Digital Breast Tomosynthesis Images Research Articles

Imaging phenotype evaluation from digital breast tomosynthesis data: A preliminary study

Background:Digital breast tomosynthesis (DBT) has been widely adopted as a supplemental imaging modality for diagnostic evaluation of breast cancer and confirmation studies. In this study, a deep learning-based method for characterizing breast tissue patterns in DBT data is presented. Methods:A set of 5388 2D image patches was produced from 230 right mediolateral oblique, 259 left mediolateral oblique, 18 right craniocaudal, and 15 left craniocaudal single-breast DBT studies, using slice-wise annotations of abnormalities and normal tissue. We implemented a patch classifier to predict samples according to two differing scenarios and train it using the patch dataset. First, tissue samples were classified into the following classes: malignant, benign, and normal breast tissue. Second, tissue samples were classified into the following classes: malignant mass, benign mass, benign architectural distortion, malignant architectural distortion, and normal breast tissue. We employed transfer learning and initialized the model base layers with existing pre-trained weights obtained from Globally-Aware Multiple Instance Classifier. Results:High class-wise recall values of 0.8906, 0.8541 and 0.7345 and specificities 0.9558, 0.9575 and 0.8830 were obtained for normal, benign, and malignant classification, respectively. More intricate classification yielded class-wise recall values of 0.8708, 0.8299, 0.9444 and 0.5723 and specificities 0.9406, 0.9833, 0.8943 and 0.9652 for benign mass, normal, malignant architectural distortion, and malignant mass, respectively. However, benign architectural distortion was confused with benign mass and malignant architectural distortion. Conclusions:Combining the proposed phenotype classifier with the commonly used malignant-benign-normal classification enables a more detailed assessment of digital breast tomosynthesis images.

Diagnostic Efficacy of Five Different Imaging Modalities in the Assessment of Women Recalled at Breast Screening-A Systematic Review and Meta-Analysis.

There are variations in the assessment pathways for women recalled at screening, and the imaging assessment pathway with the best diagnostic outcome is poorly understood. This paper examines the efficacy of five imaging modalities for the assessment of screen-recalled breast lesions. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) strategy was employed to identify studies that assessed the efficacy of imaging modalities in the assessment of lesions recalled at screening from the following eight databases: Medline, Web of Science, Embase, Scopus, Science Direct, PubMed, CINAHL, and Global Health. Search terms included "Breast assessment" AND "Diagnostic Workup" OR "Mammography" AND "Digital Breast tomosynthesis" AND "contrast enhanced mammography and Magnetic Resonance imaging" AND "breast ultrasound". Studies that examined the performance of digital mammography (DM), digital breast tomosynthesis (DBT), handheld ultrasound (HHUS), contrast-enhanced mammography (CEM), and magnetic resonance imaging (MRI) in screen-recalled lesions were reviewed. Meta-analyses of these studies were conducted using the MetaDisc 2.0 software package. Results: Fifty-four studies met the inclusion criteria and examined between one and three imaging modalities. Pooled results of each imaging modality demonstrated that CEM has the highest sensitivity (95; 95% CI: 90-97) followed by MRI (93; 95% CI: 88-96), DBT (91; 95% CI: 87-94), HHUS (90; 95% CI: 86-93), and DM (85; 95% CI: 78-90). The DBT demonstrated the highest specificity (85; 95% CI: 75-91) followed by DM (77; 95% CI: 66-85), CEM (73; 95% CI: 63-81), MRI (69; 95% CI: 55-81), and HHUS (65; 95% CI: 46-80). Conclusions: The CEM, MRI, DBT, and HHUS demonstrate excellent performance in correctly identifying and classifying cancer lesions referred for diagnostic work-up, but HHUS, MRI, and CEM have a more limited ability to discriminate benign lesions than DBT and DM.

Automated assessment of task-based performance of digital mammography and tomosynthesis systems using an anthropomorphic breast phantom and deep learning-based scoring.

Conventional metrics used for assessing digital mammography (DM) and digital breast tomosynthesis (DBT) image quality, including noise, spatial resolution, and detective quantum efficiency, do not necessarily predict how well the system will perform in a clinical task. A number of existing phantom-based methods have their own limitations, such as unrealistic uniform backgrounds, subjective scoring using humans, and regular signal patterns unrepresentative of common clinical findings. We attempted to address this problem with a realistic breast phantom with random hydroxyapatite microcalcifications and semi-automated deep learning-based image scoring. Our goal was to develop a methodology for objective task-based assessment of image quality for tomosynthesis and DM systems, which includes an anthropomorphic phantom, a detection task (microcalcification clusters), and automated performance evaluation using a convolutional neural network. Experimental 2D and pseudo-3D mammograms of an anthropomorphic inkjet-printed breast phantom with inserted microcalcification clusters were collected on clinical mammography systems to train a signal-present/signal-absent image classifier based on Resnet-18 architecture. In a separate validation study using simulations, this Resnet-18 classifier was shown to approach the performance of an ideal observer. Microcalcification detection performance was evaluated as a function of four dose levels using receiver operating characteristic (ROC) analysis [i.e., area under the ROC curve (AUC)]. To demonstrate the use of this evaluation approach for assessing different technologies, the method was applied to two different mammography systems, as well as to mammograms with re-binned pixels emulating a lower-resolution X-ray detector. Microcalcification detectability, as assessed by the deep learning classifier, was observed to vary with the exposure incident on the breast phantom for both DM and tomosynthesis. At full dose, experimental AUC was 0.96 (for DM) and 0.95 (for DBT), whereas at half dose, it dropped to 0.85 and 0.71, respectively. AUC performance on DM was significantly decreased with an effective larger pixel size obtained with re-binning. The task-based assessment approach also showed the superiority of a newer mammography system compared with an older system. An objective task-based methodology for assessing the image quality of mammography and tomosynthesis systems is proposed. Possible uses for this tool could be quality control, acceptance, and constancy testing, assessing the safety and effectiveness of new technology for regulatory submissions, and system optimization. The results from this study showed that the proposed evaluation method using a deep learning model observer can track differences in microcalcification signal detectability with varied exposure conditions.

A multiple x-ray-source array (MXA) system with a planar two-dimensional source distribution for digital breast tomosynthesis.

Digital breast tomosynthesis (DBT) has outpaced digital mammography in clinical adoption in the United States; however, substantial technological limitations remain to image quality in DBT, including undersampling from a one-dimensional (1D) scan geometry, x-ray source motion during acquisition, and patient motion artifacts from long exam times. A thermionic cathode x-ray system employing two-dimensional (2D, planar) multiple x-ray-source arrays (MXA) is proposed to improve DBT image quality. A 1D MXA, consisting of a linear array of thermionic cathodes was used to simulate a 2D MXA. The 1D MXA included 11 focal spots separated by a distance of =23mm. The 11 cathodes were paired with 11 molybdenum 50mm diameter anode disks, mounted on a rotating shaft within a single vacuum enclosure. Image quality was investigated as a function of MXA configuration by integrating the 1D MXA with a 200 × 250 mm2 flat panel detector at a source-to-detector distance of 630mm, resulting in a 20° tomographic arc. To simulate a 2D MXA, the detector (with phantom) was translated orthogonally to the linear array by a distance ( ) ranging from =0mm (conventional 1D) to =57mm. All sources operated at 30kV with 80mA and 4.5 mAs/pulse, yielding ∼100 mAs per DBT dataset. DBT reconstructions involved 22 projections and used filtered backprojection with a ramp and Hann apodization filter. Volumetric reconstructions for each source were weighted by sampling differences between sources, and averaged. Image quality was assessed in terms of contrast-to-noise ratio (CNR), background clutter noise and power spectrum, and slice sensitivity profile (SSP) using a set of physical phantoms, including: (i) contrast-detail signals coupled to spherical clutter (PMMA in air); (ii) an SSP phantom; (iii) a commercial "breast" phantom (CIRS BR3D, Sun Nuclear, Norfolk, VA); and (iv) bovine muscle. Background clutter noise amplitude reduced monotonically from the 1D MXA (σclutter=5.9 A.U., =0mm) and 2D MXA arrays with increasing , with statistical significance between the 1D MXA and 2D MXA with =57mm (σclutter=5.0 A.U., p<0.001). The contrast-detail/clutter phantom demonstrated CNR from the 2D MXA (δ=57mm) outperforming the 1D MXA in all combinations of contrast and detail. 2D power spectrum analysis of clutter demonstrated a pronounced Fourier domain null cone for the 1D MXA in the anterior field-of-view (away from the 1D MXA position), whereas the 2D MXA geometry (δ=57mm) did not exhibit the null cone. The SSP was 15%-50% narrower (FWHM) for the 2D versus the 1D geometry, across all reconstruction setups. The advantages of a 2D source geometry for DBT imaging were demonstrated quantitatively compared to a conventional 1D line of x-ray sources. The improvement in the 2D geometry was attributed both to improved Fourier domain sampling and reduced SSP. We conclude that 2D MXA sources have the potential to substantially improve DBT imaging in comparison to existing commercial DBT systems.

Detection of breast cancer in digital breast tomosynthesis with vision transformers.

Digital Breast Tomosynthesis (DBT) has revolutionized more traditional breast imaging through its three-dimensional (3D) visualization capability that significantly enhances lesion discernibility, reduces tissue overlap, and improves diagnostic precision as compared to conventional two-dimensional (2D) mammography. In this study, we propose an advanced Computer-Aided Detection (CAD) system that harnesses the power of vision transformers to augment DBT's diagnostic efficiency. This scheme uses a neural network to glean attributes from the 2D slices of DBT followed by post-processing that considers features from neighboring slices to categorize the entire 3D scan. By leveraging a transfer learning technique, we trained and validated our CAD framework on a unique dataset consisting of 3,831 DBT scans and subsequently tested it on 685 scans. Of the architectures tested, the Swin Transformer outperformed the ResNet101 and vanilla Vision Transformer. It achieved an impressive AUC score of 0.934 ± 0.026 at a resolution of 384 384. Increasing the image resolution from 224 to 384 not only maintained vital image attributes but also led to a marked improvement in performance (p-value = 0.0003). The Mean Teacher algorithm, a semi-supervised method using both labeled and unlabeled DBT slices, showed no significant improvement over the supervised approach. Comprehensive analyses across different lesion types, sizes, and patient ages revealed consistent performance. The integration of attention mechanisms yielded a visual narrative of the model's decision-making process that highlighted the prioritized regions during assessments. These findings should significantly propel the methodologies employed in DBT image analysis by setting a new benchmark for breast cancer diagnostic precision.

AI-based strategies in breast mass ≤ 2 cm classification with mammography and tomosynthesis

PurposeTo evaluate the diagnosis performance of digital mammography (DM) and digital breast tomosynthesis (DBT), DM combined DBT with AI-based strategies for breast mass ≤ 2 cm. Materials and methodsDM and DBT images in 483 patients including 512 breast masses were acquired from November 2018 to November 2019. Malignant and benign tumours were determined by biopsies using histological analysis and follow-up within 24 months. The radiomics and deep learning methods were employed to extract the breast mass features in images and finally for benign and malignant classification. The DM, DBT and DM combined DBT (DM + DBT) images were fed into radiomics and deep learning models to construct corresponding models, respectively. The area under the receiver operating characteristic curve (AUC) was employed to estimate model performance. An external dataset of 146 patients from March 2021 to December 2022 from another center was enrolled for external validation. ResultsIn the internal testing dataset, compared with the DM model and the DBT model, the DM + DBT models based on radiomics and deep learning both showed statistically significant higher AUCs [0.810 (RA-DM), 0.823 (RA-DBT) and 0.869 (RA-DM + DBT), P ≤ 0.001; 0.867 (DL-DM), 0.871 (DL-DBT) and 0.908 (DL-DM + DBT), P = 0.001]. The deep learning models present superior to the radiomics models in the experiments with only DM (0.867 vs 0.810, P = 0.001), only DBT (0.871 vs 0.823, P = 0.001) and DM + DBT (0.908 vs 0.869, P = 0.003). ConclusionsDBT has a clear additional value for diagnosing breast mass less than 2 cm compared with only DM. AI-based methods, especially deep learning, can help achieve excellent performance.

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.

Comparison of Full-Field Digital Mammography with Synthesized Mammography from Tomosynthesis in a Diagnostic population: Prospective Study

Abstract Background Synthesized mammography (SM) refers to two-dimensional (2D) images derived from the digital breast tomosynthesis (DBT) data. It can reduce the radiation dose and scan duration when compared with conventional full-field digital mammography (FFDM) plus tomosynthesis. Purpose To compare the diagnostic performance of 2D FFDM with synthetic mammograms obtained from DBT in a diagnostic population. Materials and Methods A total of 1,468 mammograms with both FFDM and SM + DBT images were obtained and analyzed over 2 years, after obtaining approval from the institute ethics committee. The images were reported and compared as per the 2013 American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) lexicon in terms of breast density, morphological features of mass, calcifications, and presence of asymmetry or architectural distortion followed by the BI-RADS category. The agreement between the two modalities was studied using the Kappa value, and the radiation exposure dose was recorded in both groups. Results FFDM and SM + DBT showed strong agreement for breast density, mass characteristics, and detection of calcifications (kappa &gt; 0.8). Downstaging of breast density and mass density were seen by SM + DBT without any statistically significant difference. The nipple–areola complex visualization was poor in SM (50.34 vs. 76.29% in FFDM), and there were SM-specific artifacts mainly related to the reconstruction algorithm. The radiation dose was higher with SM. Conclusion FFDM has comparable performance to SM + DBT in diagnostic setup. The latter may be particularly helpful in patients with dense breasts.

Estimation of the absorbed dose in simultaneous digital breast tomosynthesis and mechanical imaging.

Use of mechanical imaging (MI) as complementary to digital mammography (DM), or in simultaneous digital breast tomosynthesis (DBT) and MI - DBTMI, has demonstrated the potential to increase the specificity of breast cancer screening and reduce unnecessary biopsies compared with DM. The aim of this study is to investigate the increase in the radiation dose due to the presence of an MI sensor during simultaneous image acquisition when automatic exposure control is used. A radiation dose study was conducted on clinically available breast imaging systems with and without an MI sensor present. Our estimations were based on three approaches. In the first approach, exposure values were compared in paired clinical DBT and DBTMI acquisitions in 97 women. In the second approach polymethyl methacrylate (PMMA) phantoms of various thicknesses were used, and the average glandular dose (AGD) values were compared. Finally, a rectangular PMMA phantom with a 45mm thickness was used, and the AGD values were estimated based on air kerma measurements with an electronic dosemeter. The relative increase in exposure estimated from digital imaging and communications in medicine headers when using an MI sensor in clinical DBTMI was . For the phantom measurements of various thicknesses of PMMA, the relative increases in the AGD for DM and DBT measurements were, on average, and , respectively. The relative increase in the AGD using the electronic dosemeter was in DM and in DBT. The average difference in dose between the methods was . Our measurements suggest that the use of simultaneous breast radiography and MI increases the AGD by an average of . The increase in dose is within the acceptable values for mammography screening recommended by European guidelines.

Leveraging Quantum Kernel Support Vector Machine for breast cancer diagnosis from Digital Breast Tomosynthesis images

Leveraging Quantum Kernel Support Vector Machine for breast cancer diagnosis from Digital Breast Tomosynthesis images

A Transformative Approach for Breast Cancer Detection Using Physics-Informed Neural Network and Surface Temperature Data

Abstract Early detection is the most effective defense against breast cancer. Mammography is a well-established X-ray based technique that is used for annual or biennial screening of women above age of 40. Since the dense breast tissue sometimes obscures the cancer in an x-ray image, about 10% of screened women are recalled and undergo additional adjunctive modalities, such as ultrasound, digital breast tomosynthesis or magnetic resonance imaging (MRI). These modalities have drawbacks such as additional radiation dosage, overdiagnosis and high cost. A new concurrent multi-spectral imaging approach is recently presented to eliminate the high recall rates by utilizing the breast surface temperature data with an inverse physics-informed neural network algorithm. The multi-spectral imaging does not use any harmful radiations, such as x-rays, is contact-less and does not require breast compression. It has been validated in 23 patients and offers a cost-effective solution to provide improved detection capability of cancerous cells. It is estimated to reduce the recall rates significantly from the current 10%, with a corresponding reduction in the biopsies. This adjunctive approach builds on the strength of mammography and offers a safe adjunct by relying on the higher metabolic rates of cancer cells.

X-ray source motion blur modeling and deblurring with generative diffusion for digital breast tomosynthesis

Objective. Digital breast tomosynthesis (DBT) has significantly improved the diagnosis of breast cancer due to its high sensitivity and specificity in detecting breast lesions compared to two-dimensional mammography. However, one of the primary challenges in DBT is the image blur resulting from x-ray source motion, particularly in DBT systems with a source in continuous-motion mode. This motion-induced blur can degrade the spatial resolution of DBT images, potentially affecting the visibility of subtle lesions such as microcalcifications. Approach. We addressed this issue by deriving an analytical in-plane source blur kernel for DBT images based on imaging geometry and proposing a post-processing image deblurring method with a generative diffusion model as an image prior. Main results. We showed that the source blur could be approximated by a shift-invariant kernel over the DBT slice at a given height above the detector, and we validated the accuracy of our blur kernel modeling through simulation. We also demonstrated the ability of the diffusion model to generate realistic DBT images. The proposed deblurring method successfully enhanced spatial resolution when applied to DBT images reconstructed with detector blur and correlated noise modeling. Significance. Our study demonstrated the advantages of modeling the imaging system components such as source motion blur for improving DBT image quality.

Functional hemodynamic imaging markers for the prediction of pathological outcomes in breast cancer patients treated with neoadjuvant chemotherapy.

Achieving pathologic complete response (pCR) after neoadjuvant chemotherapy (NACT) is a significant predictor of increased likelihood of survival in breast cancer patients. Early prediction of pCR is of high clinical value as it could allow personalized adjustment of treatment regimens in non-responding patients for improved outcomes. We aim to assess the association between hemoglobin-based functional imaging biomarkers derived from diffuse optical tomography (DOT) and the pathological outcome represented by pCR at different timepoints along the course of NACT. Twenty-two breast cancer patients undergoing NACT were enrolled in a multimodal DOT and X-ray digital breast tomosynthesis (DBT) imaging study in which their breasts were imaged at different compression levels. Logistic regressions were used to study the associations between DOT-derived imaging markers evaluated after the first and second cycles of chemotherapy, respectively, with pCR status determined after the conclusion of NACT at the time of surgery. Receiver operating characteristic curve analysis was also used to explore the predictive performance of selected DOT-derived markers. Normalized tumor HbT under half compression was significantly lower in the pCR group compared to the non-pCR group after two chemotherapy cycles (). In addition, the change in normalized tumor upon reducing compression from full to half mammographic force was identified as another potential indicator of pCR at an earlier time point, i.e., after the first chemo cycle (). Exploratory predictive assessments showed that AUCs using DOT-derived functional imaging markers as predictors reach as high as 0.75 and 0.71, respectively, after the first and second chemo cycle, compared to AUCs of 0.50 and 0.53 using changes in tumor size measured on DBT and MRI. These findings suggest that breast DOT could be used to assist response assessment in women undergoing NACT, a critical but unmet clinical need, and potentially enable personalized adjustments of treatment regimens.

SIFT-DBT: SELF-SUPERVISED INITIALIZATION AND FINE-TUNING FOR IMBALANCED DIGITAL BREAST TOMOSYNTHESIS IMAGE CLASSIFICATION.

Digital Breast Tomosynthesis (DBT) is a widely used medical imaging modality for breast cancer screening and diagnosis, offering higher spatial resolution and greater detail through its 3D-like breast volume imaging capability. However, the increased data volume also introduces pronounced data imbalance challenges, where only a small fraction of the volume contains suspicious tissue. This further exacerbates the data imbalance due to the case-level distribution in real-world data and leads to learning a trivial classification model that only predicts the majority class. To address this, we propose a novel method using view-level contrastive Self-supervised Initialization and Fine-Tuning for identifying abnormal DBT images, namely SIFT-DBT. We further introduce a patch-level multi-instance learning method to preserve spatial resolution. The proposed method achieves 92.69% volume-wise AUC on an evaluation of 970 unique studies.

Assessment of volumetric dense tissue segmentation in tomosynthesis using deep virtual clinical trials

The adoption of artificial intelligence (AI) in medical imaging requires careful evaluation of machine-learning algorithms. We propose the use of a “deep virtual clinical trial” (DeepVCT) method to effectively evaluate the performance of AI algorithms. In this paper, DeepVCTs have been proposed to elucidate limitations of AI applications and predictions of clinical outcomes, avoiding biases in study designs. The DeepVCT method was used to evaluate the performance of nnU-Net models in assessing volumetric breast density (VBD) from digital breast tomosynthesis (DBT) images. In total, 2010 anatomical breast models were simulated. Projections were simulated using the acquisition geometry of a clinical DBT system. The projections were reconstructed using 0.1, 0.2, and 0.5 mm plane spacing. nnU-Net models were developed using the center-most planes of the reconstructions with the respective ground-truth. The results show that the accuracy of the nnU-Net improves significantly with DBT images reconstructed with 0.1 mm plane spacing (78.4×205.3×40.1 mm3). The segmentations resulted in Dice values up to 0.84 with area under the receiver operating characteristic curve of 0.92. The optimization of plane spacing for VBD assessment was used as an exemplar of a DeepVCT application, allowing better interpretation of the input parameters and outcomes of the nnU-Net. Thus, DeepVCTs can provide evidence to predict the efficacy of AI algorithms using large-scale simulation-based data.

Adaptive fuzzy weighted median filter for microcalcifications detection in digital breast tomosynthesis images

Breast cancer is a global leading cause of female mortality. Digital breast tomosynthesis (DBT) is pivotal for early breast cancer detection, with microcalcifications serving as crucial indicators. However, the movement of the DBT machine introduces blurry artefacts, potentially impacting accurate diagnosis. This study addresses this challenge by proposing an adaptive fuzzy weighted median filter (AFWMF) to enhance DBT images and aid microcalcification diagnosis. AFWMF automatically determines optimal parameters based on input images, outperforming conventional methods with a threshold range (C) from peak to end of switching. Quantitative assessment reveals peak signal to noise ratio (PSNR), and mean absolute error (MAE) values of 96.2267 and 0.0000636, respectively, demonstrating a significant improvement in microcalcification detection. This study contributes an effective and adaptive enhancement technique for DBT images, promising better breast cancer diagnosis, particularly in microcalcification scenarios.

SAH-NET: Structure-Aware Hierarchical Network for Clustered Microcalcification Classification in Digital Breast Tomosynthesis.

Benign and malignant classification of clustered microcalcifications (MCs) in digital breast tomosynthesis (DBT) is an essential task in computer-aided diagnosis. However, due to the anisotropic resolution of DBT, three-dimensional (3-D) convolutional neural network (CNN)-based methods cannot extract hierarchical features efficiently. Moreover, the sparse distribution of MC points in the cluster makes it difficult for the CNN to extract discriminative structural information for classification. To comprehensively address these challenges, we propose a novel structure-aware hierarchical network (SAH-Net) for benign and malignant classification of clustered MC in a DBT volume. Specifically, the two-dimensional (2-D) group convolution is used to extract intraslice features. The one-to-one correspondence between group convolutions and slices ensures the independence of hierarchical feature extraction. Then, a partial deformable Transformer-based 3-D structural feature learning module is proposed to capture the long-range dependency between MC points in the cluster. We evaluate the proposed method on an in-house dataset with 495 clustered MCs collected from 462 DBT images. Experimental results confirm the validity of our proposed modules. The results also show that the proposed SAH-Net outperforms several other representative methods on this topic, and achieves the best classification result, with an area under the receiver operation curve (AUC) of 86.87%. The implementation of the proposed model is available at https://github.com/sunhaotian130911/SAHNet.

Comparative analysis of cone‐beam breast computed tomography and digital breast tomosynthesis for breast cancer diagnosis: A comprehensive study on reconstruction algorithms

AbstractBreast cancer (BC) is the most commonly diagnosed non‐skin cancer in women. To achieve early and accurate diagnosis, three‐dimensional (3D) cone‐beam breast computed tomography (CBBCT) and digital breast tomosynthesis (DBT) modalities are used. Importantly, the comparison of reconstruction accuracy of both CBBCT and DBT has rarely been investigated, thus constituting a research gap. This study systematically investigated the performances of the CBBCT and DBT for different reconstruction algorithms using both BR3D breast phantom and breast images. We acquired clinical breast images and scanned the BR3D phantom for additional breast images. These acquired images were used to simulate projection data using predefined CBBCT and DBT geometries. The simulated projections were reconstructed using five different reconstruction algorithms. To evaluate the reconstruction accuracy, we calculated average image quality assessment (IQA) indices, including peak signal‐to‐noise ratio (PSNR), structural similarity index (SSIM), root mean square error (RMSE), and others, across different algorithms and modalities. The pooled PSNR, SSIM, and RMSE for DBT and CBBCT images are (31.6265 ± 0.8725), (0.9353 ± 0.0077), and (0.0270 ± 0.0025) and (29.7007 ± 0.9249), (0.9136 ± 0.0130), and (0.0342 ± 0.0040), which implies that the overall IQA indices of DBT are superior to CBBCT; therefore, DBT tends to reveal more BC detectability as the diagnosis outcome would largely depend on good quality images. The results show that DBT gives an improved result for all algorithms compared to CBBCT, although further experimental trials may be needed to establish the findings fully. The findings suggest that using DBT may enhance the accuracy of BC diagnosis compared to CBBCT due to its superior image quality in clinical practice, emphasizing the importance of selecting optimal reconstruction algorithms for improved diagnostic outcomes.

Optimized signal of calcifications in wide-angle digital breast tomosynthesis: a virtual imaging trial.

Evaluate microcalcification detectability in digital breast tomosynthesis (DBT) and synthetic 2D mammography (SM) for different acquisition setups using a virtual imaging trial (VIT) approach. Medio-lateral oblique (MLO) DBT acquisitions on eight patients were performed at twice the automatic exposure controlled (AEC) dose. The noise was added to the projections to simulate a given dose trajectory. Virtual microcalcification models were added to a given projection set using an in-house VIT framework. Three setups were evaluated: (1) standard acquisition with 25 projections at AEC dose, (2) 25 projections with a convex dose distribution, and (3) sparse setup with 13 projections, every second one over the angular range. The total scan dose and angular range remained constant. DBT volume reconstruction and synthetic mammography image generation were performed using a Siemens prototype algorithm. Lesion detectability was assessed through a Jackknife-alternative free-response receiver operating characteristic (JAFROC) study with six observers. For DBT, the area under the curve (AUC) was 0.97 ± 0.01 for the standard, 0.95 ± 0.02 for the convex, and 0.89 ± 0.03 for the sparse setup. There was no significant difference between standard and convex dose distributions (p = 0.309). Sparse projections significantly reduced detectability (p = 0.001). Synthetic images had a higher AUC with the convex setup, though not significantly (p = 0.435). DBT required four times more reading time than synthetic mammography. A convex setup did not significantly improve detectability in DBT compared to the standard setup. Synthetic images exhibited a non-significant increase in detectability with the convex setup. Sparse setup significantly reduced detectability in both DBT and synthetic mammography. This virtual imaging trial study allowed the design and efficient testing of different dose distribution trajectories with real mammography images, using a dose-neutral protocol. • In DBT, a convex dose distribution did not increase the detectability of microcalcifications compared to the current standard setup but increased detectability for the SM images. • A sparse setup decreased microcalcification detectability in both DBT and SM images compared to the convex and current clinical setups. • Optimal microcalcification cluster detection in the system studied was achieved using either the standard or convex dose setting, with the default number of projections.

Image Quality Enhancement for Digital Breast Tomosynthesis: High-Density Object Artifact Reduction.

Breast cancer has a high incidence and mortality rate among women, early diagnosis is essential as it gives insight regarding the most appropriate therapeutic strategy for each case. Among all imaging diagnostic methods, digital breast tomosynthesis (DBT) is effective for early breast cancer detection. In DBT images, high-density object artifacts are generated when imaging objects with high X-ray absorptivity, which include metal artifacts, ripple artifacts, and deformation artifacts. In this study, we analyze the causes of these artifacts and propose a set of high-density object reconstruction methods based on iterative algorithms. Our method includes a reprojection-based high-density object projection data segmentation algorithm and an iterative reconstruction algorithm based on volume expansion. The experiments on simulation data and the human breast data with artificial surgical needles prove the effectiveness of our method. By using our algorithm, the problem of distorting the shape, size, and position of high-density objects during DBT reconstruction is raised, the influence of these artifacts is reduced, and the quality of the DBT reconstructed image is improved. We hope that our algorithm might contribute to promoting the usage of DBT.