Purpose: Tomosynthesis is a promising modality for breast imaging. The appearance of the tomosynthesis reconstructed image is greatly affected by the choice of acquisition and reconstruction parameters. The purpose of this study was to investigate the limitations of tomosynthesis breast imaging due to scan parameters and quantum noise. Tomosynthesis image quality was assessed based on performance of a mathematical observer model in a signal-known exactly ͑SKE͒ detection task. Methods: SKE detectability ͑dЈ͒ was estimated using a prewhitening observer model. Structured breast background was simulated using filtered noise. Detectability was estimated for designer nodules ranging from 0.05 to 0.8 cm in diameter. Tomosynthesis slices were reconstructed using iterative maximum-likelihood expectation-maximization. The tomosynthesis scan angle was varied between 15°and 60°, the number of views between 11 and 41 and the total number of x-ray quanta was ϱ, 6ϫ 10 5 , and 6 ϫ 10 4 . Detectability in tomosynthesis was compared to that in a single projection. Results: For constant angular sampling distance, increasing the angular scan range increased detectability for all signal sizes. Large-scale signals were little affected by quantum noise or angular sampling. For small-scale signals, quantum noise and insufficient angular sampling degraded detectability. At high quantum noise levels, angular step size of 3°or below was sufficient to avoid image degradation. At lower quantum noise levels, increased angular sampling always resulted in increased detectability. The ratio of detectability in the tomosynthesis slice to that in a single projection exhibited a peak that shifted to larger signal sizes when the angular range increased. For a given angular range, the peak shifted toward smaller signals when the number of views was increased. The ratio was greater than unity for all conditions evaluated. Conclusion: The effect of acquisition parameters on lesion detectability depends on signal size. Tomosynthesis scan angle had an effect on detectability for all signals sizes, while quantum noise and angular sampling only affected the detectability small-scale signals.

Purpose:The authors develop a practical, iterative algorithm for image-reconstruction in undersampled tomographic systems, such as digital breast tomosynthesis ͑DBT͒. Methods: The algorithm controls image regularity by minimizing the image total p variation ͑TpV͒, a function that reduces to the total variation when p = 1.0 or the image roughness when p = 2.0. Constraints on the image, such as image positivity and estimated projection-data tolerance, are enforced by projection onto convex sets. The fact that the tomographic system is undersampled translates to the mathematical property that many widely varied resultant volumes may correspond to a given data tolerance. Thus the application of image regularity serves two purposes: ͑1͒ Reduction in the number of resultant volumes out of those allowed by fixing the data tolerance, finding the minimum image TpV for fixed data tolerance, and ͑2͒ traditional regularization, sacrificing data fidelity for higher image regularity. The present algorithm allows for this dual role of image regularity in undersampled tomography. Results:The proposed image-reconstruction algorithm is applied to three clinical DBT data sets. The DBT cases include one with microcalcifications and two with masses. Conclusions: Results indicate that there may be a substantial advantage in using the present imagereconstruction algorithm for microcalcification imaging.

Artifacts are frequently encountered at clinical US, and while some are unwanted, others may reveal valuable information related to the structure and composition of the underlying tissue. They are essential in making ultrasonography (US) a clinically useful imaging modality but also can lead to errors in image interpretation and can obscure diagnoses. Many of these artifacts can be understood as deviations from the assumptions made in generating the image. Therefore, understanding the physical basis of US image formation is critical to understanding US artifacts and thus proper image interpretation. This review is limited to gray-scale artifacts and is organized into discussions of beam- and resolution-related, location-related (ie, path and speed), and attenuation-related artifacts. Specifically, artifacts discussed include those related to physical mechanisms of spatial resolution, speckle, secondary lobes, reflection and reverberation, refraction, speed of sound, and attenuation. The underlying physical mechanisms and appearances are discussed, followed by real-world strategies to mitigate or accentuate these artifacts, depending on the clinical application. Relatively new US modes, such as spatial compounding, tissue harmonic imaging, and speckle reduction imaging, are now often standard in many imaging protocols; the effects of these modes on US artifacts are discussed. The ability of a radiologist to understand the fundamental physics of ultrasound, recognize common US artifacts, and provide recommendations for altering the imaging technique is essential for proper image interpretation, troubleshooting, and utilization of the full potential of this modality. RSNA, 2017.

Burgess et al. ͓Med. Phys. 28, 419-437 ͑2001͔͒ showed that the power spectrum of mammographic breast background follows a power law and that lesion detectability is affected by the power-law exponent ␤ which measures the amount of structure in the background. Following the study of Burgess et al., the authors measured and compared the power-law exponent of mammographic backgrounds in tomosynthesis projections and reconstructed slices to investigate the effect of tomosynthesis imaging on background structure. Our data set consisted of 55 patient cases. For each case, regions of interest ͑ROIs͒ were extracted from both projection images and reconstructed slices. The periodogram of each ROI was computed by taking the squared modulus of the Fourier transform of the ROI. The power-law exponent was determined for each periodogram and averaged across all ROIs extracted from all projections or reconstructed slices for each patient data set. For the projections, the mean ␤ averaged across the 55 cases was 3.06 ͑standard deviation of 0.21͒, while it was 2.87 ͑0.24͒ for the corresponding reconstructions. The difference in ␤ for a given patient between the projection ROIs and the reconstructed ROIs averaged across the 55 cases was 0.194, which was statistically significant ͑p Ͻ 0.001͒. The 95% CI for the difference between the mean value of ␤ for the projections and reconstructions was ͓0.170, 0.218͔. The results are consistent with the observation that the amount of breast structure in the tomosynthesis slice is reduced compared to projection mammography and that this may lead to improved lesion detectability.

Digital breast tomosynthesis (DBT) has recently emerged as a new and promising three‐dimensional modality in breast imaging. In DBT, the breast volume is reconstructed from 11 projection images, taken at source angles equally spaced over an arc of 50degrees. Reconstruction algorithms for this modality are not fully optimized yet. Because computerized lesion detection in the reconstructed breast volume will be affected by the reconstruction technique, we are developing a novel mass detection algorithm that operates instead on the set of raw projection images. Mass detection is done in three stages. First, lesion candidates are obtained for each projection image separately, using a mass detection algorithm that was initially developed for screen‐film mammography. Second, the locations of a lesion candidate are backprojected into the breast volume. In this feature volume, voxel intensities are a combined measure of detection frequency (e.g., the number of projections in which a given lesion candidate was detected), and a measure of the angular range over which a given lesion was detected. Third, features are extracted after reprojecting the three‐dimensional (3‐D) locations of lesion candidates into projection images. Features are combined using linear discriminant analysis. The database used to test the algorithm consisted of 21 mass cases (13 malignant, 8 benign) and 15 cases without mass lesions. Based on this database, the algorithm yielded a sensitivity of 90% at 1.5 false positives per breast volume. Algorithm performance is positively biased because this dataset was used for development, training, and testing, and because the number of algorithm parameters was approximately the same as the number of patient cases. Our results indicate that computerized mass detection in the sequence of projection images for DBT may be effective despite the higher noise level in those images.

One of the challenges for iterative image reconstruction (IIR) is that such algorithms solve an imaging model implicitly, requiring a complete representation of the scanned subject within the viewing domain of the scanner. This requirement can place a prohibitively high computational burden for IIR applied to x-ray computed tomography (CT), especially when high-resolution tomographic volumes are required. In this work, we aim to develop an IIR algorithm for direct region-of-interest (ROI) image reconstruction. The proposed class of IIR algorithms is based on an optimization problem that incorporates a data fidelity term, which compares a derivative of the estimated data with the available projection data. In order to characterize this optimization problem, we apply it to computer-simulated two-dimensional fan-beam CT data, using both ideal noiseless data and realistic data containing a level of noise comparable to that of the breast CT application. The proposed method is demonstrated for both complete field-of-view and ROI imaging. To demonstrate the potential utility of the proposed ROI imaging method, it is applied to actual CT scanner data.

Purpose: Digital anthropomorphic breast phantoms have emerged in the past decade because of recent advances in 3D breast x-ray imaging techniques. Computer phantoms in the literature have incorporated power-law noise to represent glandular tissue and branching structures to represent linear components such as ducts. When power-law noise is added to those phantoms in one piece, the simulated fibroglandular tissue is distributed randomly throughout the breast, resulting in dense tissue placement that may not be observed in a real breast. The authors describe a method for enhancing an existing digital anthropomorphic breast phantom by adding binarized power-law noise to a limited area of the breast. Methods: Phantoms with (0.5 mm) 3 voxel size were generated using software developed by Bakic et al. Between 0% and 40% of adipose compartments in each phantom were replaced with binarized power-law noise (b ¼ 3.0) ranging from 0.1 to 0.6 volumetric glandular fraction. The phantoms were compressed to 7.5 cm thickness, then blurred using a 3 Â 3 boxcar kernel and up-sampled to (0.1 mm) 3 voxel size using trilinear interpolation. Following interpolation, the phantoms were adjusted for volumetric glandular fraction using global thresholding. Monoenergetic phantom projections were created, including quantum noise and simulated detector blur. Texture was quantified in the simulated projections using power-spectrum analysis to estimate the power-law exponent b from 25.6 Â 25.6 mm 2 regions of interest. Results: Phantoms were generated with total volumetric glandular fraction ranging from 3% to 24%. Values for b (averaged per projection view) were found to be between 2.67 and 3.73. Thus, the range of textures of the simulated breasts covers the textures observed in clinical images. Conclusions: Using these new techniques, digital anthropomorphic breast phantoms can be generated with a variety of glandular fractions and patterns. b values for this new phantom are comparable with published values for breast tissue in x-ray projection modalities. The combination of conspicuous linear structures and binarized power-law noise added to a limited area of the phantom qualitatively improves its realism.

Digital breast tomosynthesis ͑DBT͒ is a promising modality for breast imaging in which an anisotropic volume image of the breast is obtained. We present an algorithm for computerized detection of microcalcification clusters ͑MCCs͒ for DBT. This algorithm operates on the projection views only. Therefore it does not depend on reconstruction, and is computationally efficient. The algorithm was developed using a database of 30 image sets with microcalcifications, and a control group of 30 image sets without visible findings. The patient data were acquired on the first DBT prototype at Massachusetts General Hospital. Algorithm sensitivity was estimated to be 0.86 at 1.3 false positive clusters, which is below that of current MCC detection algorithms for full-field digital mammography. Because of the small number of patient cases, algorithm parameters were not optimized and one linear classifier was used. An actual limitation of our approach may be that the signal-to-noise ratio in the projection images is too low for microcalcification detection. Furthermore, the database consisted of predominantly small MCC. This may be related to the image quality obtained with this first prototype.