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Dedicated breast CT is an imaging modality that provides true 3D imaging of the breast with many advantages over current conventional breast imaging modalities. The addition of intravascular contrast increases the sensitivity of breast CT substantially. As such, there are immediate potential applications in the clinical workflow. These include using breast CT to replace much of the traditional diagnostic workup when faced with indeterminate breast lesions. Contrast-enhanced breast CT may be appropriate as a supplemental screening tool for women at high risk of breast cancer, similar to breast MRI. In addition, emerging studies are demonstrating the utility of breast CT in neoadjuvant chemotherapy tumor response monitoring as well as planning for surgical treatment options. While short exam times and fully 3D imaging in a noncompressed position are advantages of this modality, limited coverage of chest wall/axilla due to prone positioning and use of ionizing radiation are drawbacks. To date, several studies have reported on the performance characteristics of this promising modality. [ABSTRACT FROM AUTHOR]

Background: Breast computed tomography (CT) is an emerging breast imaging modality, and ongoing developments aim to improve breast CT's ability to detect microcalcifications. To understand the effects of different parameters on microcalcification detectability, a virtual clinical trial study was conducted using hybrid images and convolutional neural network (CNN)‐based model observers. Mathematically generated microcalcifications were embedded into breast CT data sets acquired at our institution, and parameters related to calcification size, calcification contrast, cluster diameter, cluster density, and image display method (i.e., single slices, slice averaging, and maximum‐intensity projections) were evaluated for their influence on microcalcification detectability. Purpose: To investigate the individual effects and the interplay of parameters affecting microcalcification detectability in breast CT. Methods: Spherical microcalcifications of varying diameters (0.04, 0.10, 0.15, 0.20, 0.25, 0.30, 0.35, 0.40 mm) and native intensities were computer simulated to portray the partial volume effects of the imaging system. Calcifications were mathematically embedded into 109 patient breast CT volume data sets as individual calcifications or as clusters of calcifications. Six numbers of calcifications (1, 3, 5, 7, 10, 15) distributed within six cluster diameters (1, 3, 5, 6, 8, 10 mm) were simulated to study the effect of cluster density. To study the role of image display method, 2D regions of interest (ROIs) and 3D volumes of interest (VOIs) were generated using single slice extraction, slice averaging, and maximum‐intensity projection (MIP). 2D and 3D CNNs were trained on the ROIs and VOIs, and receiver operating characteristic (ROC) curve analysis was used to evaluate detection performance. The area under the ROC curve (AUC) was used as the primary performance metric. Results: Detection performance decreased with increasing section thickness, and peak detection performance occurred using the native section thickness (0.2 mm) and MIP display. The MIP display method, despite using a single slice, yielded comparable performance to the native section thickness, which employed 50 slices. Reduction in slices did not sacrifice detection accuracy and provided significant computational advantages over multi‐slice image volumes. Larger cluster diameters resulted in reduced overall detectability, while smaller cluster diameters led to increased detectability. Additionally, we observed that the presence of more calcifications within a cluster improved the overall detectability, while fewer calcifications decreased it. Conclusions: As breast CT is still a relatively new breast imaging modality, there is an ongoing need to identify optimal imaging protocols. This work demonstrated the utility of MIP presentation for displaying image volumes containing microcalcification clusters. It is likely that human observers may also benefit from viewing MIPs compared to individual slices. The results of this investigation begin to elucidate how model observers interact with microcalcification clusters in a 3D volume, and will be useful for future studies investigating a broader set of parameters related to breast CT. [ABSTRACT FROM AUTHOR]

Background: X‐ray breast imaging modalities are commonly employed for breast cancer detection, from screening programs to diagnosis. Thus, dosimetry studies are important for quality control and risk estimation since ionizing radiation is used. Purpose: To perform multiscale dosimetry assessments for different breast imaging modalities and for a variety of breast sizes and compositions. The first part of our study is focused on macroscopic scales (down to millimeters). Methods: Nine anthropomorphic breast phantoms with a voxel resolution of 0.5 mm were computationally generated using the BreastPhantom software, representing three breast sizes with three distinct values of volume glandular fraction (VGF) for each size. Four breast imaging modalities were studied: digital mammography (DM), contrast‐enhanced digital mammography (CEDM), digital breast tomosynthesis (DBT) and dedicated breast computed tomography (BCT). Additionally, the impact of tissue elemental compositions from two databases were compared. Monte Carlo (MC) simulations were performed with the MC‐GPU code to obtain the 3D glandular dose distribution (GDD) for each case considered with the mean glandular dose (MGD) fixed at 4 mGy (to facilitate comparisons). Results: The GDD within the breast is more uniform for CEDM and BCT compared to DM and DBT. For large breasts and high VGF, the ratio between the minimum/maximum glandular dose to MGD is 0.12/4.02 for DM and 0.46/1.77 for BCT; the corresponding results for a small breast and low VGF are 0.35/1.98 (DM) and 0.63/1.42 (BCT). The elemental compositions of skin, adipose and glandular tissue have a considerable impact on the MGD, with variations up to 30% compared to the baseline. The inclusion of tissues other than glandular and adipose within the breast has a minor impact on MGD, with differences below 2%. Variations in the final compressed breast thickness alter the shape of the GDD, with a higher compression resulting in a more uniform GDD. Conclusions: For a constant MGD, the GDD varies with imaging modality and breast compression. Elemental tissue compositions are an important factor for obtaining MGD values, being a source of systematic uncertainties in MC simulations and, consequently, in breast dosimetry. [ABSTRACT FROM AUTHOR]

Dedicated cone-beam breast computed tomography (CBBCT) is an emerging modality and provides fully three-dimensional (3D) images of the uncompressed breast at an isotropic voxel resolution. In an effort to translate this modality to breast cancer screening, advanced image reconstruction methods are being pursued. Since radiographic breast density is an established risk factor for breast cancer and CBBCT provides volumetric data, this study investigates the reproducibility of the volumetric glandular fraction (VGF), defined as the proportion of fibroglandular tissue volume relative to the total breast volume excluding the skin. Four image reconstruction methods were investigated: the analytical Feldkamp–Davis–Kress (FDK), a compressed sensing-based fast, regularized, iterative statistical technique (FRIST), a fully supervised deep learning approach using a multi-scale residual dense network (MS-RDN), and a self-supervised approach based on Noise-to-Noise (N2N) learning. Projection datasets from 106 women who participated in a prior clinical trial were reconstructed using each of these algorithms at a fixed isotropic voxel size of (0.273 mm3). Each reconstructed breast volume was segmented into skin, adipose, and fibroglandular tissues, and the VGF was computed. The VGF did not differ among the four reconstruction methods (p = 0.167), and none of the three advanced image reconstruction algorithms differed from the standard FDK reconstruction (p > 0.862). Advanced reconstruction algorithms developed for low-dose CBBCT reproduce the VGF to provide quantitative breast density, which can be used for risk estimation. [ABSTRACT FROM AUTHOR]

Objectives: Breast density is considered an independent risk factor for the development of breast cancer. This study aimed to quantitatively assess the percent breast density (PBD) and the mammary glands volume (MGV) according to the patient's age and breast quadrant. We propose a regression model to estimate PBD and MGV as a function of the patient's age. Methods: The breast composition in 1027 spiral breast CT (BCT) datasets without soft tissue masses, calcifications, or implants from 517 women (57 ± 8 years) were segmented. The breast tissue volume (BTV), MGV, and PBD of the breasts were measured in the entire breast and each of the four quadrants. The three breast composition features were analyzed in the seven age groups, from 40 to 74 years in 5-year intervals. A logarithmic model was fitted to the BTV, and a multiplicative inverse model to the MGV and PBD as a function of age was established using a least-squares method. Results: The BTV increased from 545 ± 345 to 676 ± 412 cm3, and the MGV and PBD decreased from 111 ± 164 to 57 ± 43 cm3 and from 21 ± 21 to 11 ± 9%, respectively, from the youngest to the oldest group (p < 0.05). The average PBD over all ages were 14 ± 13%. The regression models could predict the BTV, MGV, and PBD based on the patient's age with residual standard errors of 386 cm3, 67 cm3, and 13%, respectively. The reduction in MGV and PBD in each quadrant followed the ones in the entire breast. Conclusions: The PBD and MGV computed from BCT examinations provide important information for breast cancer risk assessment in women. The study quantified the breast mammary gland reduction and density decrease over the entire breast. It established mathematical models to estimate the breast composition features—BTV, MGV, and PBD, as a function of the patient's age. [ABSTRACT FROM AUTHOR]

Background: Radiopaque breast markers cause artifacts in dedicated spiral breast-computed tomography (SBCT). This study investigates the extent of artifacts in different marker types and the feasibility of reducing artifacts through a metal artifact reduction (MAR) algorithm. Methods: The pilot study included 18 women who underwent contrast-enhanced SBCT. In total, 20 markers of 4 different types were analyzed for artifacts. The extent of artifacts with and without MAR was measured via the consensus of two readers. Image noise was quantitatively evaluated, and the effect of MAR on the detectability of breast lesions was evaluated on a 3-point Likert scale. Results: Breast markers caused significant artifacts that impaired image quality and the detectability of lesions. MAR decreased artifact size in all analyzed cases, even in cases with multiple markers in a single slice. The median length of in-plain artifacts significantly decreased from 31 mm (range 11–51 mm) in uncorrected to 2 mm (range 1–5 mm) in corrected images (p ≤ 0.05). Artifact size was dependent on marker size. Image noise in slices affected by artifacts was significantly lower in corrected (13.6 ± 2.2 HU) than in uncorrected images (19.2 ± 6.8 HU, p ≤ 0.05). MAR improved the detectability of lesions affected by artifacts in 5 out of 11 cases. Conclusion: MAR is feasible in SBCT and improves the image quality and detectability of lesions. [ABSTRACT FROM AUTHOR]

Background: In breast CT, scattered photons form a large portion of the acquired signal, adversely impacting image quality throughout the frequency response of the imaging system. Prior studies provided evidence for a new image acquisition design, dubbed Narrow Beam Breast CT (NB‐bCT), in preventing scatter acquisition. Purpose: Here, we report the design, implementation, and initial characterization of the first NB‐bCT prototype. Methods: The imaging system's apparatus is composed of two primary assemblies: a dynamic Fluence Modulator (collimator) and a photon‐counting line detector. The design of the assemblies enables them to operate in lockstep during image acquisition, converting sourced x‐rays into a moving narrow beam. During a projection, this narrow beam sweeps the entire fan angle coverage of the imaging system. The assemblies are each comprised of a metal housing, a sensory system, and a robotic system. A controller unit handles their relative movements. To study the impact of fluence modulation on the signal received in the detector, three physical breast phantoms, representative of small, average, and large size breasts, were developed and imaged, and acquired projections analyzed. The scatter acquisition in each projection as a function of breast phantom size was investigated. The imaging system's spatial resolution at the center and periphery of the field of view was measured. Results: Minimal acquisition of scattered rays occurs during image acquisition with NB‐bCT; results in minimal scatter to primary ratios in small, average, and large breast phantoms imaged were 0.05, 0.07, and 0.9, respectively. System spatial resolution of 5.2 lp/mm at 10% max MTF and 2.9 lp/mm at 50% max MTF at the center of the field of view was achieved, with minimal loss with the shift toward the corner (5.0 lp/mm at 10% max MTF and 2.5 lp/mm at 50% max MTF). Conclusion: The disclosed development, implementation, and characterization of a physical NB‐bCT prototype system demonstrates a new method of CT‐based image acquisition that yields high spatial resolution while minimizing scatter‐components in acquired projections. This methodology holds promise for high‐resolution CT‐imaging applications in which reduction of scatter contamination is desirable. [ABSTRACT FROM AUTHOR]

Purpose: Breast cancer is the most common malignancy in women. Unfortunately, current breast imaging techniques all suffer from certain limitations: they are either not fully three dimensional, have an insufficient resolution or low soft‐tissue contrast. Grating interferometry breast computed tomography (GI‐BCT) is a promising X‐ray phase contrast modality that could overcome these limitations by offering high soft‐tissue contrast and excellent three‐dimensional resolution. To enable the transition of this technology to clinical practice, dedicated data‐processing algorithms must be developed in order to effectively retrieve the signals of interest from the measured raw data. Methods: This article proposes a novel denoising algorithm that can cope with the high‐noise amplitudes and heteroscedasticity which arise in GI‐BCT when operated in a low‐dose regime to effectively regularize the ill‐conditioned GI‐BCT inverse problem. We present a data‐driven algorithm called INSIDEnet, which combines different ideas such as multiscale image processing, transform‐domain filtering, transform learning, and explicit orthogonality to build an Interpretable NonexpanSIve Data‐Efficient network (INSIDEnet). Results: We apply the method to simulated breast phantom datasets and to real data acquired on a GI‐BCT prototype and show that the proposed algorithm outperforms traditional state‐of‐the‐art filters and is competitive with deep neural networks. The strong inductive bias given by the proposed model's architecture allows to reliably train the algorithm with very limited data while providing high model interpretability, thus offering a great advantage over classical convolutional neural networks (CNNs). Conclusions: The proposed INSIDEnet is highly data‐efficient, interpretable, and outperforms state‐of‐the‐art CNNs when trained on very limited training data. We expect the proposed method to become an important tool as part of a dedicated plug‐and‐play GI‐BCT reconstruction framework, needed to translate this promising technology to the clinics. [ABSTRACT FROM AUTHOR]

Purpose: The SYRMA‐3D collaboration is setting up a breast computed tomography (bCT) clinical program at the Elettra synchrotron radiation facility in Trieste, Italy. Unlike the few dedicated scanners available at hospitals, synchrotron radiation bCT requires the patient's rotation, which in turn implies a long scan duration (from tens of seconds to few minutes). At the same time, it allows the achievement of high spatial resolution. These features make synchrotron radiation bCT prone to motion artifacts. This article aims at assessing and compensating for motion artifacts through an optical tracking approach. Methods: In this study, patients' movements due to breathing have been first assessed on seven volunteers and then simulated during the CT scans of a breast phantom and a surgical specimen, by adding a periodic oscillatory motion (constant speed, 1 mm amplitude, 12 cycles/minute). CT scans were carried out at 28 keV with a mean glandular dose of 5 mGy. Motion artifacts were evaluated and a correction algorithm based on the optical tracking of fiducial marks was introduced. A quantitative analysis based on the structural similarity (SSIM) index and the normalized mean square error (nMSE) was performed on the reconstructed CT images. Results: CT images reconstructed through the optical tracking procedure were found to be as good as the motionless reference image. Moreover, the analysis of SSIM and nMSE demonstrated that an uncorrected motion of the order of the system's point spread function (around 0.1 mm in the present case) can be tolerated. Conclusions: Results suggest that a motion correction procedure based on an optical tracking system would be beneficial in synchrotron radiation bCT. [ABSTRACT FROM AUTHOR]

Purpose: A clinical‐prototype, dedicated, cone‐beam breast computed tomography (CBBCT) system with offset detector is undergoing clinical evaluation at our institution. This study is to estimate the normalized glandular dose coefficients (DgNCT) that provide air kerma‐to‐mean glandular dose conversion factors using Monte Carlo simulations. Materials and methods: The clinical prototype CBBCT system uses 49 kV x‐ray spectrum with 1.39 mm 1st half‐value layer thickness. Monte Carlo simulations (GATE, version 8) were performed with semi‐ellipsoidal, homogeneous breasts of various fibroglandular weight fractions (fg=0.01,0.15,0.5,1), chest wall diameters (d=8,10,14,18,20 cm), and chest wall to nipple length (l=0.75d), aligned with the axis of rotation (AOR) located at 65 cm from the focal spot to determine the DgNCT. Three geometries were considered – 40×30‐cm detector with no offset that served as reference and corresponds to a clinical CBBCT system, 30×30‐cm detector with 5 cm offset, and a 30×30‐cm detector with 10 cm offset. Results: For 5 cm lateral offset, the DgNCT ranged 0.177‐0.574 mGy/mGy and reduction in DgNCT with respect to reference geometry was observed only for 18 cm (6.4%±0.23%) and 20 cm (9.6%±0.22%) diameter breasts. For the 10 cm lateral offset, the DgNCT ranged 0.221‐0.581 mGy/mGy and reduction in DgNCT was observed for all breast diameters. The reduction in DgNCT was 1.4%±0.48%, 7.1%±0.13%, 17.5%±0.19%, 25.1%±0.15%, and 27.7%±0.08% for 8, 10, 14, 18, and 20 cm diameter breasts, respectively. For a given breast diameter, the reduction in DgNCT with offset‐detector geometries was not dependent on fg. Numerical fits of DgNCTd,l,fg were generated for each geometry. Conclusion: The DgNCT and the numerical fit, DgNCTd,l,fg would be of benefit for current CBBCT systems using the reference geometry and for future generations using offset‐detector geometry. There exists a potential for radiation dose reduction with offset‐detector geometry, provided the same technique factors as the reference geometry are used, and the image quality is clinically acceptable. [ABSTRACT FROM AUTHOR]

Purpose: To develop and evaluate the diagnostic performance of an algorithm for multi‐marker radiomic‐based classification of breast masses in dedicated breast computed tomography (bCT) images. Methods: Over 1000 radiomic descriptors aimed at quantifying mass and border heterogeneity, morphology, and margin sharpness were developed and implemented. These included well‐established texture and shape feature descriptors, which were supplemented with additional approaches for contour irregularity quantification, spicule and lobe detection, characterization of degree of infiltration, and differences in peritumoral compartments. All descriptors were extracted from a training set of 202 bCT masses (133 benign and 69 malignant), and their individual diagnostic performance was investigated in terms of area under the receiver operating characteristics (ROC) curve (AUC) of single‐feature‐based linear discriminant analysis (LDA) classifiers. Subsequently, the most relevant descriptors were selected through a multiple‐step feature selection process (including stability analysis, statistical significance, evaluation of feature interaction, and dimensionality reduction), and used to develop a final LDA radiomic model for classification of benign and malignant masses, which was then tested on an independent test set of 82 cases (45 benign and 37 malignant). Results: The majority of the individual radiomic descriptors showed, on the training set, an AUC value deriving from a linear decision boundary higher than 0.65, with the lower limit of the associated 95% confidence interval (C.I.) not overlapping with random chance (AUC = 0.5). The final LDA radiomic model resulted in a test set AUC of 0.90 (95% C.I. 0.80–0.96). Conclusions: The proposed multi‐marker radiomic approach achieved high diagnostic accuracy in bCT mass classification, using a radiomic signature based on different feature types. While future studies with larger datasets are needed to further validate these results, quantitative radiomics applied to bCT shows potential to improve the breast cancer diagnosis pipeline. [ABSTRACT FROM AUTHOR]

Purpose: Clinical use of dedicated breast computed tomography (bCT) requires relatively short scan times necessitating systems with high frame rates. This in turn impacts the x‐ray tube operating range. We characterize the effects of tube voltage, beam filtration, dose, and object size on contrast and noise properties related to soft tissue and iodine contrast agents as a way to optimize imaging protocols for soft tissue and iodine contrast at high frame rates. Methods: This study design uses the signal‐difference‐to‐noise ratio (SDNR), noise‐equivalent quanta (NEQ), and detectability (d´) as measures of imaging performance for a prototype breast CT scanner that utilizes a pulsed x‐ray tube (with a 4 ms pulse width) at 43.5 fps acquisition rate. We assess a range of kV, filtration, breast phantom size, and mean glandular dose (MGD). Performance measures are estimated from images of adipose‐equivalent breast phantoms machined to have a representative size and shape of small, medium, and large breasts. Water (glandular tissue equivalent) and iodine contrast (5 mg/ml) were used to fill two cylindrical wells in the phantoms. Results: Air kerma levels required for obtaining an MGD of 6 mGy ranged from 7.1 to 9.1 mGy and are reported across all kV, filtration, and breast phantom sizes. However, at 50 kV, the thick filters (0.3 mm of Cu or Gd) exceeded the maximum available mA of the x‐ray generator, and hence, these conditions were excluded from subsequent analysis. There was a strong positive association between measurements of SDNR and d' (R2 > 0.97) within the range of parameters investigated in this work. A significant decrease in soft tissue SDNR was observed for increasing phantom size and increasing kV with a maximum SDNR at 50 kV with 0.2 mm Cu or 0.2 mm Gd filtration. For iodine contrast SDNR, a significant decrease was observed with increasing phantom size, but a decrease in SDNR for increasing kV was only observed for 70 kV (50 and 60 kV were not significantly different). Thicker Gd filtration (0.3 mm Gd) resulted in a significant increase in iodine SDNR and decrease in soft tissue SDNR but requires significantly more tube current to deliver the same MGD. Conclusions: The choice of 60 kV with 0.2 mm Gd filtration provides a good trade‐off for maximizing both soft tissue and iodine contrast. This scanning technique takes advantage of the ~50 keV Gd k‐edge to produce contrast and can be achieved within operating range of the x‐ray generator used in this work. Imaging at 60 kV allows for a greater range in dose delivered to the large breast sizes when uniform image quality is desired across all breast sizes. While imaging performance metrics (i.e., detectability index and SDNR) were shown to be strongly correlated, the methodologies presented in this work for the estimation of NEQ (and subsequently d') provides a meaningful description of the spatial resolution and noise characteristics of this prototype bCT system across a range of beam quality, dose, and object sizes. [ABSTRACT FROM AUTHOR]

Purpose: Contrast‐enhanced imaging of the breast is frequently used in breast MRI and has recently become more common in mammography. The purpose of this study was to make single‐scan contrast‐enhanced imaging feasible for photon‐counting breast CT (pcBCT) and to assess the spectral performance of a pcBCT scanner by evaluating iodine maps and virtual non‐contrast (VNC) images. Methods: We optimized the settings of a pcBCT to maximize the signal‐to‐noise ratio between iodinated contrast agent and breast tissue. Therefore, an electronic energy threshold dividing the x‐ray spectrum used into two energy bins was swept from 23.17 keV to 50.65 keV. Validation measurements were performed by placing syringes with contrast agent (2.5 mg/ml to 40 mg/ml) in phantoms with 7.5 cm and 12 cm in diameter. Images were acquired at different tube currents and reconstructed with 300 μm isotropic voxel size. Iodine maps and VNC images were generated using image‐based material decomposition. Iodine concentrations and CT values were measured for each syringe and compared to the known concentrations and reference CT values. Results: Maximal signal‐to‐noise ratios were found at a threshold position of 32.59 keV. Accurate iodine quantification (average root mean square error of 0.56 mg/ml) was possible down to a concentration of 2.5 mg/ml for all tube currents investigated. The enhancement has been sufficiently removed in the VNC images, so they can be interpreted as unenhanced CT images. Only minor changes of CT values compared to a conventional CT scan were observed. Noise was increased by the decomposition by a factor of 2.62 and 4.87 (7.5 cm and 12 cm phantoms) but did not compromise the accuracy of the iodine quantification. Conclusions: Accurate iodine quantification and generation of VNC images can be achieved using contrast‐enhanced pcBCT from a single CT scan in the absence of temporal or spatial misalignment. Using iodine maps and VNC images, pcBCT has the potential to reduce dose, shorten examination and reading time, and to increase cancer detection rates. [ABSTRACT FROM AUTHOR]

BACKGROUND: High-resolution, low-noise detectors with minimal dead-space at chest-wall could improve posterior coverage and microcalcification visibility in the dedicated cone-beam breast CT (CBBCT). However, the smaller field-of-view necessitates laterally-shifted detector geometry to enable optimizing the air-gap for x-ray scatter rejection. OBJECTIVE: To evaluate laterally-shifted detector geometry for CBBCT with clinical projection datasets that provide for anatomical structures and lesions. METHODS: CBBCT projection datasets (n = 17 breasts) acquired with a 40×30 cm detector (1024×768-pixels, 0.388-mm pixels) were truncated along the fan-angle to emulate 20.3×30 cm, 22.2×30 cm and 24.1×30 cm detector formats and correspond to 20, 120, 220 pixels overlap in conjugate views, respectively. Feldkamp-Davis-Kress (FDK) algorithm with 3 different weighting schemes were used for reconstruction. Visual analysis for artifacts and quantitative analysis of root-mean-squared-error (RMSE), absolute difference between truncated and 40×30 cm reconstructions (Diff), and its power spectrum (PSDiff) were performed. RESULTS: Artifacts were observed for 20.3×30 cm, but not for other formats. The 24.1×30 cm provided the best quantitative results with RMSE and Diff (both in units of μ, cm-1) of 4.39×10-3±1.98×10-3 and 4.95×10-4±1.34×10-4, respectively. The PSDiff (>0.3 cycles/mm) was in the order of 10-14μ2mm3 and was spatial-frequency independent. CONCLUSIONS: Laterally-shifted detector CBBCT with at least 220 pixels overlap in conjugate views (24.1×30 cm detector format) provides quantitatively accurate and artifact-free image reconstruction. [ABSTRACT FROM AUTHOR]

Breast computed tomography (BCT) is an emerging application of X‐ray tomography in radiological practice. A few clinical prototypes are under evaluation in hospitals and new systems are under development aiming at improving spatial and contrast resolution and reducing delivered dose. At the same time, synchrotron‐radiation phase‐contrast mammography has been demonstrated to offer substantial advantages when compared with conventional mammography. At Elettra, the Italian synchrotron radiation facility, a clinical program of phase‐contrast BCT based on the free‐space propagation approach is under development. In this paper, full‐volume breast samples imaged with a beam energy of 32 keV delivering a mean glandular dose of 5 mGy are presented. The whole acquisition setup mimics a clinical study in order to evaluate its feasibility in terms of acquisition time and image quality. Acquisitions are performed using a high‐resolution CdTe photon‐counting detector and the projection data are processed via a phase‐retrieval algorithm. Tomographic reconstructions are compared with conventional mammographic images acquired prior to surgery and with histologic examinations. Results indicate that BCT with monochromatic beam and free‐space propagation phase‐contrast imaging provide relevant three‐dimensional insights of breast morphology at clinically acceptable doses and scan times. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this work was to generate uncompressed heterogeneous breast phantom models using size‐dependent fibroglandular distributions derived from a large cohort of breast CT (bCT) datasets, and to compare differences in normalized glandular dose coefficients for bCT "DgNCT" when the realistic heterogeneous model is considered relative to the simple, homogeneous model used in the past. Methods: A cohort of 274 segmented bCT datasets were used to quantify the fibroglandular tissue distribution within the breast parenchyma. Each dataset was interpolated to an isotropic voxel size of 0.25 mm and the breast center‐of‐mass was aligned for all coronal slices. Each aligned dataset was converted into two binarized volumes representing voxels containing only glandular tissue "G(x,y,z)" and voxels containing glandular or adipose tissue "AG(x,y,z)". The datasets were classified by volume in accordance with previously reported size‐dependent, breast‐shaped phantoms. Within the five groups — each containing on average 55 datasets, all G(x,y,z) and AG(x,y,z) volumes were summed separately representing the cumulative distribution of glandular tissue or breast parenchyma (glandular + adipose), respectively. G(x,y,z) was divided by AG(x,y,z) on a voxel‐by‐voxel basis resulting in a glandular fraction "GF(x,y,z)" distribution for each phantom size. The GF(x,y,z) distributions were used to construct heterogeneous mathematical phantoms for the small, median, and large breast sizes with a 1.5 mm skin thickness — based on previously reported measurements from bCT, and a 5 mm skin thickness for comparison with outdated assumptions of skin thickness. A subset of 15 bCT datasets from the cohort (five for each breast size) were used to construct voxelized patient models for validation of the heterogeneous phantom models. Monte Carlo techniques were used to estimate monoenergetic DgN(E)CT values for photon energies from 9 to 70 keV (in 1 keV intervals) using the mathematical phantoms composed of either heterogeneous or homogeneous breast parenchyma. Polyenergetic (pDgNCT) coefficients were determined by weighting the DgN(E)CT values by x‐ray spectra tuned to the beam characteristic of breast CT. Dose coefficients were compared between the two breast compositions for each volume class, breast density, and skin thickness. Results: For photon energies ≲45 keV, the homogeneous model overestimates DgN(E) values relative to the realistic heterogeneous model sorted into five volume classes. The 5 mm skin thickness underestimates DgN(E) values relative to the realistic 1.5 mm thickness for lower energies and the differences diminish up to 70 keV. Averaged across all phantom sizes the homogeneous model overestimates pDgNCT by 5.7% and 23.3% for the 60 kV W/Cu and 49 kV W/Al spectra, respectively. The heterogeneous model was also found to be in agreement with the voxelized bCT patient models with pDgNCT differences less than 2.3% and 5.2% for the 60 kV W/Cu and 49 kV W/Al spectra, respectively, across all phantom sizes. Conclusion: Anatomically accurate heterogeneous phantom models were developed using bCT image‐derived fibroglandular tissue distributions. These new models improve the accuracy of bCT dosimetry, and in conjunction with previous models for mammography, may help in providing a more universally accepted breast dosimetry model. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to develop a realistic patient‐based 4D digital breast phantom including time‐varying contrast enhancement for simulation of dedicated breast CT perfusion imaging. Methods: A 3D static phantom is first created by segmenting a breast CT image from a healthy patient into skin, fibroglandular tissue, adipose tissue, and vasculature. For the creation of abnormal cases, a breast lesion model was developed and can be added to the phantom. After defining the necessary perfusion parameters for each tissue (e.g., arterial input function for vasculature, blood volume and blood flow for the other normal tissues) based on contrast‐enhanced dynamic breast MRI data, the corresponding time‐enhancement curves are computed for each voxel in the phantom, according to tissue type. These curves are calculated by convolution between the arterial input function and a shifted exponential function. This exponential depends on the perfusion parameters associated with each tissue voxel, and, to incorporate normal biological variability, a uniform random distribution is used to vary the perfusion parameters on a voxel‐basis. Finally, a 4D array is produced by sampling the continuous time‐enhancement curves at the desired sampling rate. Beside modeling different enhancement dynamics according to the given input perfusion parameters, the phantom also includes the possibility to realistically simulate different spatial enhancement patterns for the breast parenchyma, taking into account the arterial sources supplying the breast. Finally, different patterns of contrast medium uptake can also be simulated for the tumor models (homogeneous and rim enhancement). Results: As an example, a typical 4D phantom has dimensions of 426 × 421 × 260 × 559 (x, y, z, t), with a voxel size of 273 μm and a sampling time of 1 s. The characteristics of the tumor model can be modified at will to evaluate perfusion in different types of breast lesions. Results show the expected enhancement of tissues, consistent with the given input parameters. Moreover, the tumor models evaluated in this work show different enhancement dynamics according to the tumor type (defined by different input perfusion parameters), and also present a higher enhancement compared to the other healthy tissues, as expected. Conclusions: The proposed digital phantom can model the breast tissue perfusion during 4D breast CT image acquisition, displaying the different enhancement dynamics that could be found in a real patient breast. This phantom can be used during the development of dynamic contrast‐enhanced dedicated breast CT imaging, for optimization of image acquisition, image reconstruction, and image analysis. This modality could provide functional information of the breast, resulting in detection, diagnosis, and treatment improvements of breast cancer with breast CT. [ABSTRACT FROM AUTHOR]

Purpose: With the advent of three‐dimensional (3D) breast imaging modalities such as digital breast tomosynthesis (DBT) and dedicated breast CT (bCT), research into new anthropomorphic breast phantoms has accelerated. These breast phantoms are important for the optimization of new breast imaging systems, assessing new regulatory submissions to prove safety and effectiveness, and for developing new approaches to acceptance and constancy testing of 3D breast imaging systems. This paper provides a review of current research investigating both digital and physical breast phantom development for use in x‐ray based imaging. Methods: Two approaches for designing anthropomorphic, digital breast phantoms are discussed, procedural model‐based phantom generation, where breast features are expressed using mathematical models, and patient‐based generation, where breast structures from tissue specimens or patient‐based breast MR or CT volumes are segmented. Following this discussion, a review of physical anthropomorphic phantoms is given, with emphasis on the advantages and disadvantages present with each approach. Conclusions: This paper provides a summary of the state‐of‐the‐art in anthropomorphic breast phantom development for x‐ray breast imaging. The primary advantage of model‐based digital phantoms is that an unlimited number of phantoms with varying size, shape, and density can be generated. Current research on model‐based breast phantoms is producing more and more realistic breast models; however, they probably are not yet able to pass the so‐called "fool the radiologist" visualization test. Empirical patient‐based breast phantoms are typically based on clinical breast CT data and look more realistic. However, clinical breast CT images have limited spatial resolution and thus do not always portray the finer details in the breast. A number of innovative solutions have been proposed for fabricating physical anthropomorphic breast phantoms based on digital phantom models; however, a number of challenges remain, including realistic modeling of x‐ray attenuation properties and accurately representing high‐frequency structures within breast. [ABSTRACT FROM AUTHOR]

Background: The SYRMA-3D collaboration is setting up the first clinical trial of phase-contrast breast CT with synchrotron radiation at the Elettra synchrotron facility in Trieste, Italy. In this communication, a quality control protocol for breast CT is proposed, and a first test of image quality measurements is performed by means of a custom-made radiographic phantom. Materials and methods: A set of projections is acquired and used to perform a CT reconstruction of two selected portions of the phantom. Such portions contain a uniform layer of water and a set of radiographic inserts, respectively. Together, they allow to perform several image quality measurements, namely CT number linearity, reconstruction accuracy, uniformity, noise, and low contrast resolution. All measurements are repeated at different beam energies in the range of interest, and at two different dose values. Results: Measurements show a good linearity in the soft tissue range, paired to a high accuracy of the CT number reconstruction. Uniformity and noise measurements show that reconstruction inhomogeneities are bound to a few percent of the average pixel values. However, low contrast detectability is limited to the higher portion of the explored energy range. Conclusions: The results of the measurements are satisfactory in terms of their quality, feasibility and reproducibility. With minimal modifications, the phantom is promising to allow a set of image quality measurements to be used in the upcoming clinical trial. [ABSTRACT FROM AUTHOR]

Purpose: Many computer aided diagnosis (CADx) tools for breast cancer begin by fully or semiautomatically segmenting a given breast lesion and then classifying the lesion's likelihood of malignancy using quantitative features extracted from the image. It is often assumed that better segmentation will result in better classification. However, this has not been thoroughly evaluated. The purpose of this study is to evaluate the relationship between computer segmentation performance and computer classification performance. Method: We used 85 breast lesions (32 benign, 56 malignant) from breast computed tomography (CT) cases of 82 women. We prepared one smooth and one sharp iterative image reconstructions (IIR) and a clinical reconstruction for each of the 82 breast CT scans. For each reconstruction, we created 15 segmentation outcomes by applying 15 different segmentation algorithms. Specifically, we simulated 15 segmentation algorithms by changing parameters in a single segmentation algorithm. We then created 15 classification outcomes by conducting quantitative image feature analysis on the segmented image results. Using a 10‐fold cross‐validation, we evaluated the relationship between segmentation and classification performances. Result: We found a low positive correlation between segmentation and classification performances for the smooth IIR (median Pearson's rho = 0.18), while a moderate positive correlation (median Pearson's rho = 0.4−0.43) was found between the two performances for the sharp IIR and clinical reconstruction. However, we found large variations in both segmentation and classification performances for the sharp IIR and clinical reconstruction. There were cases where segmentation algorithms resulted in similar segmentation performances, but the corresponding classification performances were different. These results indicate that an improvement in segmentation performance does not guarantee an improvement in the corresponding classification performance. Conclusion: Computer segmentation is an indirect variable affecting the computer classification. As better segmentation does not guarantee better classification, we should report both segmentation and classification performances when comparing segmentation algorithms. [ABSTRACT FROM AUTHOR]

Hounsfield Units (HU) are used clinically in differentiating tissue types in a reconstructed CT image, and therefore the HU accuracy of a system is important, especially when using multiple sources, novel detector and non-traditional trajectories. Dedicated clinical breast CT (BCT) systems therefore should be similarly evaluated. In this study, uniform cylindrical phantoms filled with various uniform density fluids were used to characterize differences in HU values between simple circular and complex 3D (saddle) orbits. Based on ACR recommendations, the HU accuracy, center-to-edge variability within a slice, and overall variability within the reconstructed volume were characterized for simple and complex acquisitions possible on a single versatile BCT system. Results illustrate the statistically significantly better performance of the saddle orbit, especially close to the chest and nipple regions of what would clinically be a pendant breast volume. The incomplete cone beam acquisition of a simple circular orbit causes shading artifacts near the nipple, due to insufficient sampling, rendering a major portion of the scanned phantom unusable, whereas the saddle orbit performs exceptionally well and provides a tighter distribution of HU values throughout the reconstructed volumes. This study further establishes the advantages of using 3D acquisition trajectories for breast CT as well as other applications by demonstrating the robustness of HU values throughout large reconstructed volumes. [ABSTRACT FROM AUTHOR]

Conventional mammographic dosimetry has been developed over the past 40 years. Prior to the availability of high-resolution three-dimensional breast images, certain assumptions about breast anatomy were required. These assumptions were based on the information evident on two-dimensional mammograms; they included assumptions of thick skin, a uniform mixture of glandular and adipose tissue, and a median breast density of 50%. Recently, the availability of high-resolution breast CT studies has provided more accurate data about breast anatomy, and this, in turn, has provided the opportunity to update mammographic dosimetry. Based on hundreds of data sets on breast CT volume, a number of studies were performed and reported which have shed light on the basic breast anatomy specific to dosimetry in mammography. It was shown that the average skin thickness of the breast was approximately 1.5 mm, instead of the 4 or 5 mm in the past. In another study, 3-D breast CT data sets were used for validation of the 2-D algorithm developed at the University of Toronto, leading to data suggesting that the overall average breast density is of the order of 16-20%, rather than the previously assumed 50%. Both of these assumptions led to normalized glandular dose (DgN) coefficients which are higher than those of the past. However, a comprehensive study on hundreds of breast CT data sets confirmed the findings of other investigators that there is a more centralized average location of glandular tissue within the breast. Combined with Monte Carlo studies for dosimetry, when accurate models of the distribution of glandular tissue were used, a 30% reduction in the radiation dose (as determined by the DgN coefficient) was found as an average across typical molybdenum and tungsten spectra used clinically. The 30% average reduction was found even when the thinner skin and the lower average breast density were considered. The article reviews three specific anatomic observations made possible based on high-resolution breast CT data by several different research groups. It is noted that, periodically, previous assumptions pertaining to dosimetry can be updated when new information becomes available, so that more accurate dosimetry is achieved. Dogmatic practices typically change slowly, but it is hoped that the medical physics community will continue to evaluate changes in DgN coefficients such that they become more accurate. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this study is to determine the optimal representative reconstruction and quantitative image feature set for a computer-aided diagnosis ( CADx) scheme for dedicated breast computer tomography ( bCT). Method We used 93 bCT scans that contain 102 breast lesions (62 malignant, 40 benign). Using an iterative image reconstruction ( IIR) algorithm, we created 37 reconstructions with different image appearances for each case. In addition, we added a clinical reconstruction for comparison purposes. We used image sharpness, determined by the gradient of gray value in a parenchymal portion of the reconstructed breast, as a surrogate measure of the image qualities/appearances for the 38 reconstructions. After segmentation of the breast lesion, we extracted 23 quantitative image features. Using leave-one-out-cross-validation ( LOOCV), we conducted the feature selection, classifier training, and testing. For this study, we used the linear discriminant analysis classifier. Then, we selected the representative reconstruction and feature set for the classifier with the best diagnostic performance among all reconstructions and feature sets. Then, we conducted an observer study with six radiologists using a subset of breast lesions ( N = 50). Using 1000 bootstrap samples, we compared the diagnostic performance of the trained classifier to those of the radiologists. Result The diagnostic performance of the trained classifier increased as the image sharpness of a given reconstruction increased. Among combinations of reconstructions and quantitative image feature sets, we selected one of the sharp reconstructions and three quantitative image feature sets with the first three highest diagnostic performances under LOOCV as the representative reconstruction and feature set for the classifier. The classifier on the representative reconstruction and feature set achieved better diagnostic performance with an area under the ROC curve ( AUC) of 0.94 (95% CI = [0.81, 0.98]) than those of the radiologists, where their maximum AUC was 0.78 (95% CI = [0.63, 0.90]). Moreover, the partial AUC, at 90% sensitivity or higher, of the classifier ( pAUC = 0.085 with 95% CI = [0.063, 0.094]) was statistically better ( P-value < 0.0001) than those of the radiologists (maximum pAUC = 0.009 with 95% CI = [0.003, 0.024]). Conclusion We found that image sharpness measure can be a good candidate to estimate the diagnostic performance of a given CADx algorithm. In addition, we found that there exists a reconstruction (i.e., sharp reconstruction) and a feature set that maximizes the diagnostic performance of a CADx algorithm. On this optimal representative reconstruction and feature set, the CADx algorithm outperformed radiologists. [ABSTRACT FROM AUTHOR]

OBJECTIVE: The purpose of this study was to utilize a dedicated breast CT system using a 2D beam stop array to physically evaluate the scatter to primary ratios (SPRs) of different geometric phantoms and prospectively acquired clinical patient data. METHODS: Including clinically unrealizable compositions of 100% glandular and 100% fat, projection images were acquired using three geometrically different phantoms filled with fluids simulating breast tissue. The beam stop array method was used for measuring scatter in projection space, and creating the scatter corrected primary images.2DSPRs were calculated. Additionally, a new figure of merit, the 3D normalized scatter contribution (NSC) volumes were calculated. RESULTS: The 2D SPR values (0.52–1.10) were primarily dependent on phantom geometry; a secondary dependence was due to their uniform density; 2D SPRs were low frequency and smoothly varying in the uniformly filled phantoms. SPRs of clinical patient data followed similar trends as phantoms, but with noticeable deviations and high frequency components due to the heterogeneous distribution of glandular tissue. The maximum measured patient 2D SPRs were all <0.6, even for the largest diameter breast. These results demonstrate modest scatter components with changing object geometries and densities; the 3D NSC volumes with higher frequency components help visualize scatter distribution throughout the reconstructed image volumes. Furthermore, the SPRs in the heterogeneous clinical breast cases were underestimated by the equivalent density, uniformly filled phantoms. CONCLUSIONS:These results provide guidance on the use of uniformly distributed density and differently shaped phantoms when considering simulations. They also clearly demonstrate that results from patients can vary considerably from 2D SPRs of uniformly simulated phantoms. [ABSTRACT FROM AUTHOR]

Mammography has been the mainstay of breast imaging for over four decades and is the only screening modality demonstrated to reduce mortality from breast cancer. The known limitations of mammography have prompted the development of newer imaging techniques with three-dimensional capabilities such as dedicated breast computed tomography (bCT). Several studies have shown the superiority of bCT in detection of breast masses, when compared to 2-D mammography. Malignant micro-calcification lesions can be detected and characterized by bCT. With further development of higher resolution detectors, bCT should become a modality for large population screening. Contrast-enhanced bCT (CE-bCT) adds improved specificity over mammography and may be utilized as an imaging biomarker in the emerging era of precision medicine. [ABSTRACT FROM AUTHOR]

Purpose: Current dosimetry methods in mammography assume that the breast is comprised of a homogeneous mixture of glandular and adipose tissues. Three-dimensional (3D) dedicated breast CT (bCT) data sets were used previously to assess the complex anatomical structure within the breast, characterizing the statistical distribution of glandular tissue in the breast. The purpose of this work was to investigate the effect of bCT-derived heterogeneous glandular distributions on dosimetry in mammography. Methods: bCT-derived breast diameters, volumes, and 3D fibroglandular distributions were used to design realistic compressed breast models comprised of heterogeneous distributions of glandular tissue. The bCT-derived glandular distributions were fit to biGaussian functions and used as probability density maps to assign the density distributions within compressed breast models. The MCNPX 2.6.0 Monte Carlo code was used to estimate monoenergetic normalized mean glandular dose "DgN(E)" values in mammography geometry. The DgN(E) values were then weighted by typical mammography x-ray spectra to determine polyenergetic DgN (pDgN) coefficients for heterogeneous (pDgNhetero) and homogeneous (pDgNhomo) cases. The dependence of estimated pDgN values on phantom size, volumetric glandular fraction (VGF), x-ray technique factors, and location of the heterogeneous glandular distributions was investigated. Results: The pDgNhetero coefficients were on average 35.3% (SD, 4.1) and 24.2% (SD, 3.0) lower than the pDgNhomo coefficients for the Mo-Mo and W-Rh x-ray spectra, respectively, across all phantom sizes and VGFs when the glandular distributions were centered within the breast phantom in the coronal plane. At constant breast size, increasing VGF from 7.3% to 19.1% lead to a reduction in pDgNhetero relative to pDgNhomo of 23.6%-27.4% for a W-Rh spectrum. Displacement of the glandular distribution, at a distance equal to 10% of the compressed breast width in the superior and inferior directions, resulted in a 37.3% and a -26.6% change in the pDgNhetero coefficient, respectively, relative to the centered distribution for the Mo-Mo spectrum. Lateral displacement of the glandular distribution, at a distance equal to 10% of the compressed breast width, resulted in a 1.5% change in the pDgNhetero coefficient relative to the centered distribution for the W-Rh spectrum. Conclusions: Introducing bCT-derived heterogeneous glandular distributions into mammography phantom design resulted in decreased glandular dose relative to the widely used homogeneous assumption. A homogeneous distribution overestimates the amount of glandular tissue near the entrant surface of the breast, where dose deposition is exponentially higher. While these findings are based on clinically measured distributions of glandular tissue using a large cohort of women, future work is required to improve the classification of glandular distributions based on breast size and overall glandular fraction. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to measure the effectiveness of local curvature measures as novel image features for classifying breast tumors. Methods: A total of 119 breast lesions from 104 noncontrast dedicated breast computed tomography images of women were used in this study. Volumetric segmentation was done using a seed-based segmentation algorithm and then a triangulated surface was extracted from the resulting segmentation. Total, mean, and Gaussian curvatures were then computed. Normalized curvatures were used as classification features. In addition, traditional image features were also extracted and a forward feature selection scheme was used to select the optimal feature set. Logistic regression was used as a classifier and leave-one-out cross-validation was utilized to evaluate the classification performances of the features. The area under the receiver operating characteristic curve (AUC, area under curve) was used as a figure of merit. Results: Among curvature measures, the normalized total curvature (CT) showed the best classification performance (AUC of 0.74), while the others showed no classification power individually. Five traditional image features (two shape, two margin, and one texture descriptors) were selected via the feature selection scheme and its resulting classifier achieved an AUC of 0.83. Among those five features, the radial gradient index (RGI), which is a margin descriptor, showed the best classification performance (AUC of 0.73). A classifier combining RGI and CT yielded an AUC of 0.81, which showed similar performance (i.e., no statistically significant difference) to the classifier with the above five traditional image features. Additional comparisons in AUC values between classifiers using different combinations of traditional image features and CT were conducted. The results showed that CT was able to replace the other four image features for the classification task. Conclusions: The normalized curvature measure contains useful information in classifying breast tumors. Using this, one can reduce the number of features in a classifier, which may result in more robust classifiers for different datasets. [ABSTRACT FROM AUTHOR]

Purpose: For a given bowtie filter design, both the selection of material and the physical design control the energy fluence, and consequently the dose distribution, in the object. Using three previously described bowtie filter designs, the goal of this work is to demonstrate the effect that different materials have on the bowtie filter performance measures. Methods: Three bowtie filter designs that compensate for one or more aspects of the beam-modifying effects due to the differences in path length in a projection have been designed. The nature of the designs allows for their realization using a variety of materials. The designs were based on a phantom, 14 cm in diameter, composed of 40% fibroglandular and 60% adipose tissue. Bowtie design #1 is based on single material spectral matching and produces nearly uniform spectral shape for radiation incident upon the detector. Bowtie design #2 uses the idea of basis-material decomposition to produce the same spectral shape and intensity at the detector, using two different materials. With bowtie design #3, it is possible to eliminate the beam hardening effect in the reconstructed image by adjusting the bowtie filter thickness so that the effective attenuation coefficient for every ray is the same. Seven different materials were chosen to represent a range of chemical compositions and densities. After calculation of construction parameters for each bowtie filter design, a bowtie filter was created using each of these materials (assuming reasonable construction parameters were obtained), resulting in a total of 26 bowtie filters modeled analytically and in the PENELOPE Monte Carlo simulation environment. Using the analytical model of each bowtie filter, design profiles were obtained and energy fluence as a function of fan-angle was calculated. Projection images with and without each bowtie filter design were also generated using PENELOPE and reconstructed using FBP. Parameters such as dose distribution, noise uniformity, and scatter were investigated. Results: Analytical calculations with and without each bowtie filter show that some materials for a given design produce bowtie filters that are too large for implementation in breast CT scanners or too small to accurately manufacture. Results also demonstrate the ability to manipulate the energy fluence distribution (dynamic range) by using different materials, or different combinations of materials, for a given bowtie filter design. This feature is especially advantageous when using photon counting detector technology. Monte Carlo simulation results from PENELOPE show that all studied material choices for bowtie design #2 achieve nearly uniform dose distribution, noise uniformity index less than 5%, and nearly uniform scatter-to-primary ratio. These same features can also be obtained using certain materials with bowtie designs #1 and #3. Conclusions: With the three bowtie filter designs used in this work, the selection of material is an important design consideration. An appropriate material choice can improve image quality, dose uniformity, and dynamic range. [ABSTRACT FROM AUTHOR]

Purpose: A novel breast CT system capable of arbitrary 3D trajectories has been developed to address cone beam sampling insufficiency as well as to image further into the patient's chest wall. The purpose of this study was to characterize any trajectory-related differences in 3D x-ray dose distribution in a pendant target when imaged with different orbits. Methods: Two acquisition trajectories were evaluated: circular azimuthal (no-tilt) and sinusoidal (saddle) orbit with ±15? tilts around a pendant breast, using Monte Carlo simulations as well as physical measurements. Simulations were performed with tungsten (W) filtration of a W-anode source; the simulated source flux was normalized to the measured exposure of a W-anode source. A water-filled cylindrical phantom was divided into 1 cm³ voxels, and the cumulative energy deposited was tracked in each voxel. Energy deposited per voxel was converted to dose, yielding the 3D distributed dose volumes. Additionally, three cylindrical phantoms of different diameters (10, 12.5, and 15 cm) and an anthropomorphic breast phantom, initially filled with water (mimicking pure fibroglandular tissue) and then with a 75% methanol-25% water mixture (mimicking 50-50 fibroglandular-adipose tissues), were used to simulate the pendant breast geometry and scanned on the physical system. Ionization chamber calibrated radiochromic film was used to determine the dose delivered in a 2D plane through the center of the volume for a fully 3D CT scan using the different orbits. Results: Measured experimental results for the same exposure indicated that the mean dose measured throughout the central slice for different diameters ranged from 3.93 to 5.28 mGy, with the lowest average dose measured on the largest cylinder with water mimicking a homogeneously fibroglandular breast. These results align well with the cylinder phantom Monte Carlo studies which also showed a marginal difference in dose delivered by a saddle trajectory in the central slice. Regardless of phantom material or filled fluid density, dose delivered by the saddle scan was negligibly different than the simple circular, no-tilt scans. The average dose measured in the breast phantom was marginally higher for saddle than the circular no tilt scan at 3.82 and 3.87 mGy, respectively. Conclusions: Not only does nontraditional 3D-trajectory CT scanning yield more complete sampling of the breast volume but also has comparable dose deposition throughout the breast and anterior chest volume, as verified by Monte Carlo simulation and physical measurements. [ABSTRACT FROM AUTHOR]

X-ray mammography of the compressed breast is well recognized as the "gold standard" for early detection of breast cancer, but its performance is not ideal. One limitation of screening mammography is tissue superposition, particularly for dense breasts. Since 2001, several research groups in the USA and in the European Union have developed computed tomography (CT) systems with digital detector technology dedicated to x-ray imaging of the uncompressed breast (breast CT or BCT) for breast cancer screening and diagnosis. This CT technology--tracing back to initial studies in the 1970s-- allows some of the limitations of mammography to be overcome, keeping the levels of radiation dose to the radiosensitive breast glandular tissue similar to that of two-view mammography for the same breast size and composition. This paper presents an evaluation of the research efforts carried out in the invention, development, and improvement of BCT with dedicated scanners with state-of-the-art technology, including initial steps toward commercialization, after more than a decade of R&D in the laboratory and/or in the clinic. The intended focus here is on the technological/engineering aspects of BCT and on outlining advantages and limitations as reported in the related literature. Prospects for future research in this field are discussed. [ABSTRACT FROM AUTHOR]

Mammography is the gold standard in routine screening for the detection of breast cancer in the general population. However, limitations in sensitivity, particularly in dense breasts, has motivated the development of alternative imaging techniques such as digital breast tomosynthesis, whole breast ultrasound, breast-specific gamma imaging, and more recently dedicated breast computed tomography or 'breast CT'. Virtually all diagnostic work-ups of asymptomatic nonpalpable findings arise from screening mammography. In most cases, diagnostic mammography and ultrasound are sufficient for diagnosis, with magnetic resonance imaging ( MRI) playing an occasional role. Digital breast tomosynthesis, a limited-angle tomographic technique, is increasingly being used for screening. Dedicated breast CT has full three-dimensional (3D) capability with near-isotropic resolution, which could potentially improve diagnostic accuracy. In current dedicated breast CT clinical prototypes, 300-500 low-dose projections are acquired in a circular trajectory around the breast using a flat panel detector, followed by image reconstruction to provide the 3D breast volume. The average glandular dose to the breast from breast CT can range from as little as a two-view screening mammogram to approximately that of a diagnostic mammography examination. Breast CT displays 3D images of the internal structures of the breast; therefore, evaluation of suspicious features like microcalcifications, masses, and asymmetries can be made in multiple anatomical planes from a single scan. The potential role of breast CT for diagnostic imaging is illustrated here through clinical examples such as imaging soft tissue abnormalities and microcalcifications. The potential for breast CT to serve as an imaging tool for extent of disease evaluation and for monitoring neo-adjuvant chemotherapy response is also illustrated. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this work is to develop and demonstrate a set of practical metrics for CT systems optimization. These metrics, based on the Hotelling observer (HO) figure of merit, are task-based. The authors therefore take the specific example of optimizing a dedicated breast CT system, including the reconstruction algorithm, for two relevant tasks, signal detection and Rayleigh discrimination. Methods: A dedicated breast CT system is simulated using specifications in the literature from an existing prototype. The authors optimize configuration and image reconstruction algorithm parameters for two tasks: the detection of simulated microcalcifications and the discrimination of two adjacent, high-contrast signals, known as the Rayleigh discrimination task. The effects on task performance of breast diameter, signal location, image grid size, projection view number, and reconstruction filter were all investigated. Two HO metrics were evaluated: the percentage of correct decisions in a two-alternative forced choice experiment (equivalent to area under the ROC curve or AUC), and the HO efficiency, defined as the squared ratio of HO signal-to-noise ratio (SNR) in the reconstructed image to HO SNR in the projection data. Results: The ease and efficiency of the HO metric computation allows a rapid high-resolution survey of many system parameters. Optimization of a range of system parameters using the HO results in images that subjectively appear optimal for the tasks investigated. Further, the results of assessment through the HO reproduce closely many existing results in the literature regarding the impact of parameter selection on image quality. Conclusions: This study demonstrates the utility of a task-based approach to system design, evaluation, and optimization. The methodology presented is equally applicable to determining the impact of a wide range of factors, including patient parameters, system and acquisition design, and the reconstruction algorithm. The results demonstrate the versatility of the proposed HO formalism by not only generating a set of parameters that are optimal for a given task but also by qualitatively reproducing many existing results from the breast CT literature. Meanwhile, the implementation of the proposed methodology is straightforward and entirely simulation-based. This is an attractive feature for many system optimization problems, where the goal is to analyze the individual system components such as the image reconstruction algorithm. Final assessment of the system as a whole should be based also on real data studies. [ABSTRACT FROM AUTHOR]

To investigate the feasibility of noncontrast and contrast-enhanced cone beam breast Computed Tomography ( CT) in demonstrating malignant breast lesions in the diagnostic setting. This Institutional Review Board approved, Health Information Portability and Accountability Act compliant, prospective study enrolled BI- RADS four and five patients from 2008 to 2010. Eighty-seven subjects had noncontrast breast CT, 42 had contrast-enhanced breast CT ( CE-breast CT) with 70 pathologically confirmed cancer diagnoses. All 70 comprise the study cohort for noncontrast breast CT, and 23 who had CE-breast CT comprise the cohort for CE-breast CT. All had diagnostic work-up. Patient age, breast density, lesion size and characteristics, biopsy method, and core pathology were recorded. A Fisher's exact test was used to detect a difference in detectability. For agreement in size measurement between the imaging modalities, a paired t-test was employed. Reported p-values were based on 2-sided tests. Two one-sided tests were calculated to determine equivalence within ±0.3 cm at a 90% significance level. Noncontrast breast CT identified 67 of 70 malignant lesions, detected by diagnostic work-up. CE-breast CT identified 23 of 23 index malignant lesions and in addition, found three malignant lesions in three cases not previously detected. Noncontrast breast CT demonstrated the index lesion in 67 of 70 cases and CE-breast CT demonstrated the index lesion in all 23 cases. An additional three new malignant lesions not seen with conventional diagnostic work-up were detected. In this preliminary study, breast CT with or without contrast was shown to be accurate at identifying malignant breast lesions in the diagnostic setting. [ABSTRACT FROM AUTHOR]

Purpose: Breast CT is an emerging imaging technique that can portray the breast in 3D and improve visualization of important diagnostic features. Early clinical studies have suggested that breast CT has sufficient spatial and contrast resolution for accurate detection of masses and microcalcifications in the breast, reducing structural overlap that is often a limiting factor in reading mammographic images. For a number of reasons, image quality in breast CT may be improved by use of an energy resolving photon counting detector. In this study, the authors investigate the improvements in image quality obtained when using energy weighting with an energy resolving photon counting detector as compared to that with a conventional energy integrating detector. Methods: Using computer simulation, realistic CT images of multiple breast phantoms were generated. The simulation modeled a prototype breast CT system using an amorphous silicon (a-Si), CsI based energy integrating detector with different x-ray spectra, and a hypothetical, ideal CZT based photon counting detector with capability of energy discrimination. Three biological signals of interest were modeled as spherical lesions and inserted into breast phantoms; hydroxyapatite (HA) to represent microcalcification, infiltrating ductal carcinoma (IDC), and iodine enhanced infiltrating ductal carcinoma (IIDC). Signal-to-noise ratio (SNR) of these three lesions was measured from the CT reconstructions. In addition, a psychophysical study was conducted to evaluate observer performance in detecting microcalcifications embedded into a realistic anthropomorphic breast phantom. Results: In the energy range tested, improvements in SNR with a photon counting detector using energy weighting was higher (than the energy integrating detector method) by 30%-63% and 4%-34%, for HA and IDC lesions and 12%-30% (with Al filtration) and 32%-38% (with Ce filtration) for the IIDC lesion, respectively. The average area under the receiver operating characteristic curve (AUC) for detection of microcalcifications was higher by greater than 19% (for the different energy weighting methods tested) as compared to the AUC obtained with an energy integrating detector. Conclusions: This study showed that breast CT with a CZT photon counting detector using energy weighting can provide improvements in pixel SNR, and detectability of microcalcifications as compared to that with a conventional energy integrating detector. Since a number of degrading physical factors were not modeled into the photon counting detector, this improvement should be considered as an upper bound on achievable performance. [ABSTRACT FROM AUTHOR]

Purpose: Dedicated breast CT has great potential for improving the detection and diagnosis of breast cancer. Statistical iterative reconstruction (SIR) in dedicated breast CT is a promising alternative to traditional filtered backprojection (FBP). One of the difficulties in using SIR is the presence of free parameters in the algorithm that control the appearance of the resulting image. These parameters require tuning in order to achieve high quality reconstructions. In this study, the authors investigated the penalized maximum likelihood (PML) method with two commonly used types of roughness penalty functions: hyperbolic potential and anisotropic total variation (TV) norm. Reconstructed images were compared with images obtained using standard FBP. Optimal parameters for PML with the hyperbolic prior are reported for the task of detecting microcalcifications embedded in breast tissue. Methods: Computer simulations were used to acquire projections in a half-cone beam geometry. The modeled setup describes a realistic breast CT benchtop system, with an x-ray spectra produced by a point source and an a-Si, CsI:Tl flat-panel detector. A voxelized anthropomorphic breast phantom with 280 μm microcalcification spheres embedded in it was used to model attenuation properties of the uncompressed woman's breast in a pendant position. The reconstruction of 3D images was performed using the separable paraboloidal surrogates algorithm with ordered subsets. Task performance was assessed with the ideal observer detectability index to determine optimal PML parameters. Results: The authors' findings suggest that there is a preferred range of values of the roughness penalty weight and the edge preservation threshold in the penalized objective function with the hyperbolic potential, which resulted in low noise images with high contrast microcalcifications preserved. In terms of numerical observer detectability index, the PML method with optimal parameters yielded substantially improved performance (by a factor of greater than 10) compared to FBP. The hyperbolic prior was also observed to be superior to the TV norm. A few of the best-performing parameter pairs for the PML method also demonstrated superior performance for various radiation doses. In fact, using PML with certain parameter values results in better images, acquired using 2 mGy dose, than FBP-reconstructed images acquired using 6 mGy dose. Conclusions: A range of optimal free parameters for the PML algorithm with hyperbolic and TV norm-based potentials is presented for the microcalcification detection task, in dedicated breast CT. The reported values can be used as starting values of the free parameters, when SIR techniques are used for image reconstruction. Significant improvement in image quality can be achieved by using PML with optimal combination of parameters, as compared to FBP. Importantly, these results suggest improved detection of microcalcifications can be obtained by using PML with lower radiation dose to the patient, than using FBP with higher dose. [ABSTRACT FROM AUTHOR]

Objective: This pilot study was undertaken to compare radiation dose, relative visibility/ conspicuity of biopsy-proven lesions, and relative patient comfort in diagnostic mammography and dedicated cone-beam breast computed tomography (CBBCT) in Breast Imaging-Reporting and Data System (BI-RADS)® 4 or 5 lesions. Materials and Methods: Thirty-six consecutive patients (37 breasts) with abnormal mammographic and/ or ultrasound categorized as BI-RADS® 4 or 5 lesions were evaluated with CBBCT prior to biopsy. Administered radiation dose was calculated for each modality. Mammograms and CBBCT images were compared side-by-side and lesion visibility/conspicuity was qualitatively scored. Histopathology of lesions was reviewed. Patients were administered a survey for qualitative evaluation of comfort between the two modalities. Results: CBBCT dose was similar to or less than diagnostic mammography, with a mean dose of 9.4 mGy (±3.1 SD) for CBBCT vs. 16.9 mGy (±6.9 SD) for diagnostic mammography in a total of 37 imaged breasts (P<0.001). Thirty-three of 34 mammographic lesions were scored as equally or better visualized in CBBCT relative to diagnostic mammography. Characterization of high-risk lesions was excellent. Patients reported greater comfort in CBBCT imaging relative to mammography. Conclusion: Our experience of side-by-side comparison of CBBCT and diagnostic mammography in BI-RADS® 4 and 5 breast lesions demonstrated a high degree of correlation between the two modalities across a variety of lesion types. Owing to favorable radiation dose profile, excellent visualization of lesions, and qualitative benefits including improved patient comfort, excellent field-of-view, and more anatomical evaluation of lesion margins, CBBCT offers a promising modality for diagnostic evaluation of breast lesions. [ABSTRACT FROM AUTHOR]

Breast CT is an emerging modality that reconstructs 3D linear attenuation coefficient (μ) images of the breast. Its tomographic nature reduces the overlap of structures and may improve tissue visualization. Current prototype systems produce large levels of scatter that could be used to reconstruct electron density (ρ _{e}) images. This could potentially enhance diagnosis. We are developing a first generation bench top CT system to investigate the benefits of simultaneous imaging μ and ρ _{e} of the intact breast. The system uses an algorithm capable of reconstructing ρ _{e} images from single Klein-Nishina scatter. It has been suggested that this algorithm may be impractical since measurements include coherent, bound incoherent and multiple scatter. To investigate this, the EGSnrc Monte Carlo (MC) code was used to simulate scans using a first generation system. These simulations were used to quantify the dose per scan, to provide raw data for the ρ _{e} reconstructions and to investigate corrections for multiple and coherent scatter since these can not be directly related to ρ _{e}. MC simulations show that the dose coefficients are similar to those of cone beam breast CT. Coherent scatter is only ∼9% concentrated in scattering angles < 8°. Electron binding reduces the number of incoherently scattered photons but this reduction can be included in the quantification of scatter measured by the system. Multiple scatter was found to be the major source of errors and, if not corrected for, can result in an overestimation of ρ _{e} by more than a factor of two. Empirical corrections, based on breast thickness or radiological path, can be used to reconstruct images where the variance in &rho _{e} error is half of that found in images derived from primary photons only. Although some practical challenges remain in creating a laboratory system, this work has shown that it is possible to reconstruct scatter images of the breast with a 4 mGy dose and further experimental evaluation of this technique is warranted. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the glandular dose magnitudes and characteristics resulting from image acquisition using a dedicated breast computed tomography (BCT) clinical prototype imaging system. Methods: The x-ray spectrum and output characteristics of a BCT clinical prototype (Koning Corporation, West Henrietta, NY) were determined using empirical measurements, breast phantoms, and an established spectrum model. The geometry of the BCT system was replicated in a Monte Carlo-based computer simulation using the GEANT4 toolkit and was validated by comparing the simulated results for exposure distribution in a standard 16 cm CT head phantom with those empirically determined using a 10 cm CT pencil ionization chamber and dosimeter. The computer simulation was further validated by replicating the results of a previous BCT dosimetry study. Upon validation, the computer simulation was modified to include breasts of varying sizes and homogeneous compositions spanning those encountered clinically, and the normalized mean glandular dose resulting from BCT was determined. Using the system’s measured exposure output determined automatically for breasts of different size and density, the mean glandular dose for these breasts was computed and compared to the glandular dose resulting from mammography. Finally, additional Monte Carlo simulations were performed to study how the glandular dose values vary within the breast tissue during acquisition with both this BCT prototype and a typical craniocaudal (CC) mammographic acquisition. Results: This BCT prototype uses an x-ray spectrum with a first half-value layer of 1.39 mm Al and a mean x-ray energy of 30.3 keV. The normalized mean glandular dose for breasts of varying size and composition during BCT acquisition with this system ranges from 0.278 to 0.582 mGy/mGy air kerma with the reference air kerma measured in air at the center of rotation. Using the measured exposure outputs for the tube currents automatically selected by the system for the breasts of different sizes and densities, the mean glandular dose for a BCT acquisition with this prototype system varies from 5.6 to 17.5 mGy, with the value for a breast of mean size and composition being 17.06 mGy. The glandular dose throughout the breast tissue of this mean breast varies by up to ±50% of the mean value. During a typical CC view mammographic acquisition of an equivalent mean breast, which typically results in a mean glandular dose of 2.0–2.5 mGy, the glandular dose throughout the breast tissue varies from ∼15% to ∼400% of the mean value. Conclusions: Acquisition of a BCT image with the automated tube output settings for a mean breast with the Koning Corp. clinical prototype results in mean glandular dose values approximately equivalent to three to five two-view mammographic examinations for a similar breast. For all breast sizes and compositions studied, this glandular dose ratio between acquisition with this BCT prototype and two-view mammography ranges from 1.4 to 7.2. In mammography, portions of the mean-sized breast receive a considerably higher dose than the mean value for the whole breast. However, only a small portion of a breast undergoing mammography would receive a glandular dose similar to that from BCT. [ABSTRACT FROM AUTHOR]

Purpose: This work investigates a framework for modeling volumetric breast imaging to compare detection and estimation task performance and optimize quantitative breast imaging. Methods: Volumetric reconstructions of a breast phantom, which incorporated electronic, quantum, and anatomical noise with embedded spherical lesions, were simulated over a range of acquisition angles varying from 4° to 204° with a constant total acquisition dose of 1.5 mGy. A maximum likelihood estimator was derived in terms of the noise power spectrum, which yielded figures of merit for quantitative imaging performance in terms of accuracy and precision. These metrics were computed for estimation of lesion area, volume, and location. Estimation task performance was optimized as a function of acquisition angle and compared to the performance of a more conventional lesion detection task. Results: Results revealed tradeoffs between electronic, quantum, and anatomical noise. The detection of a 4 mm sphere was optimal at an acquisition angle of 84°, where reconstructed images using a smaller acquisition angle exhibited increased anatomical noise and reconstructed images using a larger acquisition angle exhibited increased quantum and electronic noise. For all estimation tasks, accuracy was found to be fairly constant as a function acquisition angle indicating adequate system calibration, whereas a more significant dependence on acquisition angle was observed for precision performance. Precision for the 2D area estimation task was optimal at ∼104°, while precision of the 3D volume estimation task was optimal at larger angles (∼124°). Precision for the localization task showed orientation dependence where localization was significantly inferior in the depth direction. Overall, precision for localization was optimal at larger angles (i.e., >125°) compared to the size estimation tasks. Results suggested that for quantitative imaging tasks, the acquisition angle should be larger than currently used in conventional breast tomosynthesis for lesion detection. Conclusions: Analysis of quantitative imaging performance using Fourier-based metrics highlights the difference between estimation and detection task in volumetric breast imaging and provides a meaningful framework for optimizing the performance of breast imaging systems for quantitative imaging applications. [ABSTRACT FROM AUTHOR]

Purpose: For dosimetry and for work in optimization of x-ray imaging of the breast, it is commonly assumed that the breast is composed of 50% fibroglandular tissue and 50% fat. The purpose of this study was to assess whether this assumption was realistic. Methods: First, data obtained from an experimental breast CT scanner were used to validate an algorithm that measures breast density from digitized film mammograms. Density results obtained from a total of 2831 women, including 191 women receiving CT and from mammograms of 2640 women from three other groups, were then used to estimate breast compositions. Results: Mean compositions, expressed as percent fibroglandular tissue (including the skin), varied from 13.7% to 25.6% among the groups with an overall mean of 19.3%. The mean compressed breast thickness for the mammograms was 5.9 cm (σ=1.6 cm). 80% of the women in our study had volumetric breast density less than 27% and 95% were below 45%. Conclusions: Based on the results obtained from the four groups of women in our study, the “50-50” breast is not a representative model of the breast composition. [ABSTRACT FROM AUTHOR]

Dedicated breast computed tomography (CT) imaging possesses the potential for improved lesion detection over conventional mammograms, especially for women with dense breasts. The breast CT images are acquired with a glandular dose comparable to that of standard two-view mammography for a single breast. Due to dose constraints, the reconstructed volume has a non-negligible quantum noise when thin section CT slices are visualized. It is thus desirable to reduce noise in the reconstructed breast volume without loss of spatial resolution. In this study, partial diffusion equation (PDE) based denoising techniques specifically for breast CT were applied at different steps along the reconstruction process and it was found that denoising performed better when applied to the projection data rather than reconstructed data. Simulation results from the contrast detail phantom show that the PDE technique outperforms Wiener denoising as well as adaptive trimmed mean filter. The PDE technique increases its performance advantage relative to Wiener techniques when the photon fluence is reduced. With the PDE technique, the sensitivity for lesion detection using the contrast detail phantom drops by less than 7% when the dose is cut down to 40% of the two-view mammography. For subjective evaluation, the PDE technique was applied to two human subject breast data sets acquired on a prototype breast CT system. The denoised images had appealing visual characteristics with much lower noise levels and improved tissue textures while maintaining sharpness of the original reconstructed volume. [ABSTRACT FROM AUTHOR]

The effect of breast skin thickness on dosimetry in mammography was investigated. Breast computed tomography (CT) acquisition techniques, combined with algorithms designed for determining specific breast metrics, were useful for estimating skin thickness. A radial-geometry edge detection scheme was implemented on coronal reconstructed breast CT (bCT) images to measure the breast skin thickness. Skin thickness of bilateral bCT volume data from 49 women and unilateral bCT volume data from 2 women (10 healthy women and 41 women with BIRADS 4 and 5 diagnoses) was robustly measured with the edge detection scheme. The mean breast skin thickness (±inter-breast standard deviation) was found to be 1.45±0.30 mm. Since most current published normalized glandular dose (DgN) coefficients are based on the assumption of a 4-mm breast skin thickness, the DgN values computed with Monte Carlo techniques will increase up to 18% due to the thinner skin layers (e.g., 6-cm 50% glandular breast, 28 kVp Mo–Mo spectrum). The thinner skin dimensions found in this study suggest that the current DgN values used for mammographic dosimetry lead to a slight underestimate in glandular dose. [ABSTRACT FROM AUTHOR]

In cone beam breast computed tomography (CT), scattered radiation leads to nonuniform biasing of CT numbers known as a cupping artifact. Besides being visual distractions, cupping artifacts appear as background nonuniformities, which impair efficient gray scale windowing and pose a problem in threshold based volume visualization/segmentation. To overcome this problem, we have developed a background nonuniformity correction method specifically designed for cone beam breast CT. With this technique, the cupping artifact is modeled as an additive background signal profile in the reconstructed breast images. Due to the largely circularly symmetric shape of a typical breast, the additive background signal profile was also assumed to be circularly symmetric. The radial variation of the background signals was estimated by measuring the spatial variation of adipose tissue signals in front view breast images. To extract adipose tissue signals in an automated manner, a signal sampling scheme in polar coordinates and a background trend fitting algorithm were implemented. The background fits compared with targeted adipose tissue signal value (constant throughout the breast volume) to get an additive correction value for each tissue voxel. To test the accuracy, we applied the technique to cone beam CT images of mastectomy specimens. After correction, the images demonstrated significantly improved signal uniformity in both front and side view slices. The reduction of both intraslice and interslice variations in adipose tissue CT numbers supported our observations. [ABSTRACT FROM AUTHOR]

We evaluate the feasibility, benefits, and operating parameters of a quasimonochromatic beam for a newly developed x-ray cone beam computed mammotomography application. The value of a near monochromatic x-ray source for fully 3D dedicated mammotomography is the expected improved ability to separate tissues with very small differences in attenuation coefficients while maintaining dose levels at or below that of existing dual view mammography. In previous studies, simulations for a range of tungsten tube potentials, K-edge fitter materials, fitter thicknesses, and a 12 cm uncompressed breast, with a digital flat-panel CsI(Tl) detector model, indicated that thick, rare earth filter materials may provide optimized image quality. Figures of merit computed included: lesion contrast under different filtering conditions; ratio of measured lesion contrast with and without filtering; and exposure efficiency (SNR² /exposure). Initial experiments are performed with a custom built x-ray mammotomography system, cerium foil filters, and plastic breast and lesion tissue-equivalent slabs. Simulation results showed that tube potentials of 50-70 kVp with filters of Z = 57-63 yielded quasimonochromatic x-ray spectra with improved FOMs. Initial experimental measurements corroborate simulation results in that, relative trends and rank order of contrast ratios and exposure efficiency were in agreement. These studies show that this approach can be implemented practically with simple hardware and yield improved exposure efficiency versus the unfiltered or minimally filtered case. [ABSTRACT FROM AUTHOR]

Purpose: In the research and development of dedicated tomographic breast imaging systems, digital breast object models, also known as digital phantoms, are useful tools. While various digital breast phantoms do exist, the purpose of this study was to develop a realistic high-resolution model suitable for simulating three-dimensional (3D) breast imaging modalities. The primary goal was to design a model capable of producing simulations with realistic breast tissue structure. Methods: The methodology for generating an ensemble of digital breast phantoms was based on imaging surgical mastectomy specimens using a benchtop, cone-beam computed tomography system. This approach allowed low-noise, high-resolution projection views of the mastectomy specimens at each angular position. Reconstructions of these projection sets were processed using correction techniques and diffusion filtering prior to segmentation into breast tissue types in order to generate phantoms. Results: Eight compressed digital phantoms and 20 uncompressed phantoms from which an additional 96 pseudocompressed digital phantoms with voxel dimensions of 0.2 mm3 were generated. Two distinct tissue classification models were used in forming breast phantoms. The binary model classified each tissue voxel as either adipose or fibroglandular. A multivalue scaled model classified each tissue voxel as percentage of adipose tissue (range 1%-99%). Power spectral analysis was performed to compare simulated reconstructions using the breast phantoms to the original breast specimen reconstruction, and fits were observed to be similar. Conclusions: The digital breast phantoms developed herein provide a high-resolution anthropomorphic model of the 3D uncompressed and compressed breast that are suitable for use in evaluating and optimizing tomographic breast imaging modalities. The authors believe that other research groups might find the phantoms useful, and therefore they offer to make them available for wider use. [ABSTRACT FROM AUTHOR]