a 2D mammographic image followed by a set of 25 projections within an arc length of 50°. A reconstruction algorithm based on the MPA was adapted for partial isocentric rotation using a stationary detector. For reference purposes, a Back Projection (BP) algorithm was also developed for this geometry. The performance of the algorithms was evaluated, in combination with pre-filtering of the projections, in comparative studies that involved also a comparison between tomosynthesis slices and 2D mammograms.

RESULTS

Evaluation of tomosynthesis slices reconstructed with BP and MPA showed close performance for the two algorithms with no considerable differences in feature detection, size and appearance of the background tissue with the MPA running faster the overall process. Pre-filtering of the projections, led to better BT images compared to non-filtering. Increased thickness resulted in limited detection of the features of interest, especially the smaller sized ones. In these cases, the filtered BT slices allowed improved visualization due to removed superimposed tissue compared to the 2D images. The different breast-like slab arrangements in phantoms of the same thickness demonstrated a slight influence on the quality of reconstructed features.

CONCLUSIONS

The MPA which had been applied previously to reconstruct tomograms from projections acquired at synchrotron facilities, is a time efficient algorithm, and is fully compliant with and can be successfully used in BT clinical systems. Compared to 2D mammography, BT shows advantage in visualizing features of small size and for increased phantom thickness or features within a dense background with superimposed structures.

Detection of masses in digital breast tomosynthesis using complementary information of simulated projection

PURPOSE

The purpose of this study is to develop a computer-aided detection system that combines the detection results in 3D digital breast tomosynthesis (DBT) volume and 2D simulated projection (synthesized image which is not provided by the vendor but generated from DBT volume in this study) to improve the accuracy of mass detection in DBT.

METHODS

The 3D DBT volume has a problem of blurring in the out-of-focus plane because it is reconstructed from a limited number of projection view images acquired over a limited angular range. To solve the problem, the simulated projection is generated by measuring the blurriness of voxels in the DBT volume and adopting conspicuity voxels. A contour-based detection algorithm is applied to detecting masses in the simulated projection. The DBT volume is analyzed by using an unsupervised mass detection algorithm, which results in mass candidates in the DBT volume. The mass likelihood scores estimated for mass candidates on the DBT volume and the simulated projection are merged in a probabilistic manner through a Bayesian network model to differentiate masses and false positives (FPs). Experiments were conducted on a clinical data set of 320 DBT volumes. In 90 volumes, at least one biopsy-proven malignant mass was presented. The longest diameter of masses ranged from 7.0 to 56.4 mm (mean = 25.4 mm). The sizes of masses in the data set were relatively large compared to the sizes of the masses reported in other detection studies. Three image quality measurements (overall sharpness, sharpness of mass boundary, and contrast) were used to evaluate the image quality of the simulated projection compared to the DBT central slice where the mass was most conspicuous and other projection methods (maximum intensity projection and average projection). A free-response receiver operating characteristic (FROC) analysis was adopted for evaluating the accuracy of mass detection in the DBT volume, the simulated projection, and the combined approach. A jackknife FROC analysis (JAFROC) was used to estimate the statistical significance of the difference between two FROC curves.

RESULTS

The overall sharpness and the sharpness of mass boundary in the simulated projection are higher than those in the DBT central slice and other projection methods. The contrast of the simulated projection is lower than the DBT central slice. The mass detection in the DBT volume achieved region-based sensitivities of 80% and 85% with 1.75 and 2.11 FPs per DBT volume. The proposed combined mass detection approach achieved same sensitivities with reduced FPs of 1.33 and 1.93 per DBT volume. The difference of the FROC curves between the combined approach and the mass detection in the DBT volume was statistically significant (p < 0.01) by JAFROC analysis.

CONCLUSIONS

This study indicates that the combined approach that merges the detection results in the DBT volume and the simulated projection is a promising approach to improve the accuracy of mass detection in DBT.

Comparison between two-dimensional synthetic mammography reconstructed from digital breast tomosynthesis and full-field digital mammography for the detection of T1 breast cancer

OBJECTIVE

To evaluate the interpretative performance of two-dimensional (2D) synthetic mammography (SM) reconstructed from digital breast tomosynthesis (DBT) in the detection of T1-stage invasive breast cancers, compared to 2D full-field digital mammography (FFDM).

METHODS

This retrospective study enrolled 214 patients. For each patient, FFDM and DBT were performed between January and June 2013, and SM was reconstructed from DBT data. Three radiologists interpreted images and recorded visibility scores and morphologies of cancers. Diagnostic performances of SM and FFDM were compared. Percentages of detected cancers and visibility scores were compared for tumour size, and presence of calcifications for each observer.

RESULTS

Observer sensitivity showed no difference for detection with SM and FFDM (P > 0.05). One observer showed a higher specificity (P = 0.02) and higher positive predictive value with SM (95 % CI 0.6-16.4), but the differences in the corresponding values between SM and FFDM for the other observers were not statistically significant. In subgroup analyses according to tumour size and presence of calcifications, percentages of detected cancers and visibility scores were not significantly different.

CONCLUSIONS

Diagnostic performances of SM and FFDM are comparable for detecting T1-stage breast cancers. Therefore, our results indicate that SM may eliminate the need for additional FFDM during DBT-based imaging.

KEY POINTS

• DBT plus FFDM increases radiation dose compared to FFDM alone. • Detecting T1-stage cancers with only SM is comparable to detection with FFDM. • Two-dimensional SM may replace dose-requiring FFDM in DBT-based imaging.

Accuracy of Digital Breast Tomosynthesis for Depicting Breast Cancer Subgroups in a UK Retrospective Reading Study (TOMMY Trial)

PURPOSE

To compare the diagnostic performance of two-dimensional (2D) mammography, 2D mammography plus digital breast tomosynthesis (DBT), and synthetic 2D mammography plus DBT in depicting malignant radiographic features.

MATERIALS AND METHODS

In this multicenter, multireader, retrospective reading study (the TOMMY trial), after written informed consent was obtained, 8869 women (age range, 29-85 years; mean, 56 years) were recruited from July 2011 to March 2013 in an ethically approved study. From these women, a reading dataset of 7060 cases was randomly allocated for independent blinded review of (a) 2D mammography images, (b) 2D mammography plus DBT images, and (c) synthetic 2D mammography plus DBT images. Reviewers had no access to results of previous examinations. Overall sensitivities and specificities were calculated for younger women and those with dense breasts.

RESULTS

Overall sensitivity was 87% for 2D mammography, 89% for 2D mammography plus DBT, and 88% for synthetic 2D mammography plus DBT. The addition of DBT was associated with a 34% increase in the odds of depicting cancer (odds ratio [OR] = 1.34, P = .06); however, this level did not achieve significance. For patients aged 50-59 years old, sensitivity was significantly higher (P = .01) for 2D mammography plus DBT than it was for 2D mammography. For those with breast density of 50% or more, sensitivity was 86% for 2D mammography compared with 93% for 2D mammography plus DBT (P = .03). Specificity was 57% for 2D mammography, 70% for 2D mammography plus DBT, and 72% for synthetic 2D mammography plusmDBT. Specificity was significantly higher than 2D mammography (P < .001in both cases) and was observed for all subgroups (P < .001 for all cases).

CONCLUSION

The addition of DBT increased the sensitivity of 2D mammography in patients with dense breasts and the specificity of 2D mammography for all subgroups. The use of synthetic 2D DBT demonstrated performance similar to that of standard 2D mammography with DBT. DBT is of potential benefit to screening programs, particularly in younger women with dense breasts. (©) RSNA, 2015.

Digital breast tomosynthesis: lessons learned from early clinical implementation

The limitations of mammography are well known and are partly related to the fact that with conventional imaging, the three-dimensional volume of the breast is imaged and presented in a two-dimensional format. Because normal breast tissue is similar in x-ray attenuation to some breast cancers, clinically relevant malignancies may be obscured by normal overlapping tissue. In addition, complex areas of normal tissue may be perceived as suspicious. The limitations of two-dimensional breast imaging lead to low sensitivity in detecting some cancers and high false-positive recall rates. Although mammographic screening has been shown to reduce breast cancer deaths by approximately 30%, controversy exists over when and how often screening mammography should occur. Digital breast tomosynthesis (DBT) is rapidly being implemented in breast imaging clinics around the world as early clinical data demonstrate that it may address some of the limitations of conventional mammography. With DBT, multiple low-dose x-ray images are acquired in an arc and reconstructed to create a three-dimensional image, thus minimizing the impact of overlapping breast tissue and improving lesion conspicuity. Early studies of screening DBT have shown decreased false-positive callback rates and increased rates of cancer detection (particularly for invasive cancers), resulting in increased sensitivity and specificity. In our clinical practice, we have completed more than 2 years of using two-view digital mammography combined with two-view DBT for all screening and select diagnostic imaging examinations (over 25,000 patients). Our experience, combined with previously published data, demonstrates that the combined use of DBT and digital mammography is associated with improved outcomes for screening and diagnostic imaging. Online supplemental material is available for this article.

Large area CMOS active pixel sensor x-ray imager for digital breast tomosynthesis: Analysis, modeling, and characterization

PURPOSE

Large area x-ray imagers based on complementary metal-oxide-semiconductor (CMOS) active pixel sensor (APS) technology have been proposed for various medical imaging applications including digital breast tomosynthesis (DBT). The low electronic noise (50-300 e-) of CMOS APS x-ray imagers provides a possible route to shrink the pixel pitch to smaller than 75 μm for microcalcification detection and possible reduction of the DBT mean glandular dose (MGD).

METHODS

In this study, imaging performance of a large area (29×23 cm2) CMOS APS x-ray imager [Dexela 2923 MAM (PerkinElmer, London)] with a pixel pitch of 75 μm was characterized and modeled. The authors developed a cascaded system model for CMOS APS x-ray imagers using both a broadband x-ray radiation and monochromatic synchrotron radiation. The experimental data including modulation transfer function, noise power spectrum, and detective quantum efficiency (DQE) were theoretically described using the proposed cascaded system model with satisfactory consistency to experimental results. Both high full well and low full well (LFW) modes of the Dexela 2923 MAM CMOS APS x-ray imager were characterized and modeled. The cascaded system analysis results were further used to extract the contrast-to-noise ratio (CNR) for microcalcifications with sizes of 165-400 μm at various MGDs. The impact of electronic noise on CNR was also evaluated.

RESULTS

The LFW mode shows better DQE at low air kerma (Ka<10 μGy) and should be used for DBT. At current DBT applications, air kerma (Ka∼10 μGy, broadband radiation of 28 kVp), DQE of more than 0.7 and ∼0.3 was achieved using the LFW mode at spatial frequency of 0.5 line pairs per millimeter (lp/mm) and Nyquist frequency ∼6.7 lp/mm, respectively. It is shown that microcalcifications of 165-400 μm in size can be resolved using a MGD range of 0.3-1 mGy, respectively. In comparison to a General Electric GEN2 prototype DBT system (at MGD of 2.5 mGy), an increased CNR (by ∼10) for microcalcifications was observed using the Dexela 2923 MAM CMOS APS x-ray imager at a lower MGD (2.0 mGy).

CONCLUSIONS

The Dexela 2923 MAM CMOS APS x-ray imager is capable to achieve a high imaging performance at spatial frequencies up to 6.7 lp/mm. Microcalcifications of 165 μm are distinguishable based on reported data and their modeling results due to the small pixel pitch of 75 μm. At the same time, potential dose reduction is expected using the studied CMOS APS x-ray imager.