2D Digital Breast Tomosynthesis Research Articles

Synthesising two-dimensional mammographic images using compressed sensing-reconstructed digital breast tomosynthesis images

This study presents a method synthesizing a two-dimensional (2D) mammographic image of reasonable quality directly from a 3D digital breast tomosynthesis (DBT) image, reconstructed using an advanced compressed sensing (CS)-based algorithm. This approach aims to reduce the radiation dose required for complementary imaging technologies of digital mammography (DM) and DBT, eliminating the need for additional DM examinations. The method involves three main steps: projection data acquisition from a DBT system, CS-based DBT reconstruction, and synthesis of a 2D mammographic image from the reconstructed DBT image. To verify the efficacy of the proposed method, we conducted both a simulation and an experiment on a numerical breast and commercially available BR3D phantoms, respectively, prior to practical implementation in real-world DBT systems. Our simulation and experimental results indicated that the CS-based algorithm yielded markedly improved DBT reconstruction quality, preserving superior image homogeneity, better edge contour and sharpening, and fewer image artifacts. The measured contrast-to-noise ratio and structural similarity values of the CS-reconstructed DBT images were 10.31 and 0.78, respectively, which were approximately 2.2 and 3.1 times larger, respectively, than those of the filtered-backprojection-reconstructed DBT images in the simulation. The quality of the synthetic mammographic images using the CS-reconstructed DBT images was similar to that of conventional mammographic images obtained using a full dose, indicating the efficacy of the proposed method. Consequently, we successfully reconstructed DBT images of substantially high quality using the CS-based algorithm and synthesised 2D mammographic images of reasonable quality, potentially reducing the radiation dose to patients in the complementary imaging technologies of DM and DBT.

Diagnostic accuracy of supplemental three-dimensional breast ultrasound in the work-up of BI-RADS 0 screening recalls

ObjectiveTo evaluate the diagnostic accuracy of supplemental 3D automated breast ultrasound (ABUS) in the diagnostic work-up of BI-RADS 0 recalls. We hypothesized that 3D ABUS may reduce the benign biopsy rate.Materials and methodsIn this prospective multicenter diagnostic study, screening participants recalled after a BI-RADS 0 result underwent bilateral 3D ABUS supplemental to usual care: digital breast tomosynthesis (DBT) and targeted hand-held ultrasound (HHUS). Sensitivity, specificity, positive predictive value, and negative predictive value of 3D ABUS, and DBT plus HHUS, were calculated. New 3D ABUS findings and changes of management (biopsy or additional imaging) were recorded.ResultsA total of 501 women (median age 55 years, IQR [51–64]) with 525 BI-RADS 0 lesions were included between April 2018 and March 2020. Cancer was diagnosed in 45 patients. 3D ABUS sensitivity was 72.1% (95% CI [57.2–83.4%]), specificity 84.4% (95% CI [80.8–87.4%]), PPV 29.2% (95% CI [21.4–38.5%]), and NPV 97.1% 95.0–98.4%). Sensitivity of DBT plus HHUS was 100% (95% CI [90.2–100%]), specificity 71.4% (95% CI [67.2–75.2%]), PPV 23.8% (95% CI [18.1–30.5%]) and NPV 100% (95% CI [98.7–100%]). Twelve out of 43 (27.9%) malignancies in BI-RADS 0 lesions were missed on 3D ABUS, despite being detected on DBT and/or HHUS. Supplemental 3D ABUS resulted in the detection of 57 new lesions and six extra biopsy procedures, all were benign.Conclusion3D ABUS in the diagnostic work-up of BI-RADS 0 recalls may miss over a quarter of cancers detected with HHUS and/or DBT and should not be used to omit biopsy. Supplemental 3D ABUS increases the benign biopsy rate.Trial registrationDutch Trial Register, available via https://www.onderzoekmetmensen.nl/en/trial/29659Critical relevance statementSupplemental 3D automated breast ultrasound in the work-up of BI-RADS 0 recalls may miss over a quarter of cancers detected with other methods and should not be used to omit biopsy; ABUS findings did increase benign biopsy rate.Key PointsAutomated breast ultrasound (ABUS) may miss over 25% of cancers detectable by alternative methods.Don’t rely solely on 3D ABUS to assess indication for biopsy.New findings with supplemental 3D ABUS increase the benign biopsy rate.Graphical

Role of tomosynthesis and ultrasound in the assessment of asymmetric breast densities: a comparative prospective study

BackgroundTomosynthesis is a recent advancement of full-field digital mammography involves transforming two-dimensional (2D) breast images into three dimensions (3D) images. It reduces the adverse effect of tissue superimposition on conventional 2D- mammography, therefore having high potential enhancing identification and assessment of asymmetric breast densities. The aim of the study was to assess and compare the diagnostic performance of breast ultrasound and 3D digital breast tomosynthesis in the assessment of asymmetric breast densities.ResultsIn the current study, 80 patients with 80 mammographically and/or tomosynthesized breast asymmetries were included. The patients' ages ranged from 30 to 70 years old, with a mean age of 47.2 ± 9.2 SD. Breast ultrasound outperformed digital breast tomosynthesis in terms of diagnostic performance. Tomosynthesis had a sensitivity of 86.4%, specificity of 93.1%, positive predictive value of 82.6%, negative predictive value of 94.7%, and accuracy of 91.3% compared to ultrasounds' sensitivity of 100.00%, specificity of 93.1%, positive predictive value of 84.6%, negative predictive value of 100.00%, and accuracy of 95%.ConclusionsIncorporating ultrasonography in the assessment of asymmetric breast densities outperformed tomosynthesis and shown to be more precise in characterisation of lesions underlying asymmetric breast density.

Abstract 1929: Deep-learning-enabled volumetric breast density estimation with digital breast tomosynthesis

Abstract Background: It has been widely established that breast density is an independent breast cancer risk factor. With the increasing utilization of digital breast tomosynthesis (DBT) in breast cancer screening, there is an opportunity to estimate volumetric breast density (VBD) routinely. However, current available methods extrapolate VBD from 2D images acquired with DBT and/or depend on the existence of raw DBT data which is rarely archived by clinical centers due to cost and storage constraints. This study aims to harness deep learning to develop a computational tool for VBD assessment based solely on 3D reconstructed, “for presentation” DBT images. Methods: We retrospectively analyzed 1,080 negative DBT screening exams (09/20/2011 - 11/25/2016) from the Hospital of the University of Pennsylvania (mean age ± SD, 57 ± 11 years; mean BMI ± SD, 28.7 ± 7.1 kg/m2; racial makeup, 41.2% White, 54.2% Black, 4.6% Other), for which both 3D reconstructed and 2D raw DBT images (Selenia Dimensions, Hologic Inc) were available. All available standard views (left and right mediolateral-oblique and cranio-caudal views) were included for each exam, leading to 7,850 DBT views. Corresponding 3D reference-standard tissue segmentations were generated from a previously validated software that uses both 3D reconstructed slices and raw 2D DBT data to provide VBD metrics, shown to be strongly correlated with VBD measures from MRI image volumes. We based our deep learning algorithm on the U-Net architecture within the open-source Generally Nuanced Deep Learning Framework (GaNDLF) and created a 3-label image segmentation task (background, dense tissue, and fatty tissue). Our dataset was randomly split into training (70%), validation (15%) and test (15%) sets, while ensuring that all views of the same DBT exam were assigned to the same set. The performance of our deep learning algorithm against the corresponding reference-standard segmentations was measured in terms of Dice scores (DSC), with 0 signifying no overlap and 1 signifying perfect overlap, overall, as well as separately for each label. Results: After training was complete, our deep learning algorithm achieved a DSC of 0.78 on the validation, as well as on the test set. Our method accurately segmented background from breast tissue (DSC = 0.94) and demonstrated moderate to high performance in segmenting dense and fatty tissue, respectively (DSC = 0.49 and 0.89). Conclusion: Our preliminary analysis suggests that deep learning shows promise in the estimation of VBD using 3D DBT reconstructed, “for presentation” images. Future work involving transfer learning based on ground truth masks by clinical radiologists could further enhance this method’s performance. In view of rapid clinical conversion to DBT screening, such a tool has the potential to enable large retrospective epidemiologic and personalized risk assessment studies of breast density with DBT. Citation Format: Vinayak S. Ahluwalia, Walter Mankowski, Sarthak Pati, Spyridon Bakas, Ari Brooks, Celine M. Vachon, Emily F. Conant, Aimilia Gastounioti, Despina Kontos. Deep-learning-enabled volumetric breast density estimation with digital breast tomosynthesis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1929.

Artificial-intelligence-driven volumetric breast density estimation with digital breast tomosynthesis in a racially diverse screening cohort.

e13538 Background: Breast density is considered a well-established breast cancer risk factor. As quasi-3D, digital breast tomosynthesis (DBT) becomes increasingly utilized for screening, there is an opportunity to routinely estimate volumetric breast density (VBD). However, current methods extrapolate VBD from 2D images acquired with DBT and/or depend on the existence of raw DBT data, which is rarely archived due to cost and storage constraints. Using a racially diverse screening cohort, this study evaluates the potential of deep learning for VBD assessment based solely on 3D reconstructed, “for presentation” DBT images. Methods: We retrospectively analyzed 1,080 negative DBT screening exams obtained between 2011 and 2016 from the Hospital of the University of Pennsylvania (racial makeup, 41.2% White, 54.2% Black, 4.6% Other; mean age ± SD, 57 ± 11 years; mean BMI ± SD, 28.7 ± 7.1 kg/m2), for which both 2D raw and 3D reconstructed DBT images (Selenia Dimensions, Hologic Inc) were available. Corresponding 3D reference-standard tissue segmentations were generated from previously validated software that uses both 3D reconstructed slices and raw 2D DBT data to provide VBD metrics, shown to be strongly correlated with VBD measures from MRI image volumes. We based our deep learning algorithm on the U-Net architecture within the open-source Generally Nuanced Deep Learning Framework (GaNDLF) and created a 3-label image segmentation task (background, dense tissue, and fatty tissue). Our dataset was randomly split into training (70%), validation (15%) and test (15%) sets. We report on the performance of our deep learning algorithm against corresponding reference-standard segmentations for a cranio-caudal (CC) view-only subset. We also stratify our results by the two main racial groups (White and Black). Our evaluation measure was the weighted Dice score (DSC), with 0 signifying no overlap and 1 signifying perfect overlap, overall and separately for each label. Results: Our deep learning algorithm achieved an overall DSC of 0.682 (STD = 0.136). It accurately segmented the three labels of background, fatty tissue, and dense tissue, with DSC scores of 0.995, 0.884, and 0.617, respectively. DSC for White and Black women were 0.688 (STD = 0.127) and 0.680 (STD = 0.146), respectively. Conclusions: Our preliminary analysis suggests that deep learning shows promise in the estimation of VBD using 3D DBT reconstructed, “for presentation” CC view images and does not demonstrate bias among racial groups. Future work involving optimization of performance in other breast views as well as transfer learning based on ground truth masks by clinical radiologists could further enhance this method. In view of rapid clinical conversion to DBT screening, such a tool has the potential to enable large retrospective epidemiological and personalized risk assessment studies of breast density with DBT.

Breast Radiation Exposure of 3D Digital Breast Tomosynthesis Compared to Full-Field Digital Mammography in a Clinical Follow-Up Setting.

According to a position paper of the European Commission Initiative on Breast Cancer (ECIBC), DBT is close to being introduced in European breast cancer screening programmes. Our study aimed to examine radiation dose delivered by digital breast tomosynthesis (DBT) and digital mammography (FFDM) in comparison to sole FFDM in a clinical follow-up setting and in an identical patient cohort. Retrospectively, 768 breast examinations of 96 patients were included. Patients received both DBT and FFDM between May 2015 and July 2019: (I) FFDM in cranio-caudal (CC) and DBT in mediolateral oblique (MLO) view, as well as a (II) follow-up examination with FFDM in CC and MLO view. The mean glandular dose (MGD) was determined by the mammography system according to Dance’s model. The MGD (standard deviation (SD), interquartile range (IQR)) was distributed as follows: (I) (CCFFDM+MLODBT) (a) left FFDMCC 1.40 mGy (0.36 mGy, 1.13–1.59 mGy), left DBTMLO 1.62 mGy (0.51 mGy, 1.27–1.82 mGy); (b) right FFDMCC 1.36 mGy (0.34 mGy, 1.14–1.51 mGy), right DBTMLO 1.59 mGy (0.52 mGy, 1.27–1.62 mGy). (II) (CCFFDM+MLOFFDM) (a) left FFDMCC 1.35 mGy (0.35 mGy, 1.10–1.60 mGy), left FFDMMLO 1.40 mGy (0.39 mGy, 1.12–1.59 mGy), (b) right FFDMCC 1.35 mGy (0.33 mGy, 1.12–1.48 mGy), right FFDMMLO 1.40 mGy (0.36 mGy, 1.14–1.58 mGy). MGD was significantly higher for DBT mlo views compared to FFDM (p < 0.001). Radiation dose was significantly higher for DBT in MLO views compared to FFDM. However, the MGD of DBT MLO lies below the national diagnostic reference level of 2 mGy for an FFDM view. Hence, our results support the use of either DBT or FFDM as suggested in the ECIBC’s Guidelines.

Comparison of interval breast cancers with 2D digital mammography versus 3D digital breast tomosynthesis in a large community-based practice.

Incidence of interval cancers is an important outcome in assessing efficacy of screening. Our primary objective was to compare the incidence of interval cancers detected with two-dimensional digital mammography (DM) versus digital breast tomosynthesis (DBT) in a large community health system. Our secondary objectives were to compare the patient and tumor characteristics of interval cancers, cancer detection rate, and recall rate. Interval cancers before and after implementation of DBT (2012-2014 DM group; 2016-2018 DBT group) were reviewed. Patient factors (age, race, breast density, personal history of breast cancer, family history of breast cancer, known BRCA-1 or BRCA-2 genetic mutation, baseline mammogram, and presentation) and tumor characteristics (in situ versus invasive, grade, size, hormone receptor status, and nodal status) were compared with the chi-squared test or the MidP exact test. Rates (detection and recall) were compared using a z-score. The rates of interval cancers with DM (0.30 per 1000 [35/117099]) and DBT (0.33 per 1000 [40/119746]) were similar (P=.3). Proportion of node-positive interval cancers was lower in the DBT group (22.9% [8/35] vs 48% [15/31], p.01). Otherwise, the patient and tumor characteristics were similar. The cancer detection rate increased (5.9 per 1000 [709/119746] vs 3.5 per 1000 [411/117099], P=.0001), and the recall rate decreased with DBT (8.6% [10347/119746] versus 10.7% [12508/117099], (P<.0001). Although the cancer detection rate was higher with DBT, the rate of interval breast cancers was similar in both groups. Node-positive invasive interval cancers were decreased with DBT.

3D digital breast tomosynthesis versus US in evaluating breast asymmetries

BackgroundDigital breast tomosynthesis (DBT) now become one of the available diagnostic imaging modalities of the breast, and the present study was done to evaluate its diagnostic value versus that of breast ultrasound (US) in the evaluation of breast asymmetries. This study included 51 patients with 57 mammography identified breast asymmetries; their ages were ranged from 26 to 72 years (mean age 50.05 ± 8.1 SD). For all patients, both digital breast tomosynthesis and ultrasound were done, and their results were compared.ResultsTomosynthesis in this study showed better diagnostic performance compared to mammography; the sensitivity of tomosynthesis was 83.33%, the specificity was 89.74%, the positive predictive value was 78.95%, the negative predictive value was 92.11%, and the accuracy was 87.71% while the sensitivity of mammography was 72.22%, the specificity was 71.79%, the positive predictive value was 54.17%, the negative predictive value was 84.85 %, and the accuracy was 71.92 %.Breast ultrasound showed the highest sensitivity in this study with the sensitivity of ultrasound being 100.00 %, the specificity being 92.31%, the positive predictive value being 85.71%, the negative predictive value being 100.00%, and the accuracy being 94.73%.ConclusionTomosynthesis enables better depiction of asymmetries. It can be useful in the screening setting where better lesion detection and accurate description of lesions is desired. Therefore, it can detect more cancers and can reduce the number of biopsies.Breast ultrasound should be coupled with breast mammography and 3D tomosynthesis in the evaluation of the breast asymmetries as it reduces false-negative results, detects solid and cystic lesions, and assesses solid lesion nature.

Abstract PD9-03: A combination of intra- and peri-lesional deep learning classifiers from multiple views enables accurate diagnosis of architectural distortion malignancy with digital breast tomosynthesis

Abstract Background: 3D digital breast tomosynthesis (DBT) offers greater sensitivity than 2D mammography to the presence of architectural distortions (AD), a suspicious finding requiring biopsy. This higher sensitivity to AD is only beneficial if DBT can differentiate benign AD, such as radial scars (RS) from malignant AD (MAD). Automated analysis of clinical images using a neural network has potential to enhance the accuracy of breast cancer diagnosis, but such applications focus on screening and currently struggle with more challenging diagnoses (e.g. MAD vs. RS) where computer assistance would provide great clinical utility. In this work, we define a deep learning (DL) framework utilizing amalgamate predictions from a group of neural networks, specialized for multiple lesion regions and different acquisitional views to distinguish MAD from RS. Methods: A retrospective analysis was conducted of 69 patients screened at a single institution, each with AD visible on one or more DBT views. Stereotactic core biopsy determined 42 AD to be MAD and the remaining 27 to be RS. An attending breast radiologist manually identified a circular region of interest (ROI) containing the AD on a single slice of the mediolateral (ML, n=72) and/or craniocaudal (CC, n=68) views. 56 patients were utilized for training and tuning the DL networks in cross-validation, while 25 volumes from 13 patients (8 MAD, 5 RS) were held for independent testing. Volumetric samples of size 24x24x12 pixels were extracted randomly within each ROI for input to the networks. Two neural networks were trained to separately recognize patterns of MAD at the intra- and peri-lesional region. Pairs of networks were trained separately for analysis of the CC and the MLO view, resulting in 4 possible network variations. These networks were first assessed individually by cross-validation in the training set, then predictions from each were combined for diagnosis in the independent testing set. Performance was assessed by area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Results: All networks individually achieved strong performance in distinguishing RS from MAD when assessed via cross-validation in the training set. On the independent test set, networks trained to recognize patterns of malignancy at the center of the distortion outperformed those focused on the lesion margin. The CC view was found to be most discriminating in neural network assessment. Independent Test PerformanceNetwork TypeAUCSensitivitySpecificityIntralesional, CC0.650.800.50Perilesional, CC0.510.400.63Intralesional, ML0.490.600.38Perilesional, ML0.550.600.50Combined Performance0.800.601.0 When predictions from these networks were combined for diagnosis on the independent validation set, performance increased significantly to an AUC of 0.8, with a sensitivity of 0.6 and a specificity of 1.0. Conclusions: In this investigation, we demonstrated that specialized deep learning classifiers, created to address the diagnosis of MAD vs RS, can assist in diagnostic settings without generating false negatives. Our approach combines neural networks to make a diagnosis based on various spatial regions from multiple DBT views. Our approach demonstrated very high specificity which may reduce the number of benign RS biopsies and surgical excisions for AD detected by DBT. Future work will further validate these findings on a larger, multi-institutional cohort and explore their influence on clinical decision-making. Citation Format: Tristan Maidment, Nathaniel Braman, Yijiang Chen, Farhad Mehrkhani, Uliyana Yankevich, Donna Plecha, Anant Madabhushi. A combination of intra- and peri-lesional deep learning classifiers from multiple views enables accurate diagnosis of architectural distortion malignancy with digital breast tomosynthesis [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr PD9-03.

P108 - Analysis of the utility of 3D digital breast tomosynthesis

P108 - Analysis of the utility of 3D digital breast tomosynthesis

Computer-aided detection of mass in digital breast tomosynthesis using a faster region-based convolutional neural network.

Digital breast tomosynthesis (DBT) is a newly developed three-dimensional tomographic imaging modality in the field of breast cancer screening designed to alleviate the limitations of conventional digital mammography-based breast screening methods. A computer-aided detection (CAD) system was designed for masses in DBT using a faster region-based convolutional neural network (faster-RCNN). To this end, a data set was collected, including 89 patients with 105 masses. An efficient detection architecture of convolution neural network with a region proposal network (RPN) was used for each slice to generate region proposals (i.e., bounding boxes) with a mass likelihood score. In each DBT volume, a slice fusion procedure was used to merge the detection results on consecutive 2D slices into one 3D DBT volume. The performance of the CAD system was evaluated using free-response receiver operating characteristic (FROC) curves. Our RCNN-based CAD system was compared with a deep convolutional neural network (DCNN)-based CAD system. The RCNN-based CAD generated a performance with an area under the ROC (AUC) of 0.96, whereas the DCNN-based CAD achieved a performance with AUC of 0.92. For lesion-based mass detection, the sensitivity of RCNN-based CAD was 90% at 1.54 false positive (FP) per volume, whereas the sensitivity of DCNN-based CAD was 90% at 2.81 FPs/volume. For breast-based mass detection, RCNN-based CAD generated a sensitivity of 90% at 0.76 FP/breast, which is significantly increased compared with the DCNN-based CAD with a sensitivity of 90% at 2.25 FPs/breast. The results suggest that the faster R-CNN has the potential to augment the prescreening and FP reduction in the CAD system for masses.

Interactions of lesion detectability and size across single-slice DBT and 3D DBT.

Three dimensional image modalities introduce a new paradigm for visual search requiring visual exploration of a larger search space than 2D imaging modalities. The large number of slices in the 3D volumes and the limited reading times make it difficult for radiologists to explore thoroughly by fixating with their high resolution fovea on all regions of each slice. Thus, for 3D images, observers must rely much more on their visual periphery (points away from fixation) to process image information. We previously found a dissociation in signal detectability between 2D and 3D search tasks for small signals in synthetic textures evaluated with non-radiologist trained observers. Here, we extend our evaluation to more clinically realistic backgrounds and radiologist observers. We studied the detectability of simulated microcalcifications (MCALC) and masses (MASS) in Digital Breast Tomosynthesis (DBT) utilizing virtual breast phantoms. We compared the lesion detectability of 8 radiologists during free search in 3D DBT and a 2D single-slice DBT (center slice of the 3D DBT). Our results show that the detectability of the microcalcification degrades significantly in 3D DBT with respect to the 2D single-slice DBT. On the other hand, the detectability for masses does not show this behavior and its detectability is not significantly different. The large deterioration of the 3D detectability of microcalcifications relative to masses may be related to the peripheral processing given the high number of cases in which the microcalcification was missed and the high number of search errors. Together, the results extend previous findings with synthetic textures and highlight how search in 3D images is distinct from 2D search as a consequence of the interaction between search strategies and the visibility of signals in the visual periphery.

Foveated Model Observers to predict human performance in 3D images.

We evaluate 3D search requires model observers that take into account the peripheral human visual processing (foveated models) to predict human observer performance. We show that two different 3D tasks, free search and location-known detection, influence the relative human visual detectability of two signals of different sizes in synthetic backgrounds mimicking the noise found in 3D digital breast tomosynthesis. One of the signals resembled a microcalcification (a small and bright sphere), while the other one was designed to look like a mass (a larger Gaussian blob). We evaluated current standard models observers (Hotelling; Channelized Hotelling; non-prewhitening matched filter with eye filter, NPWE; and non-prewhitening matched filter model, NPW) and showed that they incorrectly predict the relative detectability of the two signals in 3D search. We propose a new model observer (3D Foveated Channelized Hotelling Observer) that incorporates the properties of the visual system over a large visual field (fovea and periphery). We show that the foveated model observer can accurately predict the rank order of detectability of the signals in 3D images for each task. Together, these results motivate the use of a new generation of foveated model observers for predicting image quality for search tasks in 3D imaging modalities such as digital breast tomosynthesis or computed tomography.

Increased Cancer Detection Rate and Variations in the Recall Rate Resulting from Implementation of 3D Digital Breast Tomosynthesis into a Population-based Screening Program.

HomeRadiologyVol. 280, No. 3 PreviousNext CommunicationsFree AccessErrataIncreased Cancer Detection Rate and Variations in the Recall Rate Resulting from Implementation of 3D Digital Breast Tomosynthesis into a Population-based Screening ProgramRichard E. Sharpe, Jr , Shambavi Venkataraman, Jordana Phillips, Vandana Dialani, Valerie J. Fein-Zachary, Seema Prakash, Priscilla J. Slanetz, Tejas S. MehtaRichard E. Sharpe, Jr , Shambavi Venkataraman, Jordana Phillips, Vandana Dialani, Valerie J. Fein-Zachary, Seema Prakash, Priscilla J. Slanetz, Tejas S. MehtaRichard E. Sharpe, Jr Shambavi VenkataramanJordana PhillipsVandana DialaniValerie J. Fein-ZacharySeema PrakashPriscilla J. SlanetzTejas S. MehtaPublished Online:Aug 17 2016https://doi.org/10.1148/radiol.2016164018MoreSectionsPDF ToolsImage ViewerAdd to favoritesCiteTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked In Originally published in:Radiology 2016;278(3):698–706 DOI:10.1148/radiol.2015142036Erratum in:Radiology 2016;280(2):981 DOI:10.1148/radiol.2016164018Last paragraph of “Screening Protocol and Examination Interpretation,” the following clarification should be added: These examinations were excluded from RR variation analysis because of the low sample size and heterogeneity of this cohort.Article HistoryPublished online: Aug 17 2016Published in print: Sept 2016 FiguresReferencesRelatedDetailsCited ByDifferential detection by breast density for digital breast tomosynthesis versus digital mammography population screening: a systematic review and meta-analysisTongLi, NehmatHoussami, NaomiNoguchi, AileenZeng, M. LukeMarinovich2022 | British Journal of Cancer, Vol. 127, No. 1Digital breast tomosynthesis (DBT)-guided biopsy of calcifications: pearls and pitfallsSadiaChoudhery, TaraAnderson, ElizabethValencia2021 | Clinical Imaging, Vol. 72Comparison of two-view versus single-view digital breast tomosynthesis and 2D-mammography in breast cancer surveillance imagingAndriaHadjipanteli, PetrosPolyviou, IliasKyriakopoulos, MariosGenagritis, NatasaKotziamani, DemetrisMoniatis, AnnePapoutsou, AnastasiaConstantinidou, Gayle E.Woloschak2021 | PLOS ONE, Vol. 16, No. 9Optimization of the Radiation Dose of Digital Breast Tomosynthesis in Opportunistic Screening by Studying the Effect of Different Combinations of FFDM and DBT ViewsMeihongSheng, JuanJi, ChenyingZhang, ZiruiZhang, ShenchuGong, YihuaLu2021 | International Journal of General Medicine, Vol. Volume 14Malignant Outcomes of Architectural Distortion on Tomosynthesis: A Systematic Review and Meta-AnalysisSadiaChoudhery, Matthew P.Johnson, Nicholas B.Larson, TaraAnderson2021 | American Journal of Roentgenology, Vol. 217, No. 2Tomosynthesis-guided breast and axillary localizations: tips and tricksSadiaChoudhery, TaraAnderson2020 | The British Journal of Radiology, Vol. 93, No. 1114Prone Versus Upright Digital Tomosynthesis–Guided BiopsySadiaChoudhery, MatthewJohnson, Robert T.Fazzio2020 | American Journal of Roentgenology, Vol. 215, No. 3Masses in the era of screening tomosynthesis: Is diagnostic ultrasound sufficient?SadiaChoudhery, JessicaAxmacher, Amy LynnConners, JenniferGeske, KathyBrandt2019 | The British Journal of Radiology, Vol. 92, No. 1096Accompanying This ArticleIncreased Cancer Detection Rate and Variations in the Recall Rate Resulting from Implementation of 3D Digital Breast Tomosynthesis into a Population-based Screening ProgramOct 9 2015RadiologyRecommended Articles Clinical Performance of Synthesized Two-dimensional Mammography Combined with Tomosynthesis in a Large Screening PopulationRadiology2017Volume: 283Issue: 1pp. 70-76Implementation of Synthesized Two-dimensional Mammography in a Population-based Digital Breast Tomosynthesis Screening ProgramRadiology2016Volume: 281Issue: 3pp. 730-736Does Reader Performance with Digital Breast Tomosynthesis Vary according to Experience with Two-dimensional Mammography?Radiology2017Volume: 283Issue: 2pp. 371-380Comparative Benefit-to–Radiation Risk Ratio of Molecular Breast Imaging, Two-Dimensional Full-Field Digital Mammography with and without Tomosynthesis, and Synthetic Mammography with TomosynthesisRadiology: Imaging Cancer2019Volume: 1Issue: 1Breast MRI Finds More Invasive Cancers than Digital Breast Tomosynthesis in Women with Dense Breasts Undergoing ScreeningRadiology: Imaging Cancer2020Volume: 2Issue: 4See More RSNA Education Exhibits Digital Breast Tomosynthesis: An Update on the State of the Technology, Evidence, and Clinical PracticeDigital Posters2019Integrating Digital Breast Tomosynthesis into a Hybrid Academic-Private PracticeDigital Posters2019Abbreviated Breast MRI: Past, Present, and FutureDigital Posters2019 RSNA Case Collection Invasive Lobular CarcinomaRSNA Case Collection2021Breast edemaRSNA Case Collection2021Slow-growing cancerRSNA Case Collection2020 Vol. 280, No. 3 Metrics Altmetric Score PDF download

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

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. 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. 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. 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.

Calibration and optimization of 3D digital breast tomosynthesis guided near infrared spectral tomography.

Calibration of a three-dimensional multimodal digital breast tomosynthesis (DBT) x-ray and non-fiber based near infrared spectral tomography (NIRST) system is challenging but essential for clinical studies. Phantom imaging results yielded linear contrast recovery of total hemoglobin (HbT) concentration for cylindrical inclusions of 15 mm, 10 mm and 7 mm with a 3.5% decrease in the HbT estimate for each 1 cm increase in inclusion depth. A clinical exam of a patient's breast containing both benign and malignant lesions was successfully imaged, with greater HbT was found in the malignancy relative to the benign abnormality and fibroglandular regions (11 μM vs. 9.5 μM). Tools developed improved imaging system characterization and optimization of signal quality, which will ultimately improve patient selection and subsequent clinical trial results.

The effect of amorphous selenium detector thickness on dual-energy digital breast imaging.

Contrast enhanced (CE) imaging techniques for both planar digital mammography (DM) and three-dimensional (3D) digital breast tomosynthesis (DBT) applications requires x-ray photon energies higher than the k-edge of iodine (33.2 keV). As a result, x-ray tube potentials much higher (>40 kVp) than those typical for screening mammography must be utilized. Amorphous selenium (a-Se) based direct conversion flat-panel imagers (FPI) have been widely used in DM and DBT imaging systems. The a-Se layer is typically 200 μm thick with quantum detective efficiency (QDE) >87% for x-ray energies below 26 keV. However, QDE decreases substantially above this energy. To improve the object detectability of either CE-DM or CE-DBT, it may be advantageous to increase the thickness (dSe) of the a-Se layer. Increasing the dSe will improve the detective quantum efficiency (DQE) at the higher energies used in CE imaging. However, because most DBT systems are designed with partially isocentric geometries, where the gantry moves about a stationary detector, the oblique entry of x-rays will introduce additional blur to the system. The present investigation quantifies the effect of a-Se thickness on imaging performance for both CE-DM and CE-DBT, discussing the effects of improving photon absorption and blurring from oblique entry of x-rays. In this paper, a cascaded linear system model (CLSM) was used to investigate the effect of dSe on the imaging performance (i.e., MTF, NPS, and DQE) of FPI in CE-DM and CE-DBT. The results from the model are used to calculate the ideal observer signal-to-noise ratio, d', which is used as a figure-of-merit to determine the total effect of increasing dSe for CE-DM and CE-DBT. The results of the CLSM show that increasing dSe causes a substantial increase in QDE at the high energies used in CE-DM. However, at the oblique projection angles used in DBT, the increased length of penetration through a-Se introduces additional image blur. The reduced MTF and DQE at high spatial frequencies lead to reduced two-dimensional d'. These losses in projection image resolution may subsequently result in a decrease in the 3D d', but the degree of which is largely dependent on the DBT reconstruction algorithm. For a filtered backprojection (FBP) algorithm with spectral apodization and slice-thickness filters, which dominate the blur for reconstructed images at oblique angles, the effect of oblique entry of x-rays on 3D d' is minimal. Thus, increasing dSe results in an improvement in d' for both CE-DM and CE-DBT with typical FBP reconstruction parameters. Increased dSe improves CE breast imaging performance by increasing QDE of detectors at higher energies, e.g., 49 kVp. Although there is additional blur in the oblique angled projections of a DBT scan, the overall 3D d' for DBT is not degraded because the dominant source blur at these angles results from the reconstruction filters of the employed FBP algorithm.

3D digital breast tomosynthesis image reconstruction using anisotropic total variation minimization.

This paper presents a compressed sensing based reconstruction method for 3D digital breast tomosynthesis (DBT) imaging. Algebraic reconstruction technique (ART) has been in use in DBT imaging by minimizing the isotropic total variation (TV) of the reconstructed image. The resolution in DBT differs in sagittal and axial directions which should be encountered during the TV minimization. In this study we develop a 3D anisotropic TV (ATV) minimization by considering the different resolutions in different directions. A customized 3D Shepp-logan phantom was generated to mimic a real DBT image by considering the overlapping tissue and directional resolution issues. Results of the ART, ART+3D TV and ART+3D ATV are compared using structural similarity (SSIM) diagram.

Multichannel response analysis on 2D projection views for detection of clustered microcalcifications in digital breast tomosynthesis

To investigate the feasibility of a new two-dimensional (2D) multichannel response (MCR) analysis approach for the detection of clustered microcalcifications (MCs) in digital breast tomosynthesis (DBT). With IRB approval and informed consent, a data set of two-view DBTs from 42 breasts containing biopsy-proven MC clusters was collected in this study. The authors developed a 2D approach for MC detection using projection view (PV) images rather than the reconstructed three-dimensional (3D) DBT volume. Signal-to-noise ratio (SNR) enhancement processing was first applied to each PV to enhance the potential MCs. The locations of MC candidates were then identified with iterative thresholding. The individual MCs were decomposed with Hermite-Gaussian (HG) and Laguerre-Gaussian (LG) basis functions and the channelized Hotelling model was trained to produce the MCRs for each MC on the 2D images. The MCRs from the PVs were fused in 3D by a coincidence counting method that backprojects the MC candidates on the PVs and traces the coincidence of their ray paths in 3D. The 3D MCR was used to differentiate the true MCs from false positives (FPs). Finally a dynamic clustering method was used to identify the potential MC clusters in the DBT volume based on the fact that true MCs of clinical significance appear in clusters. Using two-fold cross validation, the performance of the 3D MCR for classification of true and false MCs was estimated by the area under the receiver operating characteristic (ROC) curve and the overall performance of the MCR approach for detection of clustered MCs was assessed by free response receiver operating characteristic (FROC) analysis. When the HG basis function was used for MCR analysis, the detection of MC cluster achieved case-based test sensitivities of 80% and 90% at the average FP rates of 0.65 and 1.55 FPs per DBT volume, respectively. With LG basis function, the average FP rates were 0.62 and 1.57 per DBT volume at the same sensitivity levels. The difference in the two sets of basis functions for detection of MCs did not show statistical significance. The authors' experimental results indicate that the MCR approach is promising for the detection of MCs on PV images. The HG or LG basis functions are both effective in characterizing the signal response of MCs using the channelized Hotelling model. The coincidence counting method for fusion of the 2D MCR in 3D is an important step for FP reduction. Further study is underway to improve the MCR approach for microcalcification detection in DBT.

Breast tomosynthesis imaging configuration analysis

Traditional two-dimensional (2D) X-ray mammography is the most commonly used method for breast cancer diagnosis. Recently, a three-dimensional (3D) Digital Breast Tomosynthesis (DBT) system has been invented, which is likely to challenge the current mammography technology. The DBT system provides stunning 3D information, giving physicians increased detail of anatomical information, while reducing the chance of false negative screening. In this research, two reconstruction algorithms, Back Projection (BP) and Shift-And-Add (SAA), were used to investigate and compare View Angle (VA) and the number of projection images (N) with parallel imaging configurations. In addition, in order to better determine which method displayed better-quality imaging, Modulation Transfer Function (MTF) analyses were conducted with both algorithms, ultimately producing results which improve upon better breast cancer detection. Research studies find evidence that early detection of the disease is the best way to conquer breast cancer, and earlier detection results in the increase of life span for the affected person.