Digital Breast Tomosynthesis Scans Research Articles

Predicting Ki-67 expression levels in breast cancer using radiomics-based approaches on digital breast tomosynthesis and ultrasound.

To construct and validate radiomics models that utilize ultrasound (US) and digital breast tomosynthesis (DBT) images independently and in combination to non-invasively predict the Ki-67 status in breast cancer. 149 breast cancer women who underwent DBT and US scans were retrospectively enrolled from June 2018 to August 2023 in total. Radiomics features were acquired from both the DBT and US images, then selected and reduced in dimensionality using several screening approaches. Establish radiomics models based on DBT, and US separately and combined. The area under the receiver operating characteristic curve (AUC), accuracy, specificity, and sensitivity were utilized to validate the predictive ability of the models. The decision curve analysis (DCA) was used to evaluate the clinical applicability of the models. The output of the classifier with the best AUC performance was converted into Rad-score and was regarded as Rad-Score model. A nomogram was constructed using the logistic regression method, integrating the Rad-Score and clinical factors. The model's stability was assessed through AUC, calibration curves, and DCA. Support vector machine (SVM), logistic regression (LR), and random forest (RF) were trained to establish radiomics models with the selected features, with SVM showing optimal results. The AUC values for three models (US_SVM, DBT_SVM, and merge_SVM) were 0.668, 0.704, and 0.800 respectively. The DeLong test indicated a notable disparity in the area under the curve (AUC) between merge_SVM and US_SVM (p = 0.048), while there was no substantial variability between merge_SVM and DBT_SVM (p = 0.149). The DCA curve indicates that merge_SVM is superior to unimodal models in predicting high Ki-67 level, showing more clinical values. The nomogram integrating Rad-Score with tumor size obtained the better performance in test set (AUC: 0.818) and had more clinical net. The fusion radiomics model performed better in predicting the Ki-67 expression level of breast carcinoma, but the gain effect is limited; thus, DBT is preferred as a preoperative diagnosis mode when resources are limited. Nomogram offers predictive advantages over other methods and can be a valuable tool for predicting Ki-67 levels in BC.

Digital wrist tomosynthesis (DWT)-based finite element analysis of ultra-distal radius differentiates patients with and without a history of osteoporotic fracture.

Despite effective therapies for those at risk of osteoporotic fracture, low adherence to screening guidelines and limited accuracy of bone mineral density (BMD) in predicting fracture risk preclude identification of those at risk. Because of high adherence to routine mammography, bone health screening at the time of mammography using a digital breast tomosynthesis (DBT) scanner has been suggested as a potential solution. BMD and bone microstructure can be measured from the wrist using a DBT scanner. However, the extent to which biomechanical variables can be derived from digital wrist tomosynthesis (DWT) has not been explored. Accordingly, we measured stiffness from a DWT based finite element (DWT-FE) model of the ultra-distal (UD) radius and ulna, and correlate these to reference microcomputed tomography image based FE (μCT-FE) from five cadaveric forearms. Further, this method is implemented to determine in vivo reproducibility of FE derived stiffness of UD radius and demonstrate the in vivo utility of DWT-FE in bone quality assessment by comparing two groups of postmenopausal women with and without a history of an osteoporotic fracture (Fx; n=15, NFx; n=51). Stiffness obtained from DWT and μCT had a strong correlation (R2=0.87, p<0.001). In vivo repeatability error was <5%. The NFx and Fx groups were not significantly different in DXA derived minimum T-scores (p>0.3), but stiffness of the UD radius was lower for the Fx group (p<0.007). Logistic regression models of fracture status with stiffness of the nondominant arm as the predictor were significant (p<0.01). In conclusion this study demonstrates the feasibility of fracture risk assessment in mammography settings using DWT imaging and FE modeling in vivo. Using this approach, bone and breast screening can be performed in a single visit, with the potential to improve both the prevalence of bone health screening and the accuracy of fracture risk assessment.

Abstract P070: Volumetric parenchymal pattern analysis for breast cancer risk estimation

Abstract Introduction: Mammographic breast density is among the strongest risk factors for breast cancer. However, breast density is typically assessed subjectively by the radiologist according to the Breast Imaging Reporting and Data System (BI-RADS) based on 2 dimensional (2D) digital mammography (DM) images. Digital breast tomosynthesis (DBT) is quickly replacing DM and allows more detailed volumetric imaging of the breast. Advances in radiomics, the high-throughput extraction of radiologic features, has enabled characterization of breast parenchymal complexity beyond breast density alone. The purpose of this study was to compare the performance of volumetric parenchymal pattern analysis from DBT and DM with conventional breast density measurement with respect to breast cancer risk estimation. Methods: We performed a case control study among women with concurrent DM and DBT screening (Selenia Dimensions, Hologic Inc.) at our institution between 3/2011-12/2014. Cases were diagnosed with breast cancer within 1 year of screening; controls were confirmed negative or benign at 1 year follow-up, matched on race (Black, White, other/unknown) and age (5-year bins). After exclusions for imaging artifacts, craniocaudal (CC) and mediolateral oblique (MLO) views for 187 cases and 737 controls, in six image formats were assessed: 1) raw (“FOR PROCESSING”) DM; 2) processed (“FOR PRESENTATION”) DM; 3) raw DBT central projection; 4) processed DBT central projection; 5) DBT central reconstructed slice; and 6) DBT reconstructed stack. For cases, we analyzed the breast contralateral to cancer diagnosis; for controls the same breast as the matched case. We extracted radiomic features using a lattice-based approach with the publicly available CaPTk software, averaging features for each breast over CC and MLO views. We examined 3 lattice window sizes (6.4, 12.8, and 25.6 mm) and 23 resolutions for image resampling (0.075 - 2mm). We performed PCA on the resulting 487 features for each combination of window size and resolution and built conditional logistic regression models to assess the association of the first 7 principal components with breast cancer, with models including age, BMI, and BI-RADS density. For each image type we calculated the model C-statistic at all window sizes and resolutions, for a total of 2304 experimental conditions. Results: Features from reconstructed DBT scans had on average higher C-statistics across all experimental conditions. A model using only age, BMI, and BI-RADS density had a C-statistic of 0.61. Models using radiomic features plus age, BMI, and BI-RADS density had mean C-statistic of 0.68 (IQR 0.68, 0.69) for reconstructed DBT scans; for all other image types, the mean C-statistic ranged from 0.64 to 0.66. Conclusions: Incorporating volumetric breast parenchymal patterns from DBT improves breast cancer risk estimation beyond markers derived from DM and beyond conventional BI-RADS density. Citation Format: Eric A. Cohen, Omid Haji Maghsoudi, Raymond Acciavatti, Lauren Pantalone, Walter Mankowski, Alex A Nguyen, Christopher G. Scott, Stacey Winham, Andrew D. Maidment, Anne Marie McCarthy, Celine M Vachon, Emily F Conant, Despina Kontos. Volumetric parenchymal pattern analysis for breast cancer risk estimation. [abstract]. In: Proceedings of the AACR Special Conference: Precision Prevention, Early Detection, and Interception of Cancer; 2022 Nov 17-19; Austin, TX. Philadelphia (PA): AACR; Can Prev Res 2023;16(1 Suppl): Abstract nr P070.

Generative compressed breast shape model for digital mammography and digital breast tomosynthesis.

Modelling of the 3D breast shape under compression is of interest when optimizing image processing and reconstruction algorithms for mammography and digital breast tomosynthesis (DBT). Since these imaging techniques require the mechanical compression of the breast to obtain appropriate image quality, many such algorithms make use of breast-like phantoms. However, if phantoms do not have a realistic breast shape, this can impact the validity of such algorithms. To develop a point distribution model of the breast shape obtained through principal component analysis (PCA) of structured light (SL) scans from patient compressed breasts. SL scans were acquired at our institution during routine craniocaudal-view DBT imaging of 236 patients, creating a dataset containing DBT and SL scans with matching information. Thereafter, the SL scans were cleaned, merged, simplified, and set to a regular grid across all cases. A comparison between the initial SL scans after cleaning and the gridded SL scans was performed to determine the absolute difference between them. The scans with points in a regular grid were then used for PCA. Additionally, the correspondence between SL scans and DBT scans was assessed by comparing features such as the chest-to-nipple distance (CND), the projected breast area (PBA) and the length along the chest-wall (LCW). These features were compared using a paired t-test or the Wilcoxon signed rank sum test. Thereafter, the PCA shape prediction and SL scans were evaluated by calculating the mean absolute error to determine whether the model had adequately captured the information in the dataset. The coefficients obtained from the PCA could then parameterize a given breast shape as an offset from the sample means. We also explored correlations of the PCA breast shape model parameters with certain patient characteristics: age, glandular volume, glandular density by mass, total breast volume, compressed breast thickness, compression force, nipple location, and centre of the chest-wall. The median value across cases for the 90th and 99th percentiles of the interpolation error between the initial SL scans after cleaning and the gridded SL scans was 0.50 and 1.16mm, respectively. The comparison between SL and DBT scans resulted in small, but statistically significant, mean differences of 1.6mm, 1.6mm, and 2.2cm2 for the LCW, CND, and PBA, respectively. The final model achieved a median mean absolute error of 0.68mm compared to the scanned breast shapes and a perfect correlation between the first PCA coefficient and the patient breast compressed thickness, making it possible to use it to generate new model-based breast shapes with a specific breast thickness. There is a good agreement between the breast shape coverage obtained with SL scans used to construct our model and the DBT projection images, and we could therefore create a generative model based on this data that is available for download on Github.

Digital breast tomosynthesis-based peritumoral radiomics approaches in the differentiation of benign and malignant breast lesions.

PURPOSE We aimed to evaluate digital breast tomosynthesis (DBT)-based radiomics in the differentiation of benign and malignant breast lesions in women. METHODS A total of 185 patients who underwent DBT scans were enrolled between December 2017 and June 2019. The features of handcrafted and deep learning-based radiomics were extracted from the tumoral and peritumoral regions with different radial dilation distances outside the tumor. A 3-step method was used to select discriminative features and develop the radiomics signature. Discriminative clinical factors were identified by univariate logistic regression. The clinical fac- tors with P < .05 were used to build a clinical model with multivariate logistic regression. The radiomics nomogram was developed by integrating the radiomics signature and discriminative clinical factors. Discriminative performance of the radiomics signature, clinical model, nomo- gram, and breast imaging reporting and data system assessment were evaluated and compared with the receiver operating characteristic and decision curves analysis (DCA). RESULTS A total of 2 handcrafted and 2 deep features were identified as the most discriminative features from the peritumoral regions with 2 mm dilation distances and used to develop the radiomics signature. The nomogram incorporating the radiomics signature, age, and menstruation status showed the best discriminative performance with area under the curve (AUC) values of 0.980 (95% CI, 0.960 to 1.000; sensitivity =0.970, specificity =0.946) in the training cohort and 0.985 (95% CI, 0.960 to 1.000; sensitivity = 0.909, specificity = 0.966) in the validation cohort. DCA con- firmed the potential clinical usefulness of our nomogram. CONCLUSION Our results illustrate that the radiomics nomogram integrating the DBT imaging features and clinical factors (age and menstruation status) can be considered as a useful tool in aiding the clinical diagnosis of breast cancer.

Fabrication of 3D printed patient-derived anthropomorphic breast phantoms for mammography and digital breast tomosynthesis: Imaging assessment with clinical X-ray spectra

PurposeTo design, fabricate and characterize 3D printed, anatomically realistic, compressed breast phantoms for digital mammography (DM) and digital breast tomosynthesis (DBT) x-ray imaging. MaterialsWe realized 3D printed phantoms simulating healthy breasts, via fused deposition modeling (FDM), with a layer resolution of 0.1 mm and 100% infill density, using a dual extruder printer. The digital models were derived from a public dataset of segmented clinical breast computed tomography scans. Three physical phantoms were printed in polyethylene terephthalate (PET), acrylonitrile–butadiene–styrene (ABS), or in polylactic-acid (PLA) materials, using ABS as a substitute for adipose tissue, and PLA or PET filaments for replicating glandular and skin tissues. 3D printed phantoms were imaged at three clinical centers with DM and DBT scanners, using typical spectra. Anatomical noise of the manufactured phantoms was evaluated via the estimates of the β parameter both in DM images and in images acquired via a clinical computed tomography (CT) scanner. ResultsDM and DBT phantom images showed an inner texture qualitatively similar to the images of a clinical DM or DBT exam, suitably reproducing the glandular structure of their computational phantoms. β parameters evaluated in DM images of the manufactured phantoms ranged between 2.84 and 3.79; a lower β was calculated from the CT scan. ConclusionsFDM 3D printed compressed breast phantoms have been fabricated using ABS, PLA and PET filaments. DM and DBT images with clinical x-ray spectra showed realistic textures. These phantoms appear promising for clinical applications in quality assurance, image quality and dosimetry assessments.

Intratumoral and Peritumoral Analysis of Mammography, Tomosynthesis, and Multiparametric MRI for Predicting Ki-67 Level in Breast Cancer: a Radiomics-Based Study.

To noninvasively evaluate the use of intratumoral and peritumoral regions from full-field digital mammography (DM), digital breast tomosynthesis (DBT), dynamic contrast-enhanced (DCE), and diffusion-weighted (DW) magnetic resonance imaging (MRI) images separately and combined to predict the Ki-67 level based on radiomics. A total of 209 patients with pathologically confirmed breast cancer were consecutively enrolled from September 2017 to March 2021, who underwent DM, DBT, DCE-MRI, and DW MRI scans. Radiomics features were calculated from intratumoral and peritumoral regions in each modality and selected with the least absolute shrinkage and selection operator (LASSO) regression. Radiomics signatures (RSs) were built based on intratumoral, peritumoral, and combined intra- and peritumoral regions. The prediction performance of the RSs was evaluated using the area under the receiver operating characteristic curve (AUC), specificity, and sensitivity as comparison metrics. A nomogram was constructed by integrating the multi-model RS and important clinical predictors and assessed by calibration and decision curve analysis. The combined intra- and peritumoral RSs improved the AUC compared with intra- or peritumoral RSs in each modality. The DCE plus DW MRI yielded higher AUC and specificity but lower sensitivity compared with the DM plus DBT. The nomogram incorporating the multi-model RS, age, and lymph node metastasis status achieved the best prediction performance in the training (AUC, nomogram vs. fusion RS vs. clinical model, 0.922 vs. 0.917 vs. 0.672) and validation (AUCs, nomogram vs. fusion RS vs. clinical model, 0.866 vs. 0.838 vs. 0.661) cohorts. DCA analysis confirmed the potential clinical utility of the nomogram. Peritumoral regions can provide complementary information to intratumoral regions in mammography and MRI for the prediction of Ki-67 levels. The MRI performed better than mammography in terms of AUC and specificity but weaker in sensitivity. The nomogram has a predictive advantage over each modality and could be a potential tool for predicting Ki-67 levels in breast cancer.

Intratumoral analysis of digital breast tomosynthesis for predicting the Ki-67 level in breast cancer: A multi-center radiomics study.

To non-invasively evaluate the Ki-67 level in digital breast tomosynthesis (DBT) images of breast cancer (BC) patients based on subregional radiomics. A total of 266 patients who underwent DBT scans were consecutively enrolled at two centers, between September 2017 and September 2021. The whole tumor region was partitioned into various intratumoral subregions, based on individual- and population-level clustering. Handcrafted radiomics and deep learning-based features were extracted from the subregions and from the whole tumor region, and were selected by least absolute shrinkage and selection operator (LASSO) regression, yielding radiomics signatures (RSs). The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to assess the developed RSs. Each breast tumor region was partitioned into an inner subregion (S1) and a marginal subregion (S2). The RSs derived from S1 always generated higher AUCs compared with those from S2 or from the whole tumor region (W), for the external validation cohort (AUCs, S1 vs. W, handcrafted RSs: 0.583 [95% CI, 0.429-0.727] vs. 0.559 [95% CI, 0.405-0.705], p-value: 0.920; deep RSs: 0.670 [95% CI, 0.516-0.802] vs. 0.551 [95% CI, 0.397-0.698], p-value: 0.776). The fusion RSs, combining handcrafted and deep learning-based features derived from S1, yielded the highest AUCs of 0.820 (95% CI, 0.714-0.900) and 0.792 (95% CI, 0.647-0.897) for the internal and external validation cohorts, respectively. The subregional radiomics approach can accurately predict the Ki-67 level based on DBT data; thus, it may be used as a potential non-invasive tool for preoperative treatment planning in BC.

Intra- and peritumoral radiomics on assessment of breast cancer molecular subtypes based on mammography and MRI.

This study aimed to investigate the efficacy of digital mammography (DM), digital breast tomosynthesis (DBT), diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI separately and combined in the prediction of molecular subtypes of breast cancer. A total of 241 patients were enrolled and underwent breast MD, DBT, DW and DCE scans. Radiomics features were calculated from intra- and peritumoral regions, and selected with least absolute shrinkage and selection operator (LASSO) regression to develop radiomics signatures (RSs). Prediction performance of intra- and peritumoral regions in the four modalities were evaluated and compared with area under the receiver-operating characteristic (ROC) curve (AUC), specificity and sensitivity as comparison metrics. The RSs derived from combined intra- and peritumoral regions improved prediction AUCs compared with those from intra- or peritumoral regions alone. DM plus DBT generated better AUCs than the DW plus DCE on predicting Luminal A and Luminal B in the training (Luminal A: 0.859 and 0.805; Luminal B: 0.773 and 0.747) and validation (Luminal A: 0.906 and 0.853; Luminal B: 0.807 and 0.784) cohort. For the prediction of HER2-enriched and TN, the DW plus DCE yielded better AUCs than the DM plus DBT in the training (HER2-enriched: 0.954 and 0.857; TN: 0.877 and 0.802) and validation (HER2-enriched: 0.974 and 0.907; TN: 0.938 and 0.874) cohort. Peritumoral regions can provide complementary information to intratumoral regions for the prediction of molecular subtypes. Compared with MRI, the mammography showed higher AUCs for the prediction of Luminal A and B, but lower AUCs for the prediction of HER2-enriched and TN.

Radiomic Evaluations of the Diagnostic Performance of DM, DBT, DCE MRI, DWI, and Their Combination for the Diagnosisof Breast Cancer.

ObjectivesThis study aims to evaluate digital mammography (DM), digital breast tomosynthesis (DBT), dynamic contrast-enhanced (DCE), and diffusion-weighted (DW) MRI, individually and combined, for the values in the diagnosis of breast cancer, and propose a visualized clinical-radiomics nomogram for potential clinical uses.MethodsA total of 120 patients were enrolled between September 2017 and July 2018, all underwent preoperative DM, DBT, DCE, and DWI scans. Radiomics features were extracted and selected using the least absolute shrinkage and selection operator (LASSO) regression. A radiomics nomogram was constructed integrating the radiomics signature and important clinical predictors, and assessed with the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).ResultsThe radiomics signature derived from DBT plus DM generated a lower area under the ROC curve (AUC) and sensitivity, but a higher specificity compared with that from DCE plus DWI. The nomogram integrating the combined radiomics signature, age, and menstruation status achieved the best diagnostic performance in the training (AUCs, nomogram vs. combined radiomics signature vs. clinical model, 0.975 vs. 0.964 vs. 0.782) and validation (AUCs, nomogram vs. combined radiomics signature vs. clinical model, 0.983 vs. 0.978 vs. 0.680) cohorts. DCA confirmed the potential clinical usefulness of the nomogram.ConclusionsThe DBT plus DM provided a lower AUC and sensitivity, but a higher specificity than DCE plus DWI for detecting breast cancer. The proposed clinical-radiomics nomogram has diagnostic advantages over each modality, and can be considered as an efficient tool for breast cancer screening.

Bone health assessment via digital wrist tomosynthesis in the mammography setting.

Bone fractures attributable to osteoporosis are a significant problem. Though preventative treatment options are available for individuals who are at risk of a fracture, a substantial number of these individuals are not identified due to lack of adherence to bone screening recommendations. The issue is further complicated as standard diagnosis of osteoporosis is based on bone mineral density (BMD) derived from dual energy x-ray absorptiometry (DXA), which, while helpful in identifying many at risk, is limited in fully predicting risk of fracture. It is reasonable to expect that bone screening would become more prevalent and efficacious if offered in coordination with digital breast tomosynthesis (DBT) exams, provided that osteoporosis can be assessed using a DBT modality. Therefore, the objective of the current study was to explore the feasibility of using digital tomosynthesis imaging in a mammography setting. To this end, we measured density, cortical thickness and microstructural properties of the wrist bone, correlated these to reference measurements from microcomputed tomography and DXA, demonstrated the application in vivo in a small group of participants, and determined the repeatability of the measurements. We found that measurements from digital wrist tomosynthesis (DWT) imaging with a DBT scanner were highly repeatable ex vivo (error=0.05%-9.62%) and in vivo (error=0.06%-10.2%). In ex vivo trials, DWT derived BMDs were strongly correlated with reference measurements (R=0.841-0.980), as were cortical thickness measured at lateral and medial cortices (R=0.991 and R=0.959, respectively) and the majority of microstructural measures (R=0.736-0.991). The measurements were quick and tolerated by human patients with no discomfort, and appeared to be different between young and old participants in a preliminary comparison. In conclusion, DWT is feasible in a mammography setting, and informative on bone mass, cortical thickness, and microstructural qualities that are known to deteriorate in osteoporosis. To our knowledge, this study represents the first application of DBT for imaging bone. Future clinical studies are needed to further establish the efficacy for diagnosing osteoporosis and predicting risk of fragility fracture using DWT.

A synthesizing method for signal-enhanced and artifact-reduced mammogram from digital breast tomosynthesis

In this paper, we propose a method for compositing a synthetic mammogram (SM) from digital breast tomosynthesis (DBT) slice images. The method consists of four parts. The first part is image reconstruction of DBT from the acquired projection data by use of backprojection-filtration (BPF) algorithm with a low-frequency boosting scheme and a high-density object reduction technique embedded. Also, a few expectation-maximization (EM) iterations have been additively implemented on top of the BPF algorithm to prepare a separate volume image. The second is generating three kinds of intermediate SMs. A forward projection image and a linear structure weighted forward projection image were computed. A maximum intensity projection of the BPF reconstructed volume image was also generated. The third part is integrating three intermediate SMs. The last is the post-processing of the SM. We scanned two physical phantoms in a prototype DBT scanner, and we have evaluated the performance of the proposed method. We also performed a clinical data study by use of 30 patient data who went through both DBT and digital mammography (DM) scans. Three experienced radiologists have read the SMs generated by several component techniques and also read the DM of each patient, and evaluated the generated SMs. The experimental phantom study and the clinical reader study consistently demonstrated the usefulness of the proposed method.

Systematic approach to a channelized Hotelling model observer implementation for a physical phantom containing mass-like lesions: Application to digital breast tomosynthesis

Purposeto develop a channelized model observer (CHO) that matches human reader (HR) scoring of a physical phantom containing breast simulating structure and mass lesion-like targets for use in quality control of digital breast tomosynthesis (DBT) imaging systems. MethodsA total of 108 DBT scans of the phantom was acquired using a Siemens Inspiration DBT system. The detectability of mass-like targets was evaluated by human readers using a 4-alternative forced choice (4-AFC) method. The percentage correct (PC) values were then used as the benchmark for CHO tuning, again using a 4-AFC method. Three different channel functions were considered: Gabor, Laguerre-Gauss and Difference of Gaussian. With regard to the observer template, various methods for generating the expected signal were studied along with the influence of the number of training images used to form the covariance matrix for the observer template. Impact of bias in the training process on the observer template was evaluated next, as well as HR and CHO reproducibility. ResultsHR performance was most closely matched by 8 Gabor channels with tuned phase, orientation and frequency, using an observer template generated from training image data. Just 24 DBT image stacks were required to give robust CHO performance with 0% bias, although a bias of up to 33% in the training images also gave acceptable performance. CHO and HR reproducibility were similar (on average 3.2 PC versus 3.4 PC). ConclusionsThe CHO algorithm developed matches human reader performance and is therefore a promising candidate for automated readout of phantom studies.

Digital Breast Tomosynthesis imaging using compressed sensing based reconstruction for 10 radiation doses real data

Abstract Purpose Digital Breast Tomosynthesis (DBT) has recently proved promising in producing three-dimensional (3D) images of a breast. Algebraic reconstruction technique (ART), which is one of the most frequently used iterative image reconstruction techniques, has been proposed to provide satisfactory images of the breast in detecting masses and micro-calcifications. However, the greatest limitation of DBT imaging is the level of radiation dose due to the very sensitive nature of the breast. Recently, the effect of total variation (TV) minimization to enhance the image quality and to reduce the noise has been investigated in DBT imaging. Studies dealing with 3D TV minimization with ART have attracted increasing attention in the field of image reconstruction. This work investigates if iterative reconstruction techniques applied without and with TV (ART and ART + TV3D) can help reduce the level of dose in DBT imaging. Methods Projections of a realistic breast phantom (CD Pasmam 1054) were acquired with a Siemens MAMMOMAT DBT scanner at 10 different doses. The effect of dose and the methods in the reconstruction quality was assessed both quantitatively and qualitatively. Results ART + TV3D showed superior results in terms of visual assessment, contrast-to-noise ratio (CNR) and full width at half maximum (FWHM) values, and one-dimensional (1D) profiles compared with ART. CNR values were evaluated for two different regions of interest (ROIs). For instance, CNR values of ROI-1 of ART and of ART + TV3D were 46.380 and 47.675 at 63 mAs, 48.945 and 50.632 at 90 mAs, and 51.248 and 52.867 at 199 mAs, respectively. Additionally, FWHM values for ART and ART + TV3D were 2.373 and 1.758 at 63 mAs, 1.930 and 1.467 at 90 mAs, and 1.591 and 1.223 at 199 mAs, respectively. Conclusions The results suggested that a compressed sensing based iterative reconstruction method (ART + TV3D) could help decrease the radiation dose level that is one of the most critical limitations of DBT imaging.

OA243] 3D Dose distribution in two clinical digital breast tomosynthesis units: A phantom study

Purpose Measurements of the dose volume distribution in digital breast tomosynthesis (DBT) are lacking. They are useful for validation of Monte Carlo simulations in DBT dosimetry and also for devising models of radio-induced cancer risk in DBT exams. Due to the different scanner protocols (scan angle and spectra) the dose distribution can vary among DBT units and its determination is useful in scanner optimization and performance comparisons. We measured the dose volume distribution in breast phantoms for two DBT scanners. Methods We employed calibrated XR-QA2 radiochromic film pieces (25 × 15 cm2) placed within two layered breast phantoms: a circular homogenous PMMA phantom composed of five 1-cm thick layers with a diameter of 20 cm and a commercial BR3D CIRS mammographic phantom. Dose maps were measured at several horizontal planes in the phantom, including the entrance and exit surfaces. Two DBT scanners were employed at the University Hospital of Leuven: Hologic Selenia Dimensions and Mammomat Siemens Inspiration Prime, operated at 31 (HVL = 0.53 mmAl) and 29 kVp (HVL = 0.54 mmAl), respectively. Results The dose distributions within the PMMA phantom did not largely depend on the scanner type. However, the measurements with the heterogeneous CIRS phantom showed larger dose differences. For the Siemens unit, the exit dose was 21% of the entrance surface dose, for both phantoms. For the Hologic unit, this value reduced to 14% and 12%, for the CIRS and the PMMA phantoms, respectively. The in-plane dose spread in PMMA, evaluated as the percentage difference from the dose in a fixed region at the chest wall, was less than a few percent for both scanners. In the case of the CIRS phantom, the maximum in-plane dose spread was 10% for the Hologic and it was reduced to less than 5% for the Siemens unit. Conclusions 3D dose distributions were measured using two different DBT scanners. The dose spread was larger for the phantom which replicate the heterogeneous tissues distribution of the breasts. In this case, the dose spread in planes parallel to the detector is less than 10% and the entrance-to-exit dose ratio is less than 21%, for the two investigated DBT units.

Model observer for assessing digital breast tomosynthesis for multi-lesion detection in the presence of anatomical noise

Model observers are widely used in task-based assessments of medical image quality. The presence of multiple abnormalities in a single set of images, such as in multifocal multicentric breast cancer (MFMC), has an immense clinical impact on treatment planning and survival outcomes. Detecting multiple breast tumors is challenging as MFMC is relatively uncommon, and human observers do not know the number or locations of tumors a priori. Digital breast tomosynthesis (DBT), in which an x-ray beam sweeps over a limited angular range across the breast, has the potential to improve the detection of multiple tumors. However, prior studies of DBT image quality all focus on unifocal breast cancers. In this study, we extended our 2D multi-lesion (ML) channelized Hotelling observer (CHO) into a 3D ML-CHO that detects multiple lesions from volumetric imaging data. Then we employed the 3D ML-CHO to identify optimal DBT acquisition geometries for detection of MFMC. Digital breast phantoms with multiple embedded synthetic lesions were scanned by simulated DBT scanners of different geometries (wide/narrow angular span, different number of projections per scan) to simulate MFMC cases. With new implementations of 3D partial least squares (PLS) and modified Laguerre–Gauss (LG) channels, the 3D ML-CHO made detection decisions based upon the overall information from individual DBT slices and their correlations. Our evaluation results show that: (1) the 3D ML-CHO could achieve good detection performance with a small number of channels, and 3D PLS channels on average outperform the counterpart LG channels; (2) incorporating locally varying anatomical backgrounds and their correlations as in the 3D ML-CHO is essential for multi-lesion detection; (3) the most effective DBT geometry for detection of MFMC may vary when the task of clinical interest changes, and a given DBT geometry may not yield images that are equally informative for detecting MF, MC, and unifocal cancers.

Abstract ID: 60 Monte Carlo simulation and experimental validation of glandular dose coefficients in digital breast tomosynthesis

The mean radiation dose to the glandular tissue and its dependence on the irradiation geometry, beam quality, breast size and composition in digital breast tomosynthesis (DBT) exams have been studied extensively via Monte Carlo calculations [1] . On the other hand, there are few comprehensive studies on the dose distribution within the irradiated breast [2] . The distribution of glandular dose for breast irradiation from a plurality of angles, as occurs in DBT, may be of large interest in scanner optimization as well as for developing suitable models for the evaluation of the cancer risk related to the X-ray exposure for non–homogeneous irradiation. For this reason, it is of interest to evaluate the level of homogeneity of the dose spread, via the assessment of 3D dose maps in breast models during a DBT scan. This work aimed at evaluating, via Monte Carlo (MC) simulations and measurements using radiochromic films, the dose distribution within compressed layered breast phantoms during DBT scans. For this purpose, two phantoms were employed: a PMMA homogeneous phantom and a heterogeneous phantom simulating a 50% glandular breast. A series of pre-calibrated (vs free-in-air air kerma) film pieces were inserted between the phantom slices and the 3D dose maps were measured for a set of DBT scans on different commercial units, for different sample thicknesses, at various exposure technique factors. We developed an MC code based on GEANT4 toolkit ver. 10.00, simulating the clinical setup specifications, whose results have been compared to measurements. This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 692097 for the MaXIMA project: Three dimensional breast cancer models for X-ray imaging research.

Detector Blur and Correlated Noise Modeling for Digital Breast Tomosynthesis Reconstruction.

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

Multimodal breast cancer imaging using coregistered dynamic diffuse optical tomography and digital breast tomosynthesis.

Diffuse optical tomography (DOT) is emerging as a noninvasive functional imaging method for breast cancer diagnosis and neoadjuvant chemotherapy monitoring. In particular, the multimodal approach of combining DOT with x-ray digital breast tomosynthesis (DBT) is especially synergistic as DBT prior information can be used to enhance the DOT reconstruction. DOT, in turn, provides a functional information overlay onto the mammographic images, increasing sensitivity and specificity to cancer pathology. We describe a dynamic DOT apparatus designed for tight integration with commercial DBT scanners and providing a fast (up to 1 Hz) image acquisition rate to enable tracking hemodynamic changes induced by the mammographic breast compression. The system integrates 96 continuous-wave and 24 frequency-domain source locations as well as 32 continuous wave and 20 frequency-domain detection locations into low-profile plastic plates that can easily mate to the DBT compression paddle and x-ray detector cover, respectively. We demonstrate system performance using static and dynamic tissue-like phantoms as well as in vivo images acquired from the pool of patients recalled for breast biopsies at the Massachusetts General Hospital Breast Imaging Division.

Phase-contrast Digital Breast Tomosynthesis vs phase-contrast breast CT: An image quality phantom study with synchrotron radiation

Introduction Digital Mammography is a fundamental technique in breast cancer diagnosis. DM returns a two-dimensional representation of a three-dimensional object. Therefore, tissues belonging to different planes are all projected on the same X-ray image plane. Digital Breast Tomosynthesis (DBT) and Breast Computed Tomography (BCT) are two forms of 3D X-ray breast imaging for diagnostic exam developed to overcome this limitation. Recently, it has been shown that for the generation of the radiographic contrast it is possible to exploit also the information related to the wave phase shift introduced in the propagation of X-rays in matter. Purpose To compare image quality in monochromatic DBT and BCT scans with propagation based phase contrast imaging of a 3D breast phantom. Materials and methods The CIRS Model 020 BR3D is used to evaluate the image quality in the two modalities. The standard package consists of 6 slabs of thickness of 1 cm, each slice is produced by tissue equivalent materials in an approximate 50/50 ratio by weight. The central slab of the stack contains assortments of simulated microcalcifications, fibers and masses. The experiment was conducted on the biomedical beamline ID 17 at the European Synchrotron Radiation Facility. The comparison will be made in terms of noise, contrast, CNR, SNR and spatial resolution, for simulated masses and microcalcifications. Results First measurements show slightly higher values for CNR and C for masses in CT images than in DBT images. Conclusion The image quality in DBT and BCT monochromatic images of a 3D breast phantom was evaluated.