Digital Breast Tomosynthesis Projections Research Articles

Image simulation and realism evaluation for mammography and tomosynthesis based on the detailed breast phantom

The virtual image system is a key component of virtual clinical trials (VCTs) which can be utilized to evaluate and improve medical imaging devices. The main limitation of VCTs in the Chinese female breast is the lack of detailed structure in the breast model employed in the Chinese specification for testing of quality control in mammography. In this paper, based on Chinese female breast parameters, detailed breast phantoms with different glandularity (fatty, glandular, dense) and compressed breast thicknesses (CBT) (2–7 cm) were generated. Digital mammography (DM) and digital breast tomosynthesis (DBT) projections for these phantoms were simulated with clinical system configuration. Compressed breast volumes were reconstructed through DBT projections with different angles. Power spectrum analysis and fractal dimension measurement were applied for simulated and clinical images. The results show that the average power law exponents and standard deviation (DM, DBT) for clinical and simulated images were (3.26 ± 0.29, 3.33 ± 0.42) and (3.63 ± 0.25, 3.18 ±0.36) respectively. The average fractal dimensions and standard deviation (DM, DBT) for clinical and simulated images were (2.36 ± 0.03, 2.41 ± 0.05) and (2.10 ± 0.01, 2.26 ± 0.06) respectively. We constructed two virtual imaging systems for DM and DBT and obtained the imaging data based on the three-dimensional (3D) detailed breast phantoms. These results indicate that the texture indexes of simulated images are similar to that of the clinical images, and validate that these breast phantoms are suitable for imaging in VCTs requiring realistic anatomy with the Chinese female population. Results also show that the values of power law exponents for clinical and simulated images in DM and DBT are positively correlated with the value of breast glandularity, but the values of fractal dimensions keep steady for different glandularity.

A deep learning approach to estimate x-ray scatter in digital breast tomosynthesis: From phantom models to clinical applications.

Digital breast tomosynthesis (DBT) has gained popularity as breast imaging modality due to its pseudo-3D reconstruction and improved accuracy compared to digital mammography. However, DBT faces challenges in image quality and quantitative accuracy due to scatter radiation. Recent advancements in deep learning (DL) have shown promise in using fast convolutional neural networks for scatter correction, achieving comparable results to Monte Carlo (MC) simulations. To predict the scatter radiation signal in DBT projections within clinically-acceptable times and using only clinically-available data, such as compressed breast thickness and acquisition angle. MC simulations to obtain scatter estimates were generated from two types of digital breast phantoms. One set consisted of 600 realistically-shaped homogeneous breast phantoms for initial DL training. The other set was composed of 80 anthropomorphic phantoms, containing realistic internal tissue texture, aimed at fine tuning the DL model for clinical applications. The MC simulations generated scatter and primary maps per projection angle for a wide-angle DBT system. Both datasets were used to train (using 7680 projections from homogeneous phantoms), validate (using 960 and 192 projections from the homogeneous and anthropomorphic phantoms, respectively), and test (using 960 and 48 projections from the homogeneous and anthropomorphic phantoms, respectively) the DL model. The DL output was compared to the corresponding MC ground truth using both quantitative and qualitative metrics, such as mean relative and mean absolute relative differences (MRD and MARD), and to previously-published scatter-to-primary (SPR) ratios for similar breast phantoms. The scatter corrected DBT reconstructions were evaluated by analyzing the obtained linear attenuation values and by visual assessment of corrected projections in a clinical dataset. The time required for training and prediction per projection, as well as the time it takes to produce scatter-corrected projection images, were also tracked. The quantitative comparison between DL scatter predictions and MC simulations showed a median MRD of 0.05% (interquartile range (IQR), -0.04% to 0.13%) and a median MARD of 1.32% (IQR, 0.98% to 1.85%) for homogeneous phantom projections and a median MRD of -0.21% (IQR, -0.35% to -0.07%) and a median MARD of 1.43% (IQR, 1.32% to 1.66%) for the anthropomorphic phantoms. The SPRs for different breast thicknesses and at different projection angles were within±15% of the previously-published ranges. The visual assessment showed good prediction capabilities of the DL model with a close match between MC and DL scatter estimates, as well as between DL-based scatter corrected and anti-scatter grid corrected cases. The scatter correction improved the accuracy of the reconstructed linear attenuation of adipose tissue, reducing the error from -16% and -11% to -2.3% and 4.4% for an anthropomorphic digital phantom and clinical case with similar breast thickness, respectively. The DL model training took 40 min and prediction of a single projection took less than 0.01s. Generating scatter corrected images took 0.03s per projection for clinical exams and 0.16s for one entire projection set. This DL-based method for estimating the scatter signal in DBT projections is fast and accurate, paving the way for future quantitative applications.

Denoising digital breast tomosynthesis projections using deep learning with synthetic data as training set.

Image denoising based on deep neural networks (DNN) needs a big dataset containing digital breast tomosynthesis (DBT) projections acquired in different radiation doses to be trained, which is impracticable. Therefore, we propose extensively investigating the use of synthetic data generated by software for training DNNs to denoise DBT real data. The approach consists of generating a synthetic dataset representative of the DBT sample space by software, containing noisy and original images. Synthetic data were generated in two different ways: (a)virtual DBT projections generated by OpenVCT and (b)noisy images synthesized from photography regarding noise models used in DBT (e.g., Poisson-Gaussian noise). Then, DNN-based denoising techniques were trained using a synthetic dataset and tested for denoising physical DBT data. Results were evaluated in quantitative (PSNR and SSIM measures) and qualitative (visual analysis) terms. Furthermore, a dimensionality reduction technique (t-SNE) was used for visualization of sample spaces of synthetic and real datasets. The experiments showed that training DNN models with synthetic data could denoise DBT real data, achieving competitive results to traditional methods in quantitative terms but showing a better balance between noise filtering and detail preservation in a visual analysis. T-SNE enables us to visualize if synthetic and real noises are in the same sample space. We propose a solution for the lack of suitable training data to train DNN models for denoising DBT projections, showing that we just need the synthesized noise to be in the same sample space as the target image.

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.

Novel Perlin-based Phantoms Using 3D Models of Compressed Breast Shape and Fractal Noise.

Virtual clinical trials (VCTs) have been used widely to evaluate digital breast tomosynthesis (DBT) systems. VCTs require realistic simulations of the breast anatomy (phantoms) to characterize lesions and to estimate risk of masking cancers. This study introduces the use of Perlin-based phantoms to optimize the acquisition geometry of a novel DBT prototype. These phantoms were developed using a GPU implementation of a novel library called Perlin-CuPy. The breast anatomy is simulated using 3D models under mammography cranio-caudal compression. In total, 240 phantoms were created using compressed breast thickness, chest-wall to nipple distance, and skin thickness that varied in a {[35, 75], [59, 130), [1.0, 2.0]} mm interval, respectively. DBT projections and reconstructions of the phantoms were simulated using two acquisition geometries of our DBT prototype. The performance of both acquisition geometries was compared using breast volume segmentations of the Perlin phantoms. Results show that breast volume estimates are improved with the introduction of posterior-anterior motion of the x-ray source in DBT acquisitions. The breast volume is overestimated in DBT, varying substantially with the acquisition geometry; segmentation errors are more evident for thicker and larger breasts. These results provide additional evidence and suggest that custom acquisition geometries can improve the performance and accuracy in DBT. Perlin phantoms help to identify limitations in acquisition geometries and to optimize the performance of the DBT prototypes.

Computer simulations of case difficulty in digital breast tomosynthesis using virtual clinical trials.

Virtual clinical trials (VCTs) require computer simulations of representative patients and images to evaluate and compare changes in performance of imaging technologies. The simulated images are usually interpreted by model observers whose performance depends upon the selection of imaging cases used in training evaluation models. This work proposes an efficient method to simulate and calibrate soft tissue lesions, which matches the detectability threshold of virtual and human readings. Anthropomorphic breast phantoms were used to evaluate the simulation of four mass models (I-IV) that vary in shape and composition of soft tissue. Ellipsoidal (I) and spiculated (II-IV) masses were simulated using composite voxels with partial volumes. Digital breast tomosynthesis projections and reconstructions of a clinical system were simulated. Channelized Hotelling observers (CHOs) were evaluated using reconstructed slices of masses that varied in shape, composition, and density of surrounded tissue. The detectability threshold of each mass model was evaluated using receiver operating characteristic (ROC) curves calculated with the CHO's scores. The area under the curve (AUC) of each calibrated mass model were within the 95% confidence interval (mean AUC [95% CI]) reported in a previous reader study (0.93 [0.89, 0.97]). The mean AUC [95% CI] obtained were 0.94 [0.93, 0.96], 0.92 [0.90, 0.93], 0.92 [0.90, 0.94], 0.93 [0.92, 0.95] for models I to IV, respectively. The mean AUC results varied substantially as a function of shape, composition, and density of surrounded tissue. For successful VCTs, lesions composed of soft tissue should be calibrated to simulate imaging cases that match the case difficulty predicted by human readers. Lesion composition, shape, and size are parameters that should be carefully selected to calibrate VCTs.

Effect of glandularity on the detection of simulated cancers in planar, tomosynthesis, and synthetic 2D imaging of the breast using a hybrid virtual clinical trial.

The purpose of this study was to measure the threshold diameter of calcifications and masses for 2D imaging, digital breast tomosynthesis (DBT), and synthetic 2D images, for a range of breast glandularities. This study shows the limits of detection for each of the technologies and the strengths and weaknesses of each in terms of visualizing the radiological features of small cancers. Mathematical voxel breast phantoms with glandularities by volume of 9%, 18%, and 30% with a thickness of 53mm were created. Simulated ill-defined masses and calcification clusters with a range of diameters were inserted into some of these breast models. The imaging characteristics of a Siemens Inspiration X-ray system were measured for a 29kV, tungsten/rhodium anode/filter combination. Ray tracing through the breast models was undertaken to create simulated 2D and DBT projection images. These were then modified to adjust the image sharpness, and to add scatter and noise. The mean glandular doses for the images were 1.43, 1.47, and 1.47 mGy for 2D and 1.92, 1.97, and 1.98 mGy for DBT for the three glandularities. The resultant images were processed to create 2D, DBT planes and synthetic 2D images. Patches of the images with or without a simulated lesion were extracted, and used in a four-alternative forced choice study to measure the threshold diameters for each imaging mode, lesion type, and glandularity. The study was undertaken by six physicists. The threshold diameters of the lesions were 6.2, 4.9, and 6.7mm (masses) and 225, 370, and 399μm, (calcifications) for 2D, DBT, and synthetic 2D, respectively, for a breast glandularity of 18%. The threshold diameter of ill-defined masses is significantly smaller for DBT than for both 2D (p≤0.006) and synthetic 2D (p≤0.012) for all glandularities. Glandularity has a significant effect on the threshold diameter of masses, even for DBT where there is reduced background structure in the images. The calcification threshold diameters for 2D images were significantly smaller than for DBT and synthetic 2D for all glandularities. There were few significant differences for the threshold diameter of calcifications between glandularities, indicating that the background structure has little effect on the detection of calcifications. We measured larger but nonsignificant differences in the threshold diameters for synthetic 2D imaging than for 2D imaging for masses in the 9% (p=0.059) and 18% (p=0.19) glandularities. The threshold diameters for synthetic 2D imaging were larger than for 2D imaging for calcifications (p<0.001) for all glandularities. We have shown that glandularity has only a small effect on the detection of calcifications, but the threshold diameter of masses was significantly larger for higher glandularity for all of the modalities tested. We measured nonsignificantly larger threshold diameters for synthetic 2D imaging than for 2D imaging for masses at the 9% (p=0.059) and 18% (p=0.19) glandularities and significantly larger diameters for calcifications (p<0.001) for all glandularities. The lesions simulated were very subtle and further work is required to examine the clinical effect of not seeing the smallest calcifications in clusters.

Predicting Breast Density of Digital Breast Tomosynthesis from 2D Mammograms

Breast density may be used as a predictor of breast cancer risk and can measure the condition of tissues on mammograms. This research developed a computer-aided diagnosis (CAD) system to predict breast density on digital breast tomosynthesis (DBT) images. We used two-dimensional (2D) mammograms to train the linear discriminant analysis (LDA) classifier. Then load the DBT projection image to predict breast density. Experimental results show that LDA is better than other classification methods, such as one rule, naive Bayes, decision tree and support vector machine. The breast density prediction accuracy of DBT is 80%.

Evaluation of denoising digital breast tomosynthesis data in both projection and image domains and a study of noise model on digital breast tomosynthesis image domain.

Digital breast tomosynthesis (DBT) is an imaging technique created to visualize 3-D mammary structures for the purpose of diagnosing breast cancer. This imaging technique is based on the principle of computed tomography. Due to the use of a dangerous ionizing radiation, the "as low as reasonably achievable" (ALARA) principle should be respected, aiming at minimizing the radiation dose to obtain an adequate examination. Thus, a noise filtering method is a fundamental step to achieve the ALARA principle, as the noise level of the image increases as the radiation dose is reduced, making it difficult to analyze the image. In our work, a double denoising approach for DBT is proposed, filtering in both projection (prereconstruction) and image (postreconstruction) domains. First, in the prefiltering step, methods were used for filtering the Poisson noise. To reconstruct the DBT projections, we used the filtered backprojection algorithm. Then, in the postfiltering step, methods were used for filtering Gaussian noise. Experiments were performed on simulated data generated by open virtual clinical trials (OpenVCT) software and on a physical phantom, using several combinations of methods in each domain. Our results showed that double filtering (i.e., in both domains) is not superior to filtering in projection domain only. By investigating the possible reason to explain these results, it was found that the noise model in DBT image domain could be better modeled by a Burr distribution than a Gaussian distribution. Finally, this important contribution can open a research direction in the DBT denoising problem.

Synthesizing mammogram from digital breast tomosynthesis

The purpose of this study is to develop a new method for generating synthesized mammogram (SM) from digital breast tomosynthesis (DBT) and to assess its potential as an adjunct to DBT. We first applied multiscale bilateral filtering to the reconstructed DBT slices to enhance the high-frequency features and reduce noise. A maximum intensity projection (MIP) image was then obtained from the high-frequency components of the DBT slices. A multiscale image fusion method was designed to combine the MIP image and the central DBT projection view into an SM and further enhance the high-frequency features. We conducted a pilot reader study to visually assess the image quality of SM in comparison to full field digital mammograms (FFDM). For each DBT craniocaudal or mediolateral view, a clinical FFDM of the corresponding view was retrospectively collected. Three MQSA radiologists, blinded to the pathological and other clinical information, independently interpreted the SM and the corresponding FFDM side by side marked with the lesion locations. The differences in the BI-RADS assessments of both MCs and masses between SM and FFDM did not achieve statistical significance for all three readers. The conspicuity of MCs on SM was superior to that on FFDM and the BI-RADS assessments of MCs were comparable while the conspicuity of masses on SM was degraded and interpretation on SM was less accurate than that on FFDM. The SM may be useful for efficient prescreening of MCs in DBT but the DBT should be used for detection and characterization of masses.

Preoperative Staging in Women with Known Breast Cancer: Comparison between Digital Breast Tomosynthesis (DBT) and Magnetic Resonance Imaging (MRI)

Objectives&#x0D; &#x0D; To prospectively evaluate the accuracy in tumor extent and size assessment of Digital Breast Tomosynthesis (DBT) and Magnetic Resonance Imaging (MRI) in women with known breast cancer, with pathological size as the gold standard.&#x0D; &#x0D; Methods&#x0D; &#x0D; From May 2014 to April 2016, 50 patients with known breast cancer were enrolled in our prospective study. All patients underwent MRI on a 3T magnet and DBT projections. Two radiologists, with 15 and 7 years of experience in breast imaging respectively, evaluated in consensus each imaging set unaware of the final histological examination. MR and DBT sensitivity, PPV and accuracy were calculated, using histology as the gold standard. McNemar test was used to compare MR and DBT sensitivity. Correlation and regression analyses were used to evaluate MRI vs Histology, DBT vs Histology and MRI vs DBT lesions tumor size agreement to histological results. &#x0D; &#x0D; Results&#x0D; &#x0D; On histological examination 70 lesions were detected. MRI showed 100% sensitivity, 96% PPV and 96% accuracy; DBT sensitivity was 81%, PPV 92% and accuracy 77%. McNemar test p-value was 0.0003. Lesions size Pearson correlation coefficient was 0.97 for MRI vs Histology, 0.92 for DBT vs Histology, (p-value&amp;lt;0.0001). MRI vs DBT regression coefficient was 0.83.&#x0D; &#x0D; Conclusions&#x0D; &#x0D; MRI confirmed to be the most accurate imaging technique in preoperative staging of breast cancer. However, DBT showed very good accuracy, sensitivity and tumor size assessment and could be a valid tool for preoperative staging when MRI is contraindicated.

Restoration of low-dose digital breast tomosynthesis

In breast cancer screening, the radiation dose must be kept to the minimum necessary to achieve the desired diagnostic objective, thus minimizing risks associated with cancer induction. However, decreasing the radiation dose also degrades the image quality. In this work we restore digital breast tomosynthesis (DBT) projections acquired at low radiation doses with the goal of achieving a quality comparable to that obtained from current standard full-dose imaging protocols. A multiframe denoising algorithm was applied to low-dose projections, which are filtered jointly. Furthermore, a weighted average was used to inject a varying portion of the noisy signal back into the denoised one, in order to attain a signal-to-noise ratio comparable to that of standard full-dose projections. The entire restoration framework leverages a signal-dependent noise model with quantum gain which varies both upon the projection angle and on the pixel position. A clinical DBT system and a 3D anthropomorphic breast phantom were used to validate the proposed method, both on DBT projections and slices from the 3D reconstructed volume. The framework is shown to attain the standard full-dose image quality from data acquired at 50% lower radiation dose, whereas progressive loss of relevant details compromises the image quality if the dosage is further decreased.

Virtual assessment of stereoscopic viewing of digital breast tomosynthesis projection images.

Digital breast tomosynthesis (DBT) acquires a series of projection images from different angles as an x-ray source rotates around the breast. Such imaging geometry lends DBT naturally to stereoscopic viewing as two projection images with a reasonable separation angle can easily form a stereo pair. This simulation study assessed the efficacy of stereo viewing of DBT projection images. Three-dimensional computational breast phantoms with realistically shaped synthetic lesions were scanned by three simulated DBT systems. The projection images were combined into a sequence of stereo pairs and presented to a stereomatching-based model observer for deciding lesion presence. Signal-to-noise ratio was estimated, and the detection performance with stack viewing of reconstructed slices was the benchmark. We have shown that: (1)stereo viewing of projection images may underperform stack viewing of reconstructed slices for current DBT geometries; (2)DBT geometries may impact the efficacy of the two viewing modes differently: narrow-arc and wide-arc geometries may be better for stereo viewing and stack viewing, respectively; (3)the efficacy of stereo viewing may be more robust than stack viewing to reductions in dose. While in principle stereo viewing is potentially effective for visualizing DBT data, effective stereo viewing may require specifically optimized DBT image acquisition.

Visual-search observers for assessing tomographic x-ray image quality.

Mathematical model observers commonly used for diagnostic image-quality assessments in x-ray imaging research are generally constrained to relatively simple detection tasks due to their need for statistical prior information. Visual-search (VS) model observers that employ morphological features in sequential search and analysis stages have less need for such information and fewer task constraints. The authors compared four VS observers against human observers and an existing scanning model observer in a pilot study that quantified how mass detection and localization in simulated digital breast tomosynthesis (DBT) can be affected by the number P of acquired projections. Digital breast phantoms with embedded spherical masses provided single-target cases for a localization receiver operating characteristic (LROC) study. DBT projection sets based on an acquisition arc of 60° were generated for values of P between 3 and 51. DBT volumes were reconstructed using filtered backprojection with a constant 3D Butterworth postfilter; extracted 2D slices were used as test images. Three imaging physicists participated as observers. A scanning channelized nonprewhitening (CNPW) observer had knowledge of the mean lesion-absent images. The VS observers computed an initial single-feature search statistic that identified candidate locations as local maxima of either a template matched-filter (MF) image or a gradient-template MF (GMF) image. Search inefficiencies that modified the statistic were also considered. Subsequent VS candidate analyses were carried out with (i) the CNPW statistical discriminant and (ii) the discriminant computed from GMF training images. These location-invariant discriminants did not utilize covariance information. All observers read 36 training images and 108 study images per P value. Performance was scored in terms of area under the LROC curve. Average human-observer performance was stable for P between 7 and 35. In the absence of search inefficiencies, the VS models based on the GMF analysis provided the best correlation (Pearson ρ ≥ 0.62) with the human results. The CNPW-based VS observers deviated from the humans primarily at lower values of P. In this limited study, search inefficiencies allowed for good quantitative agreement with the humans for most of the VS observers. The computationally efficient training requirements for the VS observer are suitable for high-resolution imaging, indicating that the observer framework has the potential to overcome important task limitations of current model observers for x-ray applications.

Compressed-sensing (CS)-based digital breast tomosynthesis (DBT) reconstruction for low-dose, accurate 3D breast X-ray imaging

In practical applications of three-dimensional (3D) tomographic techniques, such as digital breast tomosynthesis (DBT), computed tomography (CT), etc., there are often challenges for accurate image reconstruction from incomplete data. In DBT, in particular, the limited-angle and few-view projection data are theoretically insufficient for exact reconstruction; thus, the use of common filtered-backprojection (FBP) algorithms leads to severe image artifacts, such as the loss of the average image value and edge sharpening. One possible approach to alleviate these artifacts may employ iterative statistical methods because they potentially yield reconstructed images that are in better accordance with the measured projection data. In this work, as another promising approach, we investigated potential applications to low-dose, accurate DBT imaging with a state-of-the-art reconstruction scheme based on compressed-sensing (CS) theory. We implemented an efficient CS-based DBT algorithm and performed systematic simulation works to investigate the imaging characteristics. We successfully obtained DBT images of substantially very high accuracy by using the algorithm and expect it to be applicable to developing the next-generation 3D breast X-ray imaging system.

Computational assessment of stereoscopic viewing a sequence of stereo pairs of breast tomosynthesis projection images.

Digital breast tomosynthesis (DBT) is a 3D imaging technology in which an x-ray fan beam rotates around the breast, producing a series of projection images. The imaging geometry of DBT lends itself naturally to stereo viewing because a stereo pair can be easily formed by two projection images with a reasonable separation angle. Stereo viewing may reveal the 3D structures of breasts thus has the potential to increase the sensitivity and specificity of breast imaging. In this study, we conduct a simulation study that mimics the detection of breast lesions on stereoscopic viewing of DBT projections. The presentation approach we investigate here is one in which the reader is presented with a sequence of stereo pairs from a rotating point of view. We render voxel datasets that contain random 3D power-law noise to model normal breast tissues with different breast densities. A 3D Gaussian signal is inserted to some of the datasets to model the presence of a breast lesion. Sequences of stereo pairs of projection images are generated for each voxel dataset by varying the projection angles of the two views. The diagnostic performance, in terms of the accuracy of binary decisions on the presence of the simulated lesions, is evaluated with a numerical model observer.

Comparative power law analysis of structured breast phantom and patient images in digital mammography and breast tomosynthesis

This work characterizes three candidate mammography phantoms with structured background in terms of power law analysis in the low frequency region of the power spectrum for 2D (planar) mammography and digital breast tomosynthesis (DBT). The study was performed using three phantoms (spheres in water, Voxmam, and BR3D CIRS phantoms) on two DBT systems from two different vendors (Siemens Inspiration and Hologic Selenia Dimensions). Power spectra (PS) were calculated for planar projection, DBT projection, and reconstructed images and curve fitted in the low frequency region from 0.2 to 0.7 mm(-1) with a power law function characterized by an exponent β and magnitude κ. The influence of acquisition dose and tube voltage on the power law parameters was first explored. Then power law parameters were calculated from images acquired with the same anode∕filter combination and tube voltage for the three test objects, and compared with each other. Finally, PS curves for automatic exposure controlled acquisitions (anode∕filter combination and tube voltages selected by the systems based on the breast equivalent thickness of the test objects) were compared against PS analysis performed on patient data (for Siemens 80 and for Hologic 48 mammograms and DBT series). Dosimetric aspects of the three test objects were also examined. The power law exponent (β) was found to be independent of acquisition dose for planar mammography but varied more for DBT projections of the sphere-phantom. Systematic increase of tube voltage did not affect β but decreased κ, both in planar and DBT projection phantom images. Power spectra of the BR3D phantom were closer to those of the patients than these of the Voxmam phantom; the Voxmam phantom gave high values of κ compared to the other phantoms and the patient series. The magnitude of the PS curves of the BR3D phantom was within the patient range but β was lower than the average patient value. Finally, PS magnitude for the sphere-phantom coincided with the patient curves for Siemens but was lower for the Hologic system. Close agreement of doses for all three phantoms with patient doses was found. Power law parameters of the phantoms were close to those of the patients but no single phantom matched in terms of both magnitude (κ) and texture (β) for the x-ray systems in this work. PS analysis of structured phantoms is feasible and this methodology can be used to suggest improvements in phantom design.

Measurements of system sharpness for two digital breast tomosynthesis systems

The aim of this work was to propose system sharpness parameters for digital breast tomosynthesis (DBT) systems that include the influence of focus size and focus motion for use in quality assurance protocols. X-ray focus size was measured using a multiple pinhole test object, while detector presampling modulation transfer function (MTF) was measured from projection images of a 10 cm × 10 cm, 1 mm thick steel edge, for the Siemens Inspiration and Hologic Selenia Dimensions DBT systems. The height of the edge above the table was then varied from 1 to 78 mm. The MTF expected from theory for the projection images was calculated from the measured detector MTF, focus size MTF and focus motion MTF and was compared against measured curves. Two methods were used to measure the in-plane MTF in the DBT volume: a tungsten wire of diameter 25 µm and an Al edge 0.2 mm thick, both imaged with a 15 mm thick poly(methyl methacrylate) (PMMA) plate. The in-depth point spread function (PSF) was measured using an angled tungsten wire. The full 3D MTF was estimated with a 0.5 mm diameter aluminium bead held in a 45 mm thick PMMA phantom, with the bead 15 and 65 mm above the table. Inspiration DBT projection images are saved at native detector resolution (85 µm), while the Dimensions re-bins projections to 140 µm pixels (2 × 2 binning); both systems used 2 × 2 binning of projection data before reconstruction. The 50% point for the MTF (MTF0.50) measured in the DBT projection images for the tube-travel direction fell as a function of height above the table from 3.60 to 0.90 mm−1 for the Inspiration system and from 2.50 to 1.20 mm−1 for the Dimensions unit. The maximum deviation of measured MTF0.50 from the calculated value was 13%. MTF0.50 measured in-plane (tube-travel direction) fell as a function of height above the table from 1.66 to 0.97 mm−1 for the Inspiration system and from 2.21 to 1.31 mm−1 for the Dimensions system. The full-width half-maximum for the in-depth PSF was 3.0 and 5.9 mm for the Inspiration and Dimensions systems, respectively. There was no difference in the 3D MTF curves, sectioned in the tube-travel direction, for bead heights of 15 and 65 mm above the table. A 25 µm tungsten wire held within a 15 mm thick PMMA plate was found to be a suitable test object for measurement of in-plane MTF. Evaluation of MTF as a function of height above the table, both in the projection images and in the reconstructed planes, provides important information on the impact of focus size and focus motion on the DBT system's imaging performance.

Penalized maximum likelihood reconstruction for improved microcalcification detection in breast tomosynthesis.

We examined the application of an iterative penalized maximum likelihood (PML) reconstruction method for improved detectability of microcalcifications (MCs) in digital breast tomosynthesis (DBT). Localized receiver operating characteristic (LROC) psychophysical studies with human observers and 2-D image slices were conducted to evaluate the performance of this reconstruction method and to compare its performance against the commonly used Feldkamp FBP algorithm. DBT projections were generated using rigorous computer simulations that included accurate modeling of the noise and detector blur. Acquisition dose levels of 0.7, 1.0, and 1.5 mGy in a 5-cm-thick compressed breast were tested. The defined task was to localize and detect MC clusters consisting of seven MCs. The individual MC diameter was 150 μm. Compressed-breast phantoms derived from CT images of actual mastectomy specimens provided realistic background structures for the detection task. Four observers each read 98 test images for each combination of reconstruction method and acquisition dose. All observers performed better with the PML images than with the FBP images. With the acquisition dose of 0.7 mGy, the average areas under the LROC curve (A(L)) for the PML and FBP algorithms were 0.69 and 0.43, respectively. For the 1.0-mGy dose, the values of A(L) were 0.93 (PML) and 0.7 (FBP), while the 1.5-mGy dose resulted in areas of 1.0 and 0.9, respectively, for the PML and FBP algorithms. A 2-D analysis of variance applied to the individual observer areas showed statistically significant differences (at a significance level of 0.05) between the reconstruction strategies at all three dose levels. There were no significant differences in observer performance for any of the dose levels.

Breast Percent Density: Estimation on Digital Mammograms and Central Tomosynthesis Projections

To evaluate inter- and intrareader agreement in breast percent density (PD) estimation on clinical digital mammograms and central digital breast tomosynthesis (DBT) projection images. This HIPAA-compliant study had institutional review board approval; all patients provided informed consent. Breast PD estimation was performed on the basis of anonymized digital mammograms and central DBT projections in 39 women (mean age, 51 years; range, 31-80 years). All women had recently detected abnormalities or biopsy-proved cancers. PD was estimated by three experienced readers on the mediolateral oblique views of the contralateral breasts by using software; each reader repeated the estimation after 2 months. Spearman correlations of inter- and intrareader and intermodality PD estimates, as well as kappa statistics between categoric PD estimates, were computed. High correlation (rho = 0.91) was observed between PD estimates on digital mammograms and those on central DBT projections, averaged over all estimations; the corresponding kappa coefficient (0.79) indicated substantial agreement. Mean interreader agreement for PD estimation on central DBT projections (rho = 0.85 +/- 0.05 [standard deviation]) was significantly higher (P < .01) than that for PD estimation on digital mammograms (rho = 0.75 +/- 0.05); the corresponding kappa coefficients indicated substantial (kappa = 0.65 +/- 0.12) and moderate (kappa = 0.55 +/- 0.14) agreement for central DBT projections and digital mammograms, respectively. High correlation between PD estimates on digital mammograms and those on central DBT projections suggests the latter could be used until a method for PD estimation based on three-dimensional reconstructed images is introduced. Moreover, clinical PD estimation is possible with reduced radiation dose, as each DBT projection was acquired by using about 22% of the dose for a single mammographic projection.