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Dedicated breast CT is being increasingly used for breast imaging. This technique provides images with no compression, removal of tissue overlap, rapid acquisition, and available simultaneous assessment of microcalcifications and contrast enhancement. In this second installment in a 2-part review, the current status of clinical applications and ongoing efforts to develop new imaging systems are discussed, with particular emphasis on how to achieve optimized practice including lesion detection and characterization, response to therapy monitoring, density assessment, intervention, and implant evaluation. The potential for future screening with breast CT is also addressed. KEY POINTS: • Dedicated breast CT is an emerging modality with enormous potential in the future of breast imaging by addressing numerous clinical needs from diagnosis to treatment. • Breast CT shows either noninferiority or superiority with mammography and numerical comparability to MRI after contrast administration in diagnostic statistics, demonstrates excellent performance in lesion characterization, density assessment, and intervention, and exhibits promise in implant evaluation, while potential application to breast cancer screening is still controversial. • New imaging modalities such as phase-contrast breast CT, spectral breast CT, and hybrid imaging are in the progress of R & D.

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., Medical Physics, 49 (6), ISSN:0094-2405, ISSN:2473-4209, ISSN:1522-8541

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 ( DgN CT ) 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 ( f g = 0.01 , 0.15 , 0.5 , 1 ) , chest wall diameters ( d = 8 , 10 , 14 , 18 , 20 cm), and chest wall to nipple length ( l = 0.75 d ), aligned with the axis of rotation (AOR) located at 65 cm from the focal spot to determine the DgN CT . 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 DgN CT ranged 0.177 - 0.574 mGy/mGy and reduction in DgN CT 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 DgN CT ranged 0.221 - 0.581 mGy/mGy and reduction in DgN CT was observed for all breast diameters. The reduction in DgN CT 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 DgN CT with offset-detector geometries was not dependent on f g . Numerical fits of DgN CT d , l , f g were generated for each geometry. Conclusion The DgN CT and the numerical fit, D g N CT d , l , f g 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.

Purpose: To demonstrate the utility of high-resolution micro-computed tomography ( μ CT ) for determining ground-truth size and shape properties of calcium grains for evaluation of detection performance in breast CT (bCT). Approach: Calcium carbonate grains ( ∼ 200 μ m ) were suspended in 1% agar solution to emulate microcalcifications ( μ Calcs ) within a fibroglandular tissue background. Ground-truth imaging was performed on a commercial μ CT scanner and was used for assessing calcium-grain size and shape, and for generating μ Calc signal profiles. Calcium grains were placed within a realistic breast-shaped phantom and imaged on a prototype bCT system at 3- and 6-mGy mean glandular dose (MGD) levels, and the non-prewhitening detectability was assessed. Additionally, the μ CT -derived signal profiles were used in conjunction with the bCT system characterization (MTF and NPS) to obtain predictions of bCT detectability. Results: Estimated detectability of the calcium grains on the bCT system ranged from 2.5 to 10.6 for 3 mGy and from 3.8 to 15.3 for 6 mGy with large fractions of the grains meeting the Rose criterion for visibility. Segmentation of μ CT images based on morphological operations produced accurate results in terms of segmentation boundaries and segmented region size. A regression model linking bCT detectability to μ Calc parameters indicated significant effects of μ Calc size and vertical position within the breast phantom. Detectability using μ CT -derived detection templates and bCT statistical properties (MTF and NPS) were in good correspondence with those measured directly from bCT ( R 2 > 0.88 ). Conclusions: Parameters derived from μ CT ground-truth data were shown to produce useful characterizations of detectability when compared to estimates derived directly from bCT. Signal profiles derived from μ CT imaging can be used in conjunction with measured or hypothesized statistical properties to evaluate the performance of a system, or system component, that may not currently be available.

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.

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)C T 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)C T 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.

Item does not contain fulltext 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.

Research into conebeam CT concepts began as soon as the first clinical single-slice CT scanner was conceived. Early implementations of conebeam CT in the 1980s focused on high-contrast applications where concurrent high resolution (

PurposeClinical 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.MethodsThis 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 4ms pulse width) at 43.5fps 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 (5mg/ml) were used to fill two cylindrical wells in the phantoms.ResultsAir kerma levels required for obtaining an MGD of 6mGy ranged from 7.1 to 9.1mGy and are reported across all kV, filtration, and breast phantom sizes. However, at 50kV, the thick filters (0.3mm 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 50kV with 0.2mm Cu or 0.2mm 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 70kV (50 and 60kV were not significantly different). Thicker Gd filtration (0.3mm 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.ConclusionsThe choice of 60kV with 0.2mm Gd filtration provides a good trade-off for maximizing both soft tissue and iodine contrast. This scanning technique takes advantage of the ~50keV Gd k-edge to produce contrast and can be achieved within operating range of the x-ray generator used in this work. Imaging at 60kV 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.

Contains fulltext : 232918.pdf (Publisher’s version ) (Open Access) 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.

The application of phase-contrast imaging to breast cancer detection has always been a driving force in the development of phase-sensitive techniques. By making use of the physical foundations of propagation-based imaging previously introduced, this chapter is focused on the specific application in the field of breast computed tomography.

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.

A deep learning (DL) network for 2D-based breast mass segmentation in unenhanced dedicated breast CT images was developed and validated, and its robustness in radiomic feature stability and diagnostic performance compared to manual annotations of multiple radiologists was investigated. 93 mass-like lesions were extensively augmented and used to train the network (n = 58 masses), which was then tested (n = 35 masses) against manual ground truth of a qualified breast radiologist with experience in breast CT imaging using the Conformity coefficient (with a value equal to 1 indicating a perfect performance). Stability and diagnostic power of 672 radiomic descriptors were investigated between the computerized segmentation, and 4 radiologists' annotations for the 35 test set cases. Feature stability and diagnostic performance in the discrimination between benign and malignant cases were quantified using intraclass correlation (ICC) and multivariate analysis of variance (MANOVA), performed for each segmentation case (4 radiologists and DL algorithm). DL-based segmentation resulted in a Conformity of 0.85 ± 0.06 against the annotated ground truth. For the stability analysis, although modest agreement was found among the four annotations performed by radiologists (Conformity 0.78 ± 0.03), over 90% of all radiomic features were found to be stable (ICC>0.75) across multiple segmentations. All MANOVA analyses were statistically significant (p ≤ 0.05), with all dimensions equal to 1, and Wilks’ lambda ≤0.35. In conclusion, DL-based mass segmentation in dedicated breast CT images can achieve high segmentation performance, and demonstrated to provide stable radiomic descriptors with comparable discriminative power in the classification of benign and malignant tumors to expert radiologist annotation.

Iodinated contrast-enhanced X-ray imaging of the breast has been studied with various modalities, including full-field digital mammography (FFDM), digital breast tomosynthesis (DBT), and dedicated breast CT. Contrast imaging with breast CT has a number of advantages over FFDM and DBT, including the lack of breast compression, and generation of fully isotropic 3-D reconstructions. Nonetheless, for breast CT to be considered as a viable tool for routine clinical use, it would be desirable to reduce radiation dose. One approach for dose reduction in breast CT is spectral shaping using X-ray filters. In this paper, two high atomic number filter materials are studied, namely, gadolinium (Gd) and erbium (Er), and compared with Al and Cu filters currently used in breast CT systems. Task-based performance is assessed by imaging a cylindrical poly(methyl methacrylate) phantom with iodine inserts on a benchtop breast CT system that emulates clinical breast CT. To evaluate detectability, a channelized hoteling observer (CHO) is used with sums of Laguerre-Gauss channels. It was observed that spectral shaping using Er and Gd filters substantially increased the dose efficiency (defined as signal-to-noise ratio of the CHO divided by mean glandular dose) as compared with kilovolt peak and filter settings used in commercial and prototype breast CT systems. These experimental phantom study results are encouraging for reducing dose of breast CT, however, further evaluation involving patients is needed.

Rationale and Objectives Dedicated breast computed tomography (DBCT) is an emerging and promising modality for breast lesions. The objective of this study was to evaluate the potential use of applying the BI-RADS Mammography Atlas 5th Edition for reporting and assessing breast lesions on DBCT. Currently, no atlas exists for DBCT. Materials and Methods Four radiologists trained in breast imaging were recruited in this institutional review board-approved, Health Insurance Portability and Accountability Act-compliant study. The enrolled radiologists, who were blinded to mammographic and histopathologic findings, individually reviewed 30 randomized DBCT cases that contained marked lesions. Thirty-four lesions were included in this study: 24 (70.6%) masses, 7 (20.6%) calcifications, and 3 (8.8%) architectural distortions. Eight (23.5%) lesions were malignant and 26 (76.5%) were benign. The reader was asked to specify according to the BI-RADS Mammography Atlas for each marked DBCT lesion: primary findings, features, breast density, and final assessment. We calculated readers' diagnostic performances for differentiating between benign and malignant lesions and interobserver variability for reporting and assessing lesions using a generalized estimating equation and the Fleiss kappa (κ) statistic. Results The estimated overall sensitivity of the readers was 0.969, and the specificity was 0.529. There were no significant differences in the sensitivity and the specificity between lesion types. For reporting the presence of a primary finding, the overall substantial agreement (κ = 0.70) was seen. In assigning the breast density and the final assessment, the overall agreement was moderate (κ = 0.53) and fair (κ = 0.30). Conclusion The use of the BI-RADS Mammography Atlas 5th Edition for DBCT showed high performance and good agreement among readers.

PurposeThe 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).MethodWe 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.ResultThe 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&nbsp

The dedicated cone-beam breast computed tomography (CBBCT) is a new and promising imaging modality which provides isotropic, 3D images of the breast with high spatial and contrast resolution. Non-contrast and contrast-enhanced CBBCT (CE-CBBCT) was superior to mammography for the visualization of breast masses, especially in patients with dense breast tissue. CE-CBBCT accurately detects DCIS and distinguishes it from benign causes of microcalcifications when compared with non-contrast CBBCT and mammography. The purpose of this report is to describe the technology and its possible indications, and to present the first results from recent clinical studies, illustrating these with our own image examples.

The original version of this article, published on 03 January 2020, unfortunately contained two mistakes.

We propose a novel approach for the fabrication of realistic in shape, size as well as in x-ray absorption properties 3D physical breast models. The approach eliminates the need for segmentation of breast tissues by directly mimicking the radiodensity of each voxel in a computed tomography (CT) image. This is done through a recently published study that enables the use of variable filament extrusion rate when creating 3D phantoms with fused deposition modeling printers. We use the CT image of a real patient’s breast to produce a model through the suggested approach. We then validate the fabricated physical breast phantom by obtaining a CT scan of it and comparing the latter to the original CT image of the source patient’s breast.

The limits of mammography have led to an increasing interest on possible alternatives such as the breast Computed Tomography (bCT). The common goal of all X-ray imaging techniques is to achieve the optimal contrast resolution, measured through the Contrast to Noise Ratio (CNR), while minimizing the radiological risks, quantified by the dose. Both dose and CNR depend on the energy and the intensity of the X-rays employed for the specific imaging technique. Some attempts to determine an optimal energy for bCT have suggested the range 22 keV–34 keV, some others instead suggested the range 50 keV–60 keV depending on the parameters considered in the study. Recent experimental works, based on the use of monochromatic radiation and breast specimens, show that energies around 32 keV give better image quality respect to setups based on higher energies. In this paper we report a systematic study aiming at defining the range of energies that maximizes the CNR at fixed dose in bCT. The study evaluates several compositions and diameters of the breast and includes various reconstruction algorithms as well as different dose levels. The results show that a good compromise between CNR and dose is obtained using energies around 28 keV.

Breast conserving surgery followed by radiotherapy is the standard of care for early-stage breast cancer patients. Deformable image registration (DIR) can in principle be of great value for accurate localization of the original tumor site to optimize breast irradiation after surgery. However, current state-of-the-art DIR methods are not very successful when tissue is present in one image but not in the other (i.e., in case of content mismatch). To tackle this challenge, we combined a multi-objective DIR approach with simulated tissue removal. Parameters defining the area to be removed as well as key DIR parameters (that are often tuned manually for each DIR case) are determined by a multi-objective optimization process. In multi-objective optimization, not one, but a set of solutions is found, that represent high-quality trade-offs between objectives of interest. We used three state-of-the-art multi-objective evolutionary algorithms as meta-optimizers to search for the optimal parameters, and tested our approach on four test cases of computed tomography (CT) images of breast cancer patients before and after surgery. Results show that using meta-optimization with simulated tissue removal improves the performance of DIR. This way, sets of high-quality solutions could be obtained with a mean target registration error of 2.4 mm over four test cases and an estimated excised volume that is within 20% from the measured volume of the surgical resection specimen.

We propose an algorithm to recognize breast parenchyma regions containing mass-like abnormalities in dedicated breast CT images using texture feature descriptors. From 53 patient breast CT scans (29 of which containing masses), we first isolated the parenchyma through automatic segmentation, and we obtained a total of 14,751 normal 2D image patches (negatives), and 2,100 containing a breast mass (positives). We extracted 141 texture features (10 first-order descriptors, 6 Haralick features, 20 run-length features, 45 structural and pattern descriptors, 60 Gabor features), which we then analyzed through multivariate analysis of variance (MANOVA) and linear discriminant analysis, resulting in an area under the ROC curve (AUC) of 0.9. We finally identified the most discriminant features through sequential forward selection, and used them to train and validate a neural network by dividing the data into multiple batches, with each batch always containing the whole set of positive cases, and as many different negative examples. To avoid the possible bias due to the high skewness in class proportion, the training was performed on all these batches independently, without re-initializing the network weights after each training. The network was tested using an additional independent 18 patient breast CT scans (8 normal and 10 containing a mass), on a total of 7,274 image patches (852 positives, 6,422 negatives) which were not used during the training/validation phase, resulting in 95.6% precision, 95.8% recall, and 0.99 AUC. Our results suggest that the proposed approach could be further evaluated and expanded for computer-aided detection tasks in breast CT imaging.

In this work, we proposed a non-linear observer model based on convolutional neural network and compare its performance with LG-CHO for four alternative forced choice detection task using simulated breast CT images. In our network, each convolutional layer contained 3×3 filters and a leaky-ReLU as an activation function, but a pooling layer and a zero padding to the output of each convolutional layer were not used unlike general convolutional neural network. Network training was conducted using ADAM optimizer with two design parameters (i.e., network depth and width). The optimal value of the design parameter was found by brute force searching, which spanned up to 30 for depth and 128 for channel, respectively. To generate training and validation dataset, we generated anatomical noise images using a power law spectrum of breast anatomy. 50% volume glandular fraction was assumed, and 1 mm diameter signal was used for detection task. The generated images were recon- structed using filtered back-projection with a fan beam CT geometry, and ramp and Hanning filters were used as an apodization filter to generate different noise structures. To train our network, 125,000 signal present images and 375,000 signal absent images were reconstructed for each apodization filter. To measure detectability, we used percent correction with 4,000 images, generated independently from training and validation dataset. Our results show that the proposed network composed of 30 layers and 64 channels provides higher detectability than LG-CHO. We believe that the improved detectability is achieved by the presence of the non-linear module (i.e., leaky-ReLU) in the network.

This work demonstrates the potential for using a deformable mapping method to register lesions between dedicated breast computed tomography (bCT) and both automated breast ultrasound (ABUS) and digital breast tomosynthesis (DBT) images (craniocaudal [CC] and mediolateral oblique [MLO] views). Two multi-modality breast phantoms with external fiducial markers attached were imaged by the three modalities. The DBT MLO view was excluded for the second phantom. The automated deformable mapping algorithm uses biomechanical modeling to determine corresponding lesions based on distances between their centers of mass (dCOM) in the deformed bCT model and the reference model (DBT or ABUS). For bCT to ABUS, the mean dCOM was 5.2 ± 2.6 mm. For bCT to DBT (CC), the mean dCOM was 5.1 ± 2.4 mm. For bCT to DBT (MLO), the mean dCOM was 4.7 ± 2.5 mm. This application could help improve a radiologist's efficiency and accuracy in breast lesion characterization, using multiple imaging modalities.

Digital phantoms are important tools for optimization and evaluation of x-ray imaging systems, and should ideally reflect the 3D structure of human anatomy and its potential variability. In addition, they need to include a good level of detail at a high enough spatial resolution to accurately model the continuous nature of the human anatomy. A pipeline to increase the spatial resolution of patient-based digital breast phantoms that can be used for computer simulations of breast imaging is proposed. Given a tomographic breast image of finite resolution, the proposed methods can generate a phantom and increase its resolution at will, not only simply through super-sampling, but also by generating additional random glandular details to account for glandular edges and strands to compensate for those that may have not been detected in the original image due to the limited spatial resolution of the imaging system used. The proposed algorithms use supervised learning to predict the loss in glandularity due to limited resolution, and then to realistically recover this loss by learning the mapping between low and high resolution images. They were trained on high-resolution synchrotron images (detector pixel size 60 μm) reconstructed at seven voxel dimensions (60 μm-480 μm), and applied to patient images acquired with a clinical breast CT system (detector pixel size 194 μm) to generate super-resolution phantoms (voxel sizes 68 μm). Several evaluations were made to assess the appropriateness of the developed methods, both with the synchrotron (relative prediction error 0.010 ± 0.004, recovering accuracy 0.95 ± 0.04), and with the clinical images (average glandularity error at 194 μm: 0.15% ± 0.12%). Finally, a breast radiologist assessed the realism of the developed phantoms by blindly comparing original and phantom images, resulting in not being able to distinguish the real from the phantom images. In conclusion, the proposed method can generate super-resolution phantoms from tomographic breast patient images that can be used for future computer simulations for optimization of new breast imaging technologies.

The inadequate visibility of microcalcifications-small calcium deposits that cue radiologists to early stages of cancer-is a major limitation in current designs of dedicated breast computed tomography (bCT). This limitation has previously been attributed to the constituent components, spatial resolution, and utilized dose. Scattered radiation has been considered an occurrence with low-frequency impacts that can be compensated for in post-processing. We hypothesized, however, that the acquisition of scattered radiation has a far more detrimental impact on clinically relevant features than has previously been understood. Critically, acquisition of scatter leads to the reduced visibility of microcalcifications. This hypothesis was investigated and supported via mathematical derivations and simulation studies. We conducted a series of comparative studies in which four bCT systems were simulated under iso-dose and iso-resolution conditions, characterizing the dependencies of microcalcification contrast on accumulated scatter. Included among the simulated systems is a novel bCT design-narrow beam bCT (NB-bCT)-that captures nearly zero scatter. We find that current bCT systems suffer from significant levels of scatter. As validated in theory, depending on the system and size of microcalcifications, between 25% and over 70% of contrast resolution is lost due to scatter. The results in NB-bCT, however, provide evidence that by removing scatter build-up in projections, the contrast of microcalcifications in a bCT image is preserved, regardless of their size or location in the breast.

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.

PurposeCompare conspicuity of ductal carcinoma in-situ (DCIS) to benign calcifications on unenhanced (bCT), contrast-enhanced dedicated breast CT (CEbCT) and mammography (DM).Methods and materialsThe institutional review board approved this HIPAA-compliant study. 42 women with Breast Imaging Reporting and Data System 4 or 5 category micro-calcifications had breast CT before biopsy. Three subjects with invasive disease at surgery were excluded. Two breast radiologists independently compared lesion conspicuity scores (CS) for CEbCT, to bCT and DM. Enhancement was measured in Hounsfield units (HU). Mean CS ± standard deviations are shown. Receiver operating characteristic analysis (ROC) measured radiologists' discrimination performance by comparing CS to enhancement alone. Statistical measurements were made using ANOVA F-test, Wilcoxon rank-sum test and robust linear regression analyses.Results39 lesions (17 DCIS, 22 benign) were analyzed. DCIS (8.5 ± 0.9, n=17) was more conspicuous than benign micro-calcifications (3.6 ± 2.9, n=22; p

With the rapid development of intelligent diagnostic medical imaging, artificial intelligent methods have become a research hotspot in this field with the rapid development of medical imaging intelligent diagnosis. The new research progress of artificial intelligence in medical image processing in recent five years is discussed. This paper discussed the function of medical image mining in computer-aided diagnoses. The idea of using luminance grade as the features of CT image and the method of extracting and storing the luminance grade are proposed. The results of classification based on decision tree and clustering based on density to some brain and breast CT images and the analysis to them are introduced. Moreover, the conclusion and future work are given.

In previous work, we generated computational breast phantoms by using a principal component analysis (PCA) or "Eigenbreast" technique. For this study, we sought to address resolution limitations in the previous synthesized breast phantoms by analyzing new human subject data set with higher resolution. We utilize PCA to sample input breast cases, then by using weighted sums along the different eigenvectors or "eigenbreasts," a number of new cases can be generated. While breasts can vary in structure and form, we used a series of compressed breasts derived from human subject breast CT volumes to create the eigenbreasts. We used an initial set of thirty-five phantoms from a new CT patient population with 155x155x155 μm3 voxel size. The training set and synthetized phantoms were evaluated by power law exponent β and changes in volumetric breast density as a result of the PCA process. The synthetic phantoms were found to have similar β and fibroglandular density distributions to the training dataset. Individual synthetic phantoms appeared to capture glandular features present in the training phantoms but had visually different texture features. This work shows that earlier work on the eigenbreast technique can be extended to newer datasets with higher resolution and produce synthetic phantoms that retain the quantitative properties of training data.

The breast phantoms currently available for evaluating full field digital mammography (FFDM), digital breast tomosynthesis (DBT), and breast CT (bCT) systems often lack the complexity present in real breasts. In this work we present a new methodology for creating physical anthropomorphic breast phantoms for use in FFDM, DBT, and dedicated bCT systems using zinc acetate-doped ink. First, an uncompressed virtual phantom was created through analytical modeling. The model represented a breast with 28% fibroglandular density with 13 tissue classes and contained a 5 mm lesion. The breast was binarized to two tissue classes: adipose and fibroglandular tissue. The phantom was then realized through inkjet printing using dye ink doped with zinc acetate for the fibroglandular components and three candidate materials for the adipose background: parchment paper, organic paper, and office paper. The fabrication process was evaluated in terms of material realism and reproducibility using spectroscopy, a clinical FFDM system, and a benchtop bCT system. The linear attenuation coefficient of the doped ink plus parchment paper and parchment paper alone closely matched those of the fibroglandular and adipose tissues, respectively. A methodology for generating anthropomorphic breast phantoms was developed using a novel inkjet printing technique for use in FFDM/DBT, as well as dedicated breast CT systems. A novel uncompressed breast phantom for bCT was fabricated using inexpensive, easily available materials with realistic tissue properties.

Purpose: In cone-beam computed tomography dedicated to the breast (BCT), the mean glandular dose (MGD) is the dose metric of reference, evaluated from the measured air kerma by means of normalized glandular dose coefficients (DgNCT). This work aimed at computing, for a simple breast model, a set of DgNCT values for monoenergetic and polyenergetic X-ray beams, and at validating the results vs. those for patient specific digital phantoms from BCT scans. Methods: We developed a Monte Carlo code for calculation of monoenergetic DgNCT coefficients (energy range 4.25–82.25 keV). The pendant breast was modelled as a cylinder of a homogeneous mixture of adipose and glandular tissue with glandular fractions by mass of 0.1%, 14.3%, 25%, 50% or 100%, enveloped by a 1.45 mmthick skin layer. The breast diameter ranged between 8 cm and 18 cm. Then, polyenergetic DgNCT coefficients were analytically derived for 49-kVp W-anode spectra (half value layer 1.25–1.50mm Al), as in a commercial BCT scanner. We compared the homogeneous models to 20 digital phantoms produced from classified 3D breast images. Results: Polyenergetic DgNCT resulted 13% lower than most recent published data. The comparison vs. patient specific breast phantoms showed that the homogeneous cylindrical model leads to a DgNCT percentage difference between −15% and +27%, with an average overestimation of 8%. Conclusions: A dataset of monoenergetic and polyenergetic DgNCT coefficients for BCT was provided. Patient specific breast models showed a different volume distribution of glandular dose and determined a DgNCT 8% lower, on average, than homogeneous breast model.

We proposed the neutrosophic approach for segmenting breast lesions in breast computed tomography (bCT) images. The neutrosophic set considers the nature and properties of neutrality (or indeterminacy). We considered the image noise as an indeterminate component while treating the breast lesion and other breast areas as true and false components. We iteratively smoothed and contrast-enhanced the image to reduce the noise level of the true set. We then applied one existing algorithm for bCT images, the RGI segmentation, on the resulting noise-reduced image to segment the breast lesions. We compared the segmentation performance of the proposed method (named as NS-RGI) to that of the regular RGI segmentation. We used 122 breast lesions (44 benign and 78 malignant) of 111 noncontrast enhanced bCT cases. We measured the segmentation performances of the NS-RGI and the RGI using the Dice coefficient. The average Dice values of the NS-RGI and RGI were 0.82 and 0.80, respectively, and their difference was statistically significant ([Formula: see text]). We conducted a subsequent feature analysis on the resulting segmentations. The classifier performance for the NS-RGI ([Formula: see text]) improved over that of the RGI ([Formula: see text] , [Formula: see text]).

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.

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 (pDgN{sub hetero}) and homogeneous (pDgN{sub homo}) 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 pDgN{sub hetero} coefficients were on average 35.3% (SD, 4.1)more » and 24.2% (SD, 3.0) lower than the pDgN{sub homo} 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 pDgN{sub hetero} relative to pDgN{sub homo} 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 pDgN{sub hetero} 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 pDgN{sub hetero} 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.« less

In definierten Altersgruppen ist die Mammografie sowohl in der Klinik als auch beim Screening ein akzeptables Verfahren. Ihre Einschrankungen liegen – auser der Strahlenbelastung – im Nachweis nicht kalzifizierter Karzinome bei Frauen mit hoher Dichte des Drusengewebes. Die Tomosynthese, die digitale Kontrastmittelmammografie und die Brust-CT (mit oder ohne Kontrastmittel) fuhren zu besseren Ergebnissen. Insbesondere die Applikation von Kontrastmittel zur Darstellung der Tumorangiogenese ist allen anderen bildgebenden Verfahren ohne Kontrastmittel uberlegen. Trotzdem werden die ausgezeichneten Ergebnisse der Brust-MRT wahrscheinlich mit keinem dieser Verfahren zu erreichen sein.

Purpose To investigate the dose saving potential of direct-converting CdTe photon-counting detector technology for dedicated breast CT. Materials and methods We analyzed the modulation transfer function (MTF), the noise power spectrum (NPS) and the detective quantum efficiency (DQE) of two detector technologies, suitable for breast CT (BCT): a flat-panel energy-integrating detector with a 70 μm and a 208 μm thick gadolinium oxysulfide (GOS) and a 150 μm thick cesium iodide (CsI) scintillator and a photon-counting detector with a 1000 μm thick CdTe sensor. Results The measurements for GOS scintillator thicknesses of 70 μm and 208 μm delivered 10% pre-sampled MTF values of 6.6 mm−1 and 3.2 mm−1, and DQE(0) values of 23% and 61%. The 10% pre-sampled MTF value for the 150 μm thick CsI scintillator 6.9 mm−1, and the DQE(0) value was 49%. The CdTe sensor reached a 10% pre-sampled MTF value of 8.5 mm−1 and a DQE(0) value of 85%. Conclusion The photon-counting CdTe detector technology allows for significant dose reduction compared to the energy-integrating scintillation detector technology used in BCT today. Our comparative evaluation indicates that a high potential dose saving may be possible for BCT by using CdTe detectors, without loss of spatial resolution.

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.

PURPOSE: Many computer aided diagnosis (CADx) tools for breast cancer begin by fully or semi automatically 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.

Purpose To design volume-specific breast phantoms from breast CT (bCT) data sets and estimate the associated normalized mean glandular dose coefficients for breast CT using Monte Carlo methods. Methods A large cohort of bCT data sets (N=215) was used to evaluate breast volume into quintiles (plus the top 5%). The average radius profile was then determined for each of the six volume-specific groups and used to both fabricate physical phantoms and generate mathematical phantoms (V1-V6; “V” denotes classification by volume). The MCNP6 Monte Carlo code was used to model a prototype bCT system fabricated at our institution; and this model was validated against physical measurements in the fabricated phantoms. The mathematical phantoms were used to simulate normalized mean glandular dose coefficients for both monoenergetic source photons “DgNCT(E)” (8–70 keV in 1 keV intervals) and polyenergetic x-ray beams “pDgNCT” (35–70 kV in 1 kV intervals). The Monte Carlo code was used to study the influence of breast size (V1 vs. V5) and glandular fraction (6.4% vs. 45.8%) on glandular dose. The pDgNCT coefficients estimated for the V1, V3, and V5 phantoms were also compared to those generated using simple, cylindrical phantoms with equivalent volume and two geometrical constraints including; (1) cylinder radius determined at the breast phantom chest wall “Rcw”; and (2) cylinder radius determined at the breast phantom center-of-mass “RCOM”. Results Satisfactory agreement was observed for dose estimations using MCNP6 compared with both physical measurements in the V1, V3, and V5 phantoms (R2 = 0.995) and reference bCT dose coefficients using simple phantoms (R2 = 0.999). For a 49 kV spectrum with 1.5 mm Al filtration, differences in glandular fraction (6.5% (5th percentile) vs. 45.8% (95th percentile)) had a 13.2% influence on pDgNCT for the V3 phantom, and differences in breast size (V1 vs. V5) had a 16.6% influence on pDgNCT for a breast composed of 17% (median) fibroglandular tissue. For cylindrical phantoms with a radius of RCOM the differences were 1.5%, 0.1%, and 2.1% compared with the V1, V3, and V5 phantoms, respectively. Conclusion Breast phantoms were designed using a large cohort of bCT data sets across a range of six breast sizes. These phantoms were then fabricated and used for the estimation of glandular dose in breast CT. The mathematical phantoms and associated glandular dose coefficients for a range of breast sizes (V1 – V6) and glandular fractions (5th to 95th percentiles) are available for interested users. This article is protected by copyright. All rights reserved.

Dedicated breast computed tomography (BCT) is an emerging clinical modality that can eliminate tissue superposition and has the potential for improved sensitivity and specificity for breast cancer detection and diagnosis. It is performed without physical compression of the breast. Most of the dedicated BCT systems use large-area detectors operating in cone-beam geometry and are referred to as cone-beam breast CT (CBBCT) systems. The large-area detectors in CBBCT systems are energy-integrating, indirect-type detectors employing a scintillator that converts x-ray photons to light, followed by detection of optical photons. A key consideration that determines the image quality achieved by such CBBCT systems is the choice of scintillator and its performance characteristics. In this work, a framework for analyzing the impact of the scintillator on CBBCT performance and its use for task-specific optimization of CBBCT imaging performance is described.

Contains fulltext : 181906.pdf (Publisher’s version ) (Open Access) The aim of this work is the evaluation of the software BreastSimulator, a breast X-ray imaging simulation software as a tool for the creation of 3D uncompressed breast digital models and for the simulation and the optimization of computed tomographic (CT) scanners dedicated to the breast. Eight 3D digital breast phantoms were created with glandular fraction in the range 10% to 68%. The models are characterised by different sizes and modelled realistic anatomical features.. X-ray CT projections were simulated for a dedicated cone-beam CT scanner and reconstructed with the FDK algorithm. X-ray projection images were simulated for 5 mono-energetic (27, 32, 35, 43 and 51 keV) and 3 poly-energetic X-ray spectra typically employed in current CT scanners dedicated to the breast (49, 60, or 80 kVp). Clinical BCT images acquired from two different clinical breast CT scanners were used for comparison purposes. The quantitative evaluation included calculation of the power-law exponent, beta, from simulated and real breast tomograms, based on the Noise Power Spectrum (NPS) fitted with a function S(f) = alpha/fbeta. The breast models were validated by comparison against clinical breast CT and published data. The calculated beta coefficients are close to that of clinical CT data from a dedicated breast CT scanner and reported data in the literature.In this paper we evaluated the software package BreastSimulator to generate breast models suitable for use with breast CT imaging The breast phantoms produced with the software tool can reproduce the anatomical structure of real breasts, as evaluated by calculating the beta exponent from the power spectral analysis of simulated images. As such, this research tool will contribute considerably to the further development, testing and optimisation of breast CT imaging technique.

Conventional mammographic dosimetry has been developed over the past 40years. 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 1.5mm, instead of the 4 or 5mm 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.

Purpose: To develop and validate algorithms for extraction and insertion of three‐dimensional (3D) profiles of tumor masses in cone beam breast CT (CBBCT) images to create simulated abnormality for evaluation with observer performance study. Methods: A bench‐top experimental CBBCT scanner was constructed and used to image mastectomy breast specimens with IRB approval. 5 sets of CBBCT images with confirmed tumor masses and 2 sets of normal CBBCT images were selected and used for this study. All CBBCT images were first corrected for cupping artifacts. The corrected images were then processed to reduce their noise levels and form the denoised images. The corrected and denoised CBBCT images for normal breasts were then segmented into adipose and glandular voxels. The images for abnormal breasts were reviewed by mammographers with the help of clinical images and reports to delineate the tumor masses and form 3D tumor maps. Using these maps, the 3D tumor profiles were rescaled to average glandular signals around the insertion locations in normal images. The boundaries of the 3D rescaled tumor profiles were smoothed to avoid sharp edge. The resultant 3D tumor profiles were inserted onto cupping artifact corrected normal CBBCT images by replacing the signals in adipose voxels near the insertion locations. The cupping artifacts were then added back to generate the simulated abnormal CBBCT images. These images were then visually compared with actual abnormal images for their degree of realism. Results: Based on visual comparison, the simulated abnormal CBBCT images showed no significant difference in realism from actual abnormal CBBCT images.Conclusions: We have successfully demonstrated the technique to extract 3D tumor profiles from abnormal CBBCT images and insert them onto normal CBBCT images to form simulated abnormal images for use in observer performance study. This work was supported in part by grants CA104759, CA13852 and CA124585 from the NIH‐NCI, a research grant EB00117 from the NIH‐NIBIB, and a subcontract from NIST‐ATP.

Computer simulation of breast anatomy is an essential component of Virtual Clinical Trials, a preclinical approach to validate breast imaging systems. Realism of breast phantoms affects simulation studies and their acceptance among researchers. Previously, we developed a simulation of tissue compartments defined by the hierarchy of Cooper’s ligaments, based upon recursive partitioning using octrees. In this work, we optimize the simulation parameters to represent realistically the breast subcutaneous and retromammary tissue regions. As seen in clinical images, the subcutaneous and retromammary regions contain predominantly adipose tissue organized into relatively large compartments, as opposed to the predominantly glandular breast interior. To mimic such organization, we divided the phantom volume into “subcutaneous”, “retromammary”, and “interior” regions. Within each region, parameters controlling the size and orientation of tissue compartments were selected separately. In this preliminary study, we varied parameter values and calculated the corresponding average compartment volume in each region. The proposed method was evaluated using anatomic descriptors at both radiological and pathological spatial scales. We simulated the subcutaneous region as spanning 20% of the breast diameter, comparable to published analysis of breast CT images. We simulated tissue compartments with the average volume of 0.94 cm 3 , 0.89 cm 3 and 0.31 cm 3 in the subcutaneous, retromammary and interior regions, respectively. Those average volumes match within 12% the values reported from histological analysis. Future evaluation will include a comparison of simulated and clinical parenchymal descriptors. The proposed method will be extended to automate the parameter optimization, and simulate detailed spatial variation, to further improve the realism.

To evaluate the impact of breast density on the diagnostic accuracy of non-contrast cone-beam breast computed tomography (CBBCT) in comparison to mammography for the detection of breast masses. A retrospective study was conducted from August 2015 to July 2016. Fifty-nine patients (65 breasts, 112 lesions) with BI-RADS, 5th edition 4 or 5 assessment in mammography and/or ultrasound of the breast received an additional non-contrast CBBCT. Independent double blind reading by two radiologists was performed for mammography and CBBCT imaging. Sensitivity, specificity and AUC were compared between the modalities. Breast lesions were histologically examined in 85 of 112 lesions (76%). The overall sensitivity for CBBCT (reader 1: 91%, reader 2: 88%) was higher than in mammography (both: 68%, p