Breast CT

Develop and Validate a Nomogram Combining Contrast-Enhanced Spectral Mammography Deep Learning with Clinical-Pathological Features to Predict Neoadjuvant Chemotherapy Response in Patients with ER-Positive/HER2-Negative Breast Cancer

RATIONALE AND OBJECTIVES

To develop and validate a nomogram that combines contrast-enhanced spectral mammography (CESM) deep learning with clinical-pathological features to predict neoadjuvant chemotherapy (NAC) response (either low Miller Payne (MP-L) grades 1-2 or high MP (MP-H) grades 3-5) in patients with ER-positive/HER2-negative breast cancer.

MATERIALS AND METHODS

In this retrospective study, 265 breast cancer patients were randomly allocated into training and test sets (used for models training and testing, respectively) at a 4:1 ratio. Deep learning models, based on the pre-trained ResNet34 model and initially fine-tuned for identifying breast cancer, were trained using low-energy and subtracted CESM images. The predicted results served as deep learning features for the deep learning-based model. Clinical-pathological features, including age, progesterone receptor (PR) status, estrogen receptor (ER) status, Ki67 expression levels, and neutrophil-to-lymphocyte ratio, were used for the clinical model. All these features contributed to the nomogram. Feature selection was performed through univariate analysis. Logistic regression models were developed and chosen using a stepwise selection method. The deep learning-based and clinical models, along with the nomogram, were evaluated using precision-recall curves, receiver operating characteristic (ROC) curves, specificity, recall, accuracy, negative predictive value, positive predictive value (PPV), balanced accuracy, F1-score, and decision curve analysis (DCA).

RESULTS

The nomogram demonstrated considerable predictive ability, with higher area under the ROC curve (0.95, P < 0.05), accuracy (0.94), specificity (0.98), PPV (0.89), and precision (0.89) compared to the deep learning-based and clinical models. In DCA, the nomogram showed substantial clinical value in assisting breast cancer treatment decisions, exhibiting a higher net benefit than the other models.

CONCLUSION

The nomogram, integrating CESM deep learning with clinical-pathological features, proved valuable for predicting NAC response in patients with ER-positive/HER2-negative breast cancer. Nomogram outperformed deep learning-based and clinical models.

Radiology of fibrosis. Part I: Thoracic organs

Sustained injury from factors such as hypoxia, infection, or physical damage may provoke improper tissue repair and the anomalous deposition of connective tissue that causes fibrosis. This phenomenon may take place in any organ, ultimately leading to their dysfunction and eventual failure. Tissue fibrosis has also been found to be central in both the process of carcinogenesis and cancer progression. Thus, its prompt diagnosis and regular monitoring is necessary for implementing effective disease-modifying interventions aiming to reduce mortality and improve overall quality of life. While significant research has been conducted on these subjects, a comprehensive understanding of how their relationship manifests through modern imaging techniques remains to be established. This work intends to provide a comprehensive overview of imaging technologies relevant to the detection of fibrosis affecting thoracic organs as well as to explore potential future advancements in this field.

Complete, Fully Automatic Detection and Classification of Benign and Malignant Breast Tumors Based on CT Images Using Artificial Intelligent and Image Processing

Breast cancer is the most common type of cancer in women, and early detection is important to significantly reduce its mortality rate. This study introduces a detection and diagnosis system that automatically detects and classifies breast tumors in CT scan images. First, the contours of the chest wall are extracted from computed chest tomography images, and two-dimensional image characteristics and three-dimensional image features, together with the application of active contours without edge and geodesic active contours methods, are used to detect, locate, and circle the tumor. Then, the computer-assisted diagnostic system extracts features, quantifying and classifying benign and malignant breast tumors using a greedy algorithm and a support vector machine. The study used 174 breast tumors for experiment and training and performed cross-validation 10 times (k-fold cross-validation) to evaluate performance of the system. The accuracy, sensitivity, specificity, and positive and negative predictive values of the system were 99.43%, 98.82%, 100%, 100%, and 98.89% respectively. This system supports the rapid extraction and classification of breast tumors as either benign or malignant, helping physicians to improve clinical diagnosis.

Dedicated breast CT: state of the art-Part I. Historical evolution and technical aspects

Dedicated breast CT is an emerging 3D isotropic imaging technology for breast, which overcomes the limitations of 2D compression mammography and limited angle tomosynthesis while providing some of the advantages of magnetic resonance imaging. This first installment in a 2-part review describes the evolution of dedicated breast CT beginning with a historical perspective and progressing to the present day. Moreover, it provides an overview of state-of-the-art technology. Particular emphasis is placed on technical limitations in scan protocol, radiation dose, breast coverage, patient comfort, and image artifact. Proposed methods of how to address these technical challenges are also discussed. KEY POINTS: • Advantages of breast CT include no tissue overlap, improved patient comfort, rapid acquisition, and concurrent assessment of microcalcifications and contrast enhancement. • Current clinical and prototype dedicated breast CT systems differ in acquisition modes, imaging techniques, and detector types. • There are still details to be decided regarding breast CT techniques, such as scan protocol, radiation dose, breast coverage, patient comfort, and image artifact.

Hybrid Optimization Method (HOM) Reconstruction with limited angle in Dual Energy Breast CT

Limited angle Breast Computed Tomography uses lower energy and low projection angle to detect early breast cancer or other malignant tissues in the breast. The sensitivity of breast CT can be improved by applying dual energy technology. The general challenge which hampers full exploration of dual energy imaging is noise accumulation as a result of spectral overlaps from two different images. The author proposed hybrid optimization method (HOM) which leverages on fast convergence of simultaneous algebraic reconstruction techniques (SART) and good de-noising and artefacts removal of dictionary learning (DL) to minimizes noise in each image of dual energy and then apply decomposition on the noiseless dual data. The HOM algorithm is formulated as optimization problem which find good atoms from the dictionary obtained and dictionary atom are learned from training data set. The reconstructed images which are noise-free are then decomposed using DECT algorithm into two material basis. 2D phantom known as mbat-phantom consisting of two material basis (microcalcification and normal breast tissue) were simulated to test the algorithm. Noisy projection data were also simulated under the same condition by adding poison noise. The performance of the method was evaluated by estimating some image quality indices on reconstructed images and decomposed images. The proposed method shows the highest average structural similarity index map (SSIM) of 0.9987 and 0.9921 and peak signal to-noise ratio (PSNR) of 49.24 and 46.96 for reconstructed image without noise and noisy image respectively. Also, there is a reduction in average standard deviation (STD) error of decomposed image. Our method performs excellently in streak artefact removal and noise suppression which is capable of reconstructing faithful image in presence of noisy data.

Material decomposition for simulated dual-energy breast computed tomography via hybrid optimization method

BACKGROUND

Dual-energy breast CT reconstruction has a potential application that includes separation of microcalcification from healthy breast tissue for assisting early breast cancer detection.

OBJECTIVE

To investigate and validate the noise suppression algorithm applied in the decomposition of the simulated breast phantom into microcalcification and healthy breast.

METHODS

The proposed hybrid optimization method (HOM) uses a simultaneous algebraic reconstruction technique (SART) output as a prior image, which is then incorporated into the self-adaptive dictionary learning. This self-adaptive dictionary learning seeks each group of patches to faithfully represent the learned dictionary, and the sparsity and non-local similarity of group patches are used to enforce the image regularization term of the prior image. We simulate a numerical phantom by adding different levels of Gaussian noise to test performance of the proposed method.

RESULTS

The mean value of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root mean square error (RMSE) for the proposed method are (49.043±1.571), (0.997±0.002), (0.003±0.001) and (51.329±1.998), (0.998±0.002), (0.003±0.001) for 35 kVp and 49 kVp, respectively. The PSNR of the proposed method shows greater improvement over TWIST (5.2%), SART (34.6%), FBP (40.4%) and TWIST (3.7%), SART (39.9%), FBP (50.3%) for 35 kVp and 49 kVp energy images, respectively. For the proposed method, the signal-to-noise ratio (SNR) of decomposed normal breast tissue (NBT) is (22.036±1.535), which exceeded that of TWIST, SART, and FBP by 7.5%, 49.6%, and 96.4%, respectively. The results reveal that the proposed algorithm achieves the best performance in both reconstructed and decomposed images under different levels of noise and the performance is due to the high sparsity and good denoising ability of minimization exploited to solve the convex optimization problem.

CONCLUSIONS

This study demonstrates the potential of applying dual-energy reconstruction in breast CT to detect and separate clustered MCs from healthy breast tissues without noise amplification. Compared to other competing methods, the proposed algorithm achieves the best noise suppression performance for both reconstructed and decomposed images.

Deep Encoder-Decoder Adversarial Reconstruction(DEAR) Network for 3D CT from Few-View Data

X-ray computed tomography (CT) is widely used in clinical practice. The involved ionizingX-ray radiation, however, could increase cancer risk. Hence, the reduction of the radiation dosehas been an important topic in recent years. Few-view CT image reconstruction is one of the mainways to minimize radiation dose and potentially allow a stationary CT architecture. In this paper,we propose a deep encoder-decoder adversarial reconstruction (DEAR) network for 3D CT imagereconstruction from few-view data. Since the artifacts caused by few-view reconstruction appear in3D instead of 2D geometry, a 3D deep network has a great potential for improving the image qualityin a data driven fashion. More specifically, our proposed DEAR-3D network aims at reconstructing3D volume directly from clinical 3D spiral cone-beam image data. DEAR is validated on a publiclyavailable abdominal CT dataset prepared and authorized by Mayo Clinic. Compared with other2D deep learning methods, the proposed DEAR-3D network can utilize 3D information to producepromising reconstruction results.

Multi-Energy Computed Tomography Breast Imaging with Monte Carlo Simulations: Contrast-to-Noise-Based Image Weighting

CONTEXT

Photon-counting detectors and breast computed tomography imaging have been an active area of research. With these detectors, photons are assigned an equal weight and weighting schemes can be enabled. More weight can be assigned to lower energies, resulting in an increase in the contrast-to-noise ratio (CNR).

AIMS

The aim of this study is to develop and evaluate an energy weighting imaging technique to improve the CNR of simulated breast phantoms and to improve tumour detection.

MATERIALS AND METHODS

Breast phantoms consisting of adipose, glandular, malignant tissues and iodine contrast were constructed with BreastSimulator software. The phantoms were used in egs_cbct simulations for energies ranging between 20 and 65 keV from which multiple images were reconstructed. A new CNR-based image weighting method was proposed based on the CNR values obtained from the images. This method improves on previous methods and can be applied to complicated phantoms since no structural information is needed.

RESULTS

An increase in the CNR can be seen for lower energies. A sharp increase in the CNR is seen just above the K-edge for the phantoms with the iodine contrast. The CNR-based image weighting leads to a 68.47% (1.68-fold) increase in the CNR for the malignant tissue without iodine. For the malignant tissue with iodine contrast, the increase in the CNR was 96.14% (1.96-fold).

CONCLUSIONS

The new proposed CNR-based image weighting scheme is easy to implement and can be used for complicated phantoms with varying structures. A large increase in the CNR is seen with or without the use of iodine contrast.

A Single Simulation Platform for Hybrid Photoacoustic and RF-Acoustic Computed Tomography

In recent years, multimodal thermoacoustic imaging has demonstrated superior imaging quality compared to other emerging modalities. It provides functional and molecular information, arising due to electromagnetic absorption contrast, at ultrasonic resolution using inexpensive and non-ionizing imaging methods. The development of optical- as well as radio frequency (RF)-induced thermoacoustic imaging systems would benefit from reliable numerical simulations. To date, most numerical models use a combination of different software in order to model the hybrid thermoacoustic phenomenon. Here, we demonstrate the use of a single open source finite element software platform (ONELAB) for photo- and RF-acoustic computed tomography. The solutions of the optical diffusion equation, frequency domain Maxwell’s equations, and time-domain wave equation are used to solve the optical, electromagnetic, and acoustic propagation problems, respectively, in ONELAB. The results on a test homogeneous phantom and an approximate breast phantom confirm that ONELAB is a very effective software for both photo- and RF-acoustic simulations, and invaluable for developing new reconstruction algorithms and hardware systems.

Characterization of CT Hounsfield Units for 3D acquisition trajectories on a dedicated breast CT system

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.

Low-Dose Contrast-Enhanced Breast CT Using Spectral Shaping Filters: An Experimental Study

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.

Quantitative contrast-enhanced spectral mammography based on photon-counting detectors: A feasibility study

PURPOSE

To investigate the feasibility of accurate quantification of iodine mass thickness in contrast-enhanced spectral mammography.

MATERIALS AND METHODS

A computer simulation model was developed to evaluate the performance of a photon-counting spectral mammography system in the application of contrast-enhanced spectral mammography. A figure-of-merit (FOM), which was defined as the decomposed iodine signal-to-noise ratio (SNR) with respect to the square root of the mean glandular dose (MGD), was chosen to optimize the imaging parameters, in terms of beam energy, splitting energy, and prefiltrations for breasts of various thicknesses and densities. Experimental phantom studies were also performed using a beam energy of 40 kVp and a splitting energy of 34 keV with 3 mm Al prefiltration. A two-step calibration method was investigated to quantify the iodine mass thickness, and was validated using phantoms composed of a mixture of glandular and adipose materials, for various breast thicknesses and densities. Finally, the traditional dual-energy log-weighted subtraction method was also studied as a comparison. The measured iodine signal from both methods was compared to the known value to characterize the quantification accuracy and precision.

RESULTS

The optimal imaging parameters, which lead to the highest FOM, were found at a beam energy between 42 and 46 kVp with a splitting energy at 34 keV. The optimal tube voltage decreased as the breast thickness or the Al prefiltration increased. The proposed quantification method was able to measure iodine mass thickness on phantoms of various thicknesses and densities with high accuracy. The root-mean-square (RMS) error for cm-scale lesion phantoms was estimated to be 0.20 mg/cm2 . The precision of the technique, characterized by the standard deviation of the measurements, was estimated to be 0.18 mg/cm2 . The traditional weighted subtraction method also predicted a linear correlation between the measured signal and the known iodine mass thickness. However, the correlation slope and offset values were strongly dependent on the total breast thickness and density.

CONCLUSION

The results of this study suggest that iodine mass thickness for cm-scale lesions can be accurately quantified with contrast-enhanced spectral mammography. The quantitative information can potentially improve the differential power for malignancy.

Implementation and CT sampling characterization of a third-generation SPECT-CT system for dedicated breast imaging

Stand-alone cone beam computed tomography (CT) and single-photon emission computed tomography (SPECT) systems capable of complex acquisition trajectories have previously been developed for breast imaging. Fully three-dimensional (3-D) motions of SPECT systems provide views into the chest wall and throughout the entire volume. The polar tilting capability of the CBCT system has shown improvement in sampling close to the chest wall, while eliminating cone beam artifacts. Here, a single hybrid SPECT-CT system, with each individual modality capable of independently traversing complex trajectories around a common pendant breast volume, was developed. We present the practical implementation of this design and preliminary results of the CT system. The fully 3-D SPECT was nested inside the suspended CT gantry and oriented perpendicular to the CT source-detector pair. Both subsystems were positioned on a rotation stage, with the combined polar and azimuthal motions enabling spherical trajectories. Six trajectories were used for initial evaluation of the tilt capable CT system. The developed system can achieve polar tilt angles with a [Formula: see text] positioning error and no hysteresis. Initial imaging results demonstrate that additional off-axis projection views of various geometric resolution phantoms facilitate more complete sampling, more consistent attenuation value recovery, and markedly improved reconstructions. This system could have various applications in diagnostic or therapeutic breast imaging.

Investigation of x-ray spectra for iodinated contrast-enhanced dedicated breast CT

Screening for breast cancer with mammography has been very successful, resulting in part to a reduction of breast cancer mortality by approximately 39% since 1990. However, mammography still has limitations in performance, especially for women with dense breast tissue. Iodinated contrast-enhanced, dedicated breast CT (BCT) has been proposed to improve lesion analysis and the accuracy of diagnostic workup for patients suspected of having breast cancer. A mathematical analysis to explore the use of various x-ray filters for iodinated contrast-enhanced BCT is presented. To assess task-based performance, the ideal linear observer signal-to-noise ratio (SNR) is used as a figure-of-merit under the assumptions of a linear, shift-invariant imaging system. To estimate signal and noise propagation through the BCT detector, a parallel-cascade model was used. The lesion model was embedded into a structured background and included a realistic level of iodine uptake. SNR was computed for 84,000 different exposure settings by varying the kV setting, x-ray filter materials and thickness, breast size, and composition and radiation dose. It is shown that some x-ray filter material/thickness combinations can provide up to 75% improvement in the linear ideal observer SNR over a conventionally used x-ray filter for BCT. This improvement in SNR can be traded off for substantial reductions in mean glandular dose.

Bowtie filters for dedicated breast CT: theory and computational implementation

PURPOSE

To design bowtie filters with improved properties for dedicated breast CT to improve image quality and reduce dose to the patient.

METHODS

The authors present three different bowtie filters designed for a cylindrical 14-cm diameter phantom with a uniform composition of 40/60 breast tissue, which vary in their design objectives and performance improvements. 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. Bowtie design #3 eliminates 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. All three designs are obtained using analytical computational methods and linear attenuation coefficients. Thus, the designs do not take into account the effects of scatter. The authors considered this to be a reasonable approach to the filter design problem since the use of Monte Carlo methods would have been computationally intensive. The filter profiles for a cone-angle of 0° were used for the entire length of each filter because the differences between those profiles and the correct cone-beam profiles for the cone angles in our system are very small, and the constant profiles allowed construction of the filters with the facilities available to us. For evaluation of the filters, we used Monte Carlo simulation techniques and the full cone-beam geometry. Images were generated with and without each bowtie filter to analyze the effect on dose distribution, noise uniformity, and contrast-to-noise ratio (CNR) homogeneity. Line profiles through the reconstructed images generated from the simulated projection images were also used as validation for the filter designs.

RESULTS

Examples of the three designs are presented. Initial verification of performance of the designs was done using analytical computations of HVL, intensity, and effective attenuation coefficient behind the phantom as a function of fan-angle with a cone-angle of 0°. The performance of the designs depends only weakly on incident spectrum and tissue composition. For all designs, the dynamic range requirement on the detector was reduced compared to the no-bowtie-filter case. Further verification of the filter designs was achieved through analysis of reconstructed images from simulations. Simulation data also showed that the use of our bowtie filters can reduce peripheral dose to the breast by 61% and provide uniform noise and CNR distributions. The bowtie filter design concepts validated in this work were then used to create a computational realization of a 3D anthropomorphic bowtie filter capable of achieving a constant effective attenuation coefficient behind the entire field-of-view of an anthropomorphic breast phantom.

CONCLUSIONS

Three different bowtie filter designs that vary in performance improvements were described and evaluated using computational and simulation techniques. Results indicate that the designs are robust against variations in breast diameter, breast composition, and tube voltage, and that the use of these filters can reduce patient dose and improve image quality compared to the no-bowtie-filter case.

Three dimensional dose distribution comparison of simple and complex acquisition trajectories in dedicated breast CT

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 cm(3) 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.

X-ray scatter correction method for dedicated breast computed tomography: improvements and initial patient testing

A previously proposed x-ray scatter correction method for dedicated breast computed tomography was further developed and implemented so as to allow for initial patient testing. The method involves the acquisition of a complete second set of breast CT projections covering 360° with a perforated tungsten plate in the path of the x-ray beam. To make patient testing feasible, a wirelessly controlled electronic positioner for the tungsten plate was designed and added to a breast CT system. Other improvements to the algorithm were implemented, including automated exclusion of non-valid primary estimate points and the use of a different approximation method to estimate the full scatter signal. To evaluate the effectiveness of the algorithm, evaluation of the resulting image quality was performed with a breast phantom and with nine patient images. The improvements in the algorithm resulted in the avoidance of introduction of artifacts, especially at the object borders, which was an issue in the previous implementation in some cases. Both contrast, in terms of signal difference and signal difference-to-noise ratio were improved with the proposed method, as opposed to with the correction algorithm incorporated in the system, which does not recover contrast. Patient image evaluation also showed enhanced contrast, better cupping correction, and more consistent voxel values for the different tissues. The algorithm also reduces artifacts present in reconstructions of non-regularly shaped breasts. With the implemented hardware and software improvements, the proposed method can be reliably used during patient breast CT imaging, resulting in improvement of image quality, no introduction of artifacts, and in some cases reduction of artifacts already present. The impact of the algorithm on actual clinical performance for detection, diagnosis and other clinical tasks in breast imaging remains to be evaluated.

Screening for breast cancer with Breast-CT in a ProHTA simulation

Aims: The potential of dedicated Breast-CT is evaluated by simulating its impact onto the performance of the German breast cancer screening program. Attendance rates, cancer detection and economic implications are quantified. Methods: Based on a prospective health technology assessment approach, we simulated screening in different scenarios. Results: In the simulation, attendance rates increase from 54 to up to 72% due to reduced pain. Breast cancers will be detected earlier while nodal positives and distant recurrences decrease. Assuming no additional cost, cost savings of up to €55 million in one screening period are computed. Conclusion: The simulation indicates that earlier cancer detection, fewer unnecessary biopsies and less pain are potential benefits of Breast-CT resulting in cost savings and higher attendance.

Bowtie filters for dedicated breast CT: Analysis of bowtie filter material selection

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.

Breast tissue decomposition with spectral distortion correction: a postmortem study

PURPOSE

To investigate the feasibility of an accurate measurement of water, lipid, and protein composition of breast tissue using a photon-counting spectral computed tomography (CT) with spectral distortion corrections.

METHODS

Thirty-eight postmortem breasts were imaged with a cadmium-zinc-telluride-based photon-counting spectral CT system at 100 kV. The energy-resolving capability of the photon-counting detector was used to separate photons into low and high energy bins with a splitting energy of 42 keV. The estimated mean glandular dose for each breast ranged from 1.8 to 2.2 mGy. Two spectral distortion correction techniques were implemented, respectively, on the raw images to correct the nonlinear detector response due to pulse pileup and charge-sharing artifacts. Dual energy decomposition was then used to characterize each breast in terms of water, lipid, and protein content. In the meantime, the breasts were chemically decomposed into their respective water, lipid, and protein components to provide a gold standard for comparison with dual energy decomposition results.

RESULTS

The accuracy of the tissue compositional measurement with spectral CT was determined by comparing to the reference standard from chemical analysis. The averaged root-mean-square error in percentage composition was reduced from 15.5% to 2.8% after spectral distortion corrections.

CONCLUSIONS

The results indicate that spectral CT can be used to quantify the water, lipid, and protein content in breast tissue. The accuracy of the compositional analysis depends on the applied spectral distortion correction technique.

Task-based optimization of dedicated breast CT via Hotelling observer metrics

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.

Task-based measures of image quality and their relation to radiation dose and patient risk

The theory of task-based assessment of image quality is reviewed in the context of imaging with ionizing radiation, and objective figures of merit (FOMs) for image quality are summarized. The variation of the FOMs with the task, the observer and especially with the mean number of photons recorded in the image is discussed. Then various standard methods for specifying radiation dose are reviewed and related to the mean number of photons in the image and hence to image quality. Current knowledge of the relation between local radiation dose and the risk of various adverse effects is summarized, and some graphical depictions of the tradeoffs between image quality and risk are introduced. Then various dose-reduction strategies are discussed in terms of their effect on task-based measures of image quality.

Level set segmentation of breast masses in contrast-enhanced dedicated breast CT and evaluation of stopping criteria

Dedicated breast CT (bCT) produces high-resolution 3D tomographic images of the breast, fully resolving fibroglandular tissue structures within the breast and allowing for breast lesion detection and assessment in 3D. In order to enable quantitative analysis, such as volumetrics, automated lesion segmentation on bCT is highly desirable. In addition, accurate output from CAD (computer-aided detection/diagnosis) methods depends on sufficient segmentation of lesions. Thus, in this study, we present a 3D lesion segmentation method for breast masses in contrast-enhanced bCT images. The segmentation algorithm follows a two-step approach. First, 3D radial-gradient index segmentation is used to obtain a crude initial contour, which is then refined by a 3D level set-based active contour algorithm. The data set included contrast-enhanced bCT images from 33 patients containing 38 masses (25 malignant, 13 benign). The mass centers served as input to the algorithm. In this study, three criteria for stopping the contour evolution were compared, based on (1) the change of region volume, (2) the average intensity in the segmented region increase at each iteration, and (3) the rate of change of the average intensity inside and outside the segmented region. Lesion segmentation was evaluated by computing the overlap ratio between computer segmentations and manually drawn lesion outlines. For each lesion, the overlap ratio was averaged across coronal, sagittal, and axial planes. The average overlap ratios for the three stopping criteria ranged from 0.66 to 0.68 (dice coefficient of 0.80 to 0.81), indicating that the proposed segmentation procedure is promising for use in quantitative dedicated bCT analyses.

Impact of lesion segmentation metrics on computer-aided diagnosis/detection in breast computed tomography

Evaluation of segmentation algorithms usually involves comparisons of segmentations to gold-standard delineations without regard to the ultimate medical decision-making task. We compare two segmentation evaluations methods-a Dice similarity coefficient (DSC) evaluation and a diagnostic classification task-based evaluation method using lesions from breast computed tomography. In our investigation, we use results from two previously developed lesion-segmentation algorithms [a global active contour model (GAC) and a global with local aspects active contour model]. Although similar DSC values were obtained (0.80 versus 0.77), we show that the global + local active contour (GLAC) model, as compared with the GAC model, is able to yield significantly improved classification performance in terms of area under the receivers operating characteristic (ROC) curve in the task of distinguishing malignant from benign lesions. [Area under the [Formula: see text] compared to 0.63, [Formula: see text]]. This is mainly because the GLAC model yields better detailed information required in the calculation of morphological features. Based on our findings, we conclude that the DSC metric alone is not sufficient for evaluating segmentation lesions in computer-aided diagnosis tasks.

Investigation of iterative image reconstruction in low-dose breast CT

There is interest in developing computed tomography (CT) dedicated to breast-cancer imaging. Because breast tissues are radiation-sensitive, the total radiation exposure in a breast-CT scan is kept low, often comparable to a typical two-view mammography exam, thus resulting in a challenging low-dose-data-reconstruction problem. In recent years, evidence has been found that suggests that iterative reconstruction may yield images of improved quality from low-dose data. In this work, based upon the constrained image total-variation minimization program and its numerical solver, i.e., the adaptive steepest descent-projection onto the convex set (ASD-POCS), we investigate and evaluate iterative image reconstructions from low-dose breast-CT data of patients, with a focus on identifying and determining key reconstruction parameters, devising surrogate utility metrics for characterizing reconstruction quality, and tailoring the program and ASD-POCS to the specific reconstruction task under consideration. The ASD-POCS reconstructions appear to outperform the corresponding clinical FDK reconstructions, in terms of subjective visualization and surrogate utility metrics.

Segmentation of breast masses on dedicated breast computed tomography and three-dimensional breast ultrasound images

We present and evaluate a method for the three-dimensional (3-D) segmentation of breast masses on dedicated breast computed tomography (bCT) and automated 3-D breast ultrasound images. The segmentation method, refined from our previous segmentation method for masses on contrast-enhanced bCT, includes two steps: (1) initial contour estimation and (2) active contour-based segmentation to further evolve and refine the initial contour by adding a local energy term to the level-set equation. Segmentation performance was assessed in terms of Dice coefficients (DICE) for 129 lesions on noncontrast bCT, 38 lesions on contrast-enhanced bCT, and 98 lesions on 3-D breast ultrasound (US) images. For bCT, DICE values of 0.82 and 0.80 were obtained on contrast-enhanced and noncontrast images, respectively. The improvement in segmentation performance with respect to that of our previous method was statistically significant ( p = 0.002 ). Moreover, segmentation appeared robust with respect to the presence of glandular tissue. For 3-D breast US, the DICE value was 0.71. Hence, our method obtained promising results for both 3-D imaging modalities, laying a solid foundation for further quantitative image analysis and potential future expansion to other 3-D imaging modalities.

Investigation of energy weighting using an energy discriminating photon counting detector for breast CT

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.

Measurement of breast tissue composition with dual energy cone-beam computed tomography: a postmortem study

PURPOSE

To investigate the feasibility of a three-material compositional measurement of water, lipid, and protein content of breast tissue with dual kVp cone-beam computed tomography (CT) for diagnostic purposes.

METHODS

Simulations were performed on a flat panel-based computed tomography system with a dual kVp technique in order to guide the selection of experimental acquisition parameters. The expected errors induced by using the proposed calibration materials were also estimated by simulation. Twenty pairs of postmortem breast samples were imaged with a flat-panel based dual kVp cone-beam CT system, followed by image-based material decomposition using calibration data obtained from a three-material phantom consisting of water, vegetable oil, and polyoxymethylene plastic. The tissue samples were then chemically decomposed into their respective water, lipid, and protein contents after imaging to allow direct comparison with data from dual energy decomposition.

RESULTS

Guided by results from simulation, the beam energies for the dual kVp cone-beam CT system were selected to be 50 and 120 kVp with the mean glandular dose divided equally between each exposure. The simulation also suggested that the use of polyoxymethylene as the calibration material for the measurement of pure protein may introduce an error of -11.0%. However, the tissue decomposition experiments, which employed a calibration phantom made out of water, oil, and polyoxymethylene, exhibited strong correlation with data from the chemical analysis. The average root-mean-square percentage error for water, lipid, and protein contents was 3.58% as compared with chemical analysis.

CONCLUSIONS

The results of this study suggest that the water, lipid, and protein contents can be accurately measured using dual kVp cone-beam CT. The tissue compositional information may improve the sensitivity and specificity for breast cancer diagnosis.

Breast density quantification with cone-beam CT: a post-mortem study

Forty post-mortem breasts were imaged with a flat-panel based cone-beam x-ray CT system at 50 kVp. The feasibility of breast density quantification has been investigated using standard histogram thresholding and an automatic segmentation method based on the fuzzy c-means algorithm (FCM). The breasts were chemically decomposed into water, lipid, and protein immediately after image acquisition was completed. The per cent fibroglandular volume (%FGV) from chemical analysis was used as the gold standard for breast density comparison. Both image-based segmentation techniques showed good precision in breast density quantification with high linear coefficients between the right and left breast of each pair. When comparing with the gold standard using %FGV from chemical analysis, Pearson's r-values were estimated to be 0.983 and 0.968 for the FCM clustering and the histogram thresholding techniques, respectively. The standard error of the estimate was also reduced from 3.92% to 2.45% by applying the automatic clustering technique. The results of the postmortem study suggested that breast tissue can be characterized in terms of water, lipid and protein contents with high accuracy by using chemical analysis, which offers a gold standard for breast density studies comparing different techniques. In the investigated image segmentation techniques, the FCM algorithm had high precision and accuracy in breast density quantification. In comparison to conventional histogram thresholding, it was more efficient and reduced inter-observer variation.

Investigating the effect of characteristic x-rays in cadmium zinc telluride detectors under breast computerized tomography operating conditions

A number of research groups have been investigating the use of dedicated breast computerized tomography (CT). Preliminary results have been encouraging, suggesting an improved visualization of masses on breast CT as compared to conventional mammography. Nonetheless, there are many challenges to overcome before breast CT can become a routine clinical reality. One potential improvement over current breast CT prototypes would be the use of photon counting detectors with cadmium zinc telluride (CZT) (or CdTe) semiconductor material. These detectors can operate at room temperature and provide high detection efficiency and the capability of multi-energy imaging; however, one factor in particular that limits image quality is the emission of characteristic x-rays. In this study, the degradative effects of characteristic x-rays are examined when using a CZT detector under breast CT operating conditions. Monte Carlo simulation software was used to evaluate the effect of characteristic x-rays and the detector element size on spatial and spectral resolution for a CZT detector used under breast CT operating conditions. In particular, lower kVp spectra and thinner CZT thicknesses were studied than that typically used with CZT based conventional CT detectors. In addition, the effect of characteristic x-rays on the accuracy of material decomposition in spectral CT imaging was explored. It was observed that when imaging with 50-60 kVp spectra, the x-ray transmission through CZT was very low for all detector thicknesses studied (0.5-3.0 mm), thus retaining dose efficiency. As expected, characteristic x-ray escape from the detector element of x-ray interaction increased with decreasing detector element size, approaching a 50% escape fraction for a 100 μm size detector element. The detector point spread function was observed to have only minor degradation with detector element size greater than 200 μm and lower kV settings. Characteristic x-rays produced increasing distortion in the spectral response with decreasing detector element size. If not corrected for, this caused a large bias in estimating tissue density parameters for material decomposition. It was also observed that degradation of the spectral response due to characteristic x-rays caused worsening precision in the estimation of tissue density parameters. It was observed that characteristic x-rays do cause some degradation in the spatial and spectral resolution of thin CZT detectors operating under breast CT conditions. These degradations should be manageable with careful selection of the detector element size. Even with the observed spectral distortion from characteristic x-rays, it is still possible to correctly estimate tissue parameters for material decomposition using spectral CT if accurate modeling is used.

Generation of a suite of 3D computer-generated breast phantoms from a limited set of human subject data

PURPOSE

The authors previously reported on a three-dimensional computer-generated breast phantom, based on empirical human image data, including a realistic finite-element based compression model that was capable of simulating multimodality imaging data. The computerized breast phantoms are a hybrid of two phantom generation techniques, combining empirical breast CT (bCT) data with flexible computer graphics techniques. However, to date, these phantoms have been based on single human subjects. In this paper, the authors report on a new method to generate multiple phantoms, simulating additional subjects from the limited set of original dedicated breast CT data. The authors developed an image morphing technique to construct new phantoms by gradually transitioning between two human subject datasets, with the potential to generate hundreds of additional pseudoindependent phantoms from the limited bCT cases. The authors conducted a preliminary subjective assessment with a limited number of observers (n = 4) to illustrate how realistic the simulated images generated with the pseudoindependent phantoms appeared.

METHODS

Several mesh-based geometric transformations were developed to generate distorted breast datasets from the original human subject data. Segmented bCT data from two different human subjects were used as the "base" and "target" for morphing. Several combinations of transformations were applied to morph between the "base' and "target" datasets such as changing the breast shape, rotating the glandular data, and changing the distribution of the glandular tissue. Following the morphing, regions of skin and fat were assigned to the morphed dataset in order to appropriately assign mechanical properties during the compression simulation. The resulting morphed breast was compressed using a finite element algorithm and simulated mammograms were generated using techniques described previously. Sixty-two simulated mammograms, generated from morphing three human subject datasets, were used in a preliminary observer evaluation where four board certified breast radiologists with varying amounts of experience ranked the level of realism (from 1 = "fake" to 10 = "real") of the simulated images.

RESULTS

The morphing technique was able to successfully generate new and unique morphed datasets from the original human subject data. The radiologists evaluated the realism of simulated mammograms generated from the morphed and unmorphed human subject datasets and scored the realism with an average ranking of 5.87 ± 1.99, confirming that overall the phantom image datasets appeared more "real" than "fake." Moreover, there was not a significant difference (p > 0.1) between the realism of the unmorphed datasets (6.0 ± 1.95) compared to the morphed datasets (5.86 ± 1.99). Three of the four observers had overall average rankings of 6.89 ± 0.89, 6.9 ± 1.24, 6.76 ± 1.22, whereas the fourth observer ranked them noticeably lower at 2.94 ± 0.7.

CONCLUSIONS

This work presents a technique that can be used to generate a suite of realistic computerized breast phantoms from a limited number of human subjects. This suite of flexible breast phantoms can be used for multimodality imaging research to provide a known truth while concurrently producing realistic simulated imaging data.

Anatomical complexity in breast parenchyma and its implications for optimal breast imaging strategies

PURPOSE

The purpose of this investigation was to assess the anatomical noise in breast images using a mathematically derived parameter β as a surrogate for detection performance, across the same patient cohort but in different imaging modalities including mammography, tomosynthesis, and breast CT.

METHODS

Women who were scheduled for breast biopsy were approached for participation in this IRB and HIPPA-compliant investigation. A total of 23 women had all views of each modality and represent the cohort studied in this investigation. Image data sets across all modalities were analyzed using 1000 regions of interest per image data set, and the anatomical noise power spectrum, NPS(a)(f), was computed and averaged for each breast image data set. After windowing the total noise power spectrum NPS(t)(f) to a specific frequency range corresponding to anatomical noise, the power-law slope (β) of the NPS(a)(f) was computed where NPS(a)(f) = α f(-) (β).

RESULTS

The value of β was determined for breast CT data sets, and they were 1.75 (0.424), 1.83 (0.352), and 1.79 (0.397), for the coronal, sagittal, and axial views, respectively. For tomosynthesis, β was 3.06 (0.361) and 3.10 (0.315) for the craniocaudal (CC) and medial lateral oblique (MLO) views, respectively. For mammography, these values were 3.17 (0.226) and 3.30 (0.236), for the CC and MLO views, respectively. The values of β for breast CT were significantly different than those for tomosynthesis and mammography (p < 0.001, all 12 comparisons).

CONCLUSIONS

Based on the parameter β which is thought to describe anatomical noise in breast images, breast CT was shown to have a statistically significant lower β than mammography or tomosynthesis. It has been suggested in the literature that a lower β may correspond to increased cancer detection performance; however, this has yet to be demonstrated unequivocally.

Generation of voxelized breast phantoms from surgical mastectomy specimens

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 mm(3) 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.

Evaluation of phase-contrast CT of breast tissue at conventional X-ray sources - presentation of selected findings

BACKGROUND

Grating-based phase contrast computed tomography (PC-CT) at synchrotron radiation sources has been shown to provide improved visualization of breast tumors. However, broad clinical application of phase-contrast imaging will likely depend on transferring the technology to standard polychromatic X-ray sources. On the basis of selected findings, we demonstrate the potential of grating-based PC-CT using a conventional X-ray source.

MATERIALS AND METHODS

Grating-based PC-CT of two ex-vivo formalin fixed breast specimens containing lobular carcinoma was conducted using a Talbot Lau interferometer run at a polychromatic X-ray source of 40kVp. Phase-contrast and absorption-based 3D-datasets of both specimens were simultaneously recorded. Radiological images were manually matched with corresponding histological sections. The visualization of selected histological findings in phase contrast was compared to absorption contrast.

RESULTS

Grating-based PC-CT was able to depict the 3-dimensional structure of dilated ducts and high phase contrast was found as a correlate to thickened fibrous ductal walls. Differences in contrast between fibrous and less fibrous breast tissue were observed in phase- but not in absorption-contrast images. Furthermore, regions of low phase contrast correlated with the extension of compact tumor components.

CONCLUSIONS

On the basis of selected findings, we show that grating-based PC-CT at a polychromatic X-ray source provides complementary information to conventional absorption contrast; albeit at lower spatial resolution than synchrotron-based imaging.

Quantifying admissible undersampling for sparsity-exploiting iterative image reconstruction in X-ray CT

Iterative image reconstruction with sparsity-exploiting methods, such as total variation (TV) minimization, investigated in compressive sensing claim potentially large reductions in sampling requirements. Quantifying this claim for computed tomography (CT) is nontrivial, because both full sampling in the discrete-to-discrete imaging model and the reduction in sampling admitted by sparsity-exploiting methods are ill-defined. The present article proposes definitions of full sampling by introducing four sufficient-sampling conditions (SSCs). The SSCs are based on the condition number of the system matrix of a linear imaging model and address invertibility and stability. In the example application of breast CT, the SSCs are used as reference points of full sampling for quantifying the undersampling admitted by reconstruction through TV-minimization. In numerical simulations, factors affecting admissible undersampling are studied. Differences between few-view and few-detector bin reconstruction as well as a relation between object sparsity and admitted undersampling are quantified.

Quantification of breast density with spectral mammography based on a scanned multi-slit photon-counting detector: a feasibility study

A simple and accurate measurement of breast density is crucial for the understanding of its impact in breast cancer risk models. The feasibility to quantify volumetric breast density with a photon-counting spectral mammography system has been investigated using both computer simulations and physical phantom studies. A computer simulation model involved polyenergetic spectra from a tungsten anode x-ray tube and a Si-based photon-counting detector has been evaluated for breast density quantification. The figure-of-merit (FOM), which was defined as the signal-to-noise ratio of the dual energy image with respect to the square root of mean glandular dose, was chosen to optimize the imaging protocols, in terms of tube voltage and splitting energy. A scanning multi-slit photon-counting spectral mammography system has been employed in the experimental study to quantitatively measure breast density using dual energy decomposition with glandular and adipose equivalent phantoms of uniform thickness. Four different phantom studies were designed to evaluate the accuracy of the technique, each of which addressed one specific variable in the phantom configurations, including thickness, density, area and shape. In addition to the standard calibration fitting function used for dual energy decomposition, a modified fitting function has been proposed, which brought the tube voltages used in the imaging tasks as the third variable in dual energy decomposition. For an average sized 4.5 cm thick breast, the FOM was maximized with a tube voltage of 46 kVp and a splitting energy of 24 keV. To be consistent with the tube voltage used in current clinical screening exam (∼32 kVp), the optimal splitting energy was proposed to be 22 keV, which offered a FOM greater than 90% of the optimal value. In the experimental investigation, the root-mean-square (RMS) error in breast density quantification for all four phantom studies was estimated to be approximately 1.54% using standard calibration function. The results from the modified fitting function, which integrated the tube voltage as a variable in the calibration, indicated a RMS error of approximately 1.35% for all four studies. The results of the current study suggest that photon-counting spectral mammography systems may potentially be implemented for an accurate quantification of volumetric breast density, with an RMS error of less than 2%, using the proposed dual energy imaging technique.

Effect of slice thickness on detectability in breast CT using a prewhitened matched filter and simulated mass lesions

PURPOSE

Dedicated breast CT (bCT) is an emerging technology with the potential to improve the detection of breast cancer in screening and diagnostic capacities. Typically, the 3D volume reconstructed from the scanner is displayed as sectional images. The purpose of this study was to evaluate the effect of section thickness on the detectability of simulated masses using a prewhitened matched filter (PWMF) as a model observer.

METHODS

A breast CT scanner has been designed and fabricated in the authors' laboratory with more than 200 women imaged in IRB-approved phase I and phase II trials to date. Of these, 151 bilateral data sets were selected on the basis of low artifact content, sufficient breast coverage, and excluding cases with breast implants. BIRADS breast density ratings were available for 144 of these patients. Spherical mass lesions of diameter 1, 2, 3, 5, 11, and 15 mm were mathematically generated and embedded at random locations within the parenchymal region of each bCT volume. Microcalcifications were not simulated in this study. For each viewing plane (sagittal, axial, and coronal) and section thickness (ranging from 0.3 to 44 mm), section images of the breast parenchyma containing the lesion were generated from the reconstructed bCT data sets by averaging voxels over the length of the section. Using signal known exactly (SKE) model observer methodology, receiver operating characteristic (ROC) curve analysis was performed on each generated projected image using a PWMF based model observer. ROC curves were generated for each breast data set, and the area under the ROC curve (AUC) was evaluated as well as the sensitivity at 95% specificity.

RESULTS

For all lesion sizes, performance rises modestly to a peak before falling off substantially as section thickness increases over the range of the study. We find that the optimal section thickness tracks the size of the lesion to be detected linearly with a small positive offset and slopes ranging from 0.27 to 0.44. No significant differences were observed between left and right breasts. Performance measures are negatively correlated with measures of breast density, with an average correlation coefficient of -0.48 for the BIRADS breast density score and -0.81 for the proportion of glandular tissue in the breast interior.

CONCLUSIONS

This study shows quantitatively how PWMF detection performance of a known lesion size is influenced by section thickness in dedicated breast CT. While the optimal section thickness is tuned to the size of the lesion being detected, overall performance is more robust for thin section images compared to thicker images.

The emergence of diagnostic imaging technologies in breast cancer: discovery, regulatory approval, reimbursement, and adoption in clinical guidelines

In this article, we trace the chronology of developments in breast imaging technologies that are used for diagnosis and staging of breast cancer, including mammography, ultrasonography, magnetic resonance imaging, computed tomography, and positron emission tomography. We explore factors that affected clinical acceptance and utilization of these technologies from discovery to clinical use, including milestones in peer-reviewed publication, US Food and Drug Administration approval, reimbursement by payers, and adoption into clinical guidelines. The factors driving utilization of new imaging technologies are mainly driven by regulatory approval and reimbursement by payers rather than evidence that they provide benefits to patients. Comparative effectiveness research can serve as a useful tool to investigate whether these imaging modalities provide information that improves patient outcomes in real-world settings.

Development and characterization of an anthropomorphic breast software phantom based upon region-growing algorithm

PURPOSE

We present a novel algorithm for computer simulation of breast anatomy for generation of anthropomorphic software breast phantoms. A realistic breast simulation is necessary for preclinical validation of volumetric imaging modalities.

METHODS

The anthropomorphic software breast phantom simulates the skin, regions of adipose and fibroglandular tissue, and the matrix of Cooper's ligaments and adipose compartments. The adipose compartments are simulated using a seeded region-growing algorithm; compartments are grown from a set of seed points with specific orientation and growing speed. The resulting adipose compartments vary in shape and size similar to real breasts; the adipose region has a compact coverage by adipose compartments of various sizes, while the fibroglandular region has fewer, more widely separated adipose compartments. Simulation parameters can be selected to cover the breadth of variations in breast anatomy observed clinically.

RESULTS

When simulating breasts of the same glandularity with different numbers of adipose compartments, the average compartment volume was proportional to the phantom size and inversely proportional to the number of simulated compartments. The use of the software phantom in clinical image simulation is illustrated by synthetic digital breast tomosynthesis images of the phantom. The proposed phantom design was capable of simulating breasts of different size, glandularity, and adipose compartment distribution. The region-growing approach allowed us to simulate adipose compartments with various size and shape. Qualitatively, simulated x-ray projections of the phantoms, generated using the proposed algorithm, have a more realistic appearance compared to previous versions of the phantom.

CONCLUSIONS

A new algorithm for computer simulation of breast anatomy has been proposed that improved the realism of the anthropomorphic software breast phantom.

Evaluation of the absorbed dose to the breast using radiochromic film in a dedicated CT mammotomography system employing a quasi-monochromatic x-ray beam

PURPOSE

A dual modality SPECT-CT prototype system dedicated to uncompressed breast imaging (mammotomography) has been developed. The computed tomography subsystem incorporates an ultrathick K-edge filtration technique producing a quasi-monochromatic x-ray cone beam that optimizes the dose efficiency of the system for lesion imaging in an uncompressed breast. Here, the absorbed dose in various geometric phantoms and in an uncompressed and pendant cadaveric breast using a normal tomographic cone beam imaging protocol is characterized using both thermoluminescent dosimeter (TLD) measurements and ionization chamber-calibrated radiochromic film.

METHODS

Initially, two geometric phantoms and an anthropomorphic breast phantom are filled in turn with oil and water to simulate the dose to objects that mimic various breast shapes having effective density bounds of 100% fatty and glandular breast compositions, respectively. Ultimately, an excised human cadaver breast is tomographically scanned using the normal tomographic imaging protocol, and the dose to the breast tissue is evaluated and compared to the earlier phantom-based measurements.

RESULTS

Measured trends in dose distribution across all breast geometric and anthropomorphic phantom volumes indicate lower doses in the medial breast and more proximal to the chest wall, with consequently higher doses near the lateral peripheries and nipple regions. Measured doses to the oil-filled phantoms are consistently lower across all volume shapes due to the reduced mass energy-absorption coefficient of oil relative to water. The mean measured dose to the breast cadaver, composed of adipose and glandular tissues, was measured to be 4.2 mGy compared to a mean whole-breast dose of 3.8 and 4.5 mGy for the oil- and water-filled anthropomorphic breast phantoms, respectively.

CONCLUSIONS

Assuming rotational symmetry due to the tomographic acquisition exposures, these results characterize the 3D dose distributions in an uncompressed human breast tissue volume for this dedicated breast imaging device and illustrate advantages of using the novel ultrathick K-edge filtered beam to minimize the dose to the breast during fully-3D imaging.

Radiation doses in cone-beam breast computed tomography: a Monte Carlo simulation study

PURPOSE

In this article, we describe a method to estimate the spatial dose variation, average dose and mean glandular dose (MGD) for a real breast using Monte Carlo simulation based on cone beam breast computed tomography (CBBCT) images. We present and discuss the dose estimation results for 19 mastectomy breast specimens, 4 homogeneous breast models, 6 ellipsoidal phantoms, and 6 cylindrical phantoms.

METHODS

To validate the Monte Carlo method for dose estimation in CBBCT, we compared the Monte Carlo dose estimates with the thermoluminescent dosimeter measurements at various radial positions in two polycarbonate cylinders (11- and 15-cm in diameter). Cone-beam computed tomography (CBCT) images of 19 mastectomy breast specimens, obtained with a bench-top experimental scanner, were segmented and used to construct 19 structured breast models. Monte Carlo simulation of CBBCT with these models was performed and used to estimate the point doses, average doses, and mean glandular doses for unit open air exposure at the iso-center. Mass based glandularity values were computed and used to investigate their effects on the average doses as well as the mean glandular doses. Average doses for 4 homogeneous breast models were estimated and compared to those of the corresponding structured breast models to investigate the effect of tissue structures. Average doses for ellipsoidal and cylindrical digital phantoms of identical diameter and height were also estimated for various glandularity values and compared with those for the structured breast models.

RESULTS

The absorbed dose maps for structured breast models show that doses in the glandular tissue were higher than those in the nearby adipose tissue. Estimated average doses for the homogeneous breast models were almost identical to those for the structured breast models (p=1). Normalized average doses estimated for the ellipsoidal phantoms were similar to those for the structured breast models (root mean square (rms) percentage difference = 1.7%; p = 0.01), whereas those for the cylindrical phantoms were significantly lower (rms percentage difference = 7.7%; p < 0.01). Normalized MGDs were found to decrease with increasing glandularity.

CONCLUSIONS

Our results indicate that it is sufficient to use homogeneous breast models derived from CBCT generated structured breast models to estimate the average dose. This investigation also shows that ellipsoidal digital phantoms of similar dimensions (diameter and height) and glandularity to actual breasts may be used to represent a real breast to estimate the average breast dose with Monte Carlo simulation. We have also successfully demonstrated the use of structured breast models to estimate the true MGDs and shown that the normalized MGDs decreased with the glandularity as previously reported by other researchers for CBBCT or mammography.

Optical mammography: Diffuse optical imaging of breast cancer

Existing imaging modalities for breast cancer screening, diagnosis and therapy monitoring, namely X-ray mammography and magnetic resonance imaging, have been proven to have limitations. Diffuse optical imaging is a set of non-invasive imaging modalities that use near-infrared light, which can be an alternative, if not replacement, to those existing modalities. This review covers the background knowledge, recent clinical outcome, and future outlook of this newly emerging medical imaging modality.

Dedicated breast computed tomography: the optimal cross-sectional imaging solution?

Dedicated breast computed tomography (DBCT) is a burgeoning technology that has many advantages over current breast-imaging systems. Three-dimensional visualization of the breast mitigates the limiting effects of superimposition noted with mammography. Postprocessing capabilities will allow application of advanced technologies, such as creation of maximum-intensity projection and subtraction images, and the use of both computer-aided detection and possible computer-aided diagnosis algorithms. Excellent morphologic detail and soft tissue contrast can be achieved, due in part to the isotropic image data that DBCT produces. The expected cost should be more reasonable than magnetic resonance imaging. At present, because the breast is not compressed, patients find it more comfortable than mammography. Physiologic information can be obtained when intravenous contrast material is used and/or when DBCT is combined with single photon emission-computed tomography or positron emission tomography. DBCT provides an excellent platform for multimodality systems including integration with interventional and therapeutic procedures. With a slightly altered design, the DBCT platform may also be useful for external-beam radiation with image guidance.

Radiation dose reduction using a CdZnTe-based computed tomography system: comparison to flat-panel detectors

PURPOSE

Although x-ray projection mammography has been very effective in early detection of breast cancer, its utility is reduced in the detection of small lesions that are occult or in dense breasts. One drawback is that the inherent superposition of parenchymal structures makes visualization of small lesions difficult. Breast computed tomography using flat-panel detectors has been developed to address this limitation by producing three-dimensional data while at the same time providing more comfort to the patients by eliminating breast compression. Flat panels are charge integrating detectors and therefore lack energy resolution capability. Recent advances in solid state semiconductor x-ray detector materials and associated electronics allow the investigation of x-ray imaging systems that use a photon counting and energy discriminating detector, which is the subject of this article.

METHODS

A small field-of-view computed tomography (CT) system that uses CdZnTe (CZT) photon counting detector was compared to one that uses a flat-panel detector for different imaging tasks in breast imaging. The benefits afforded by the CZT detector in the energy weighting modes were investigated. Two types of energy weighting methods were studied: Projection based and image based. Simulation and phantom studies were performed with a 2.5 cm polymethyl methacrylate (PMMA) cylinder filled with iodine and calcium contrast objects. Simulation was also performed on a 10 cm breast specimen.

RESULTS

The contrast-to-noise ratio improvements as compared to flat-panel detectors were 1.30 and 1.28 (projection based) and 1.35 and 1.25 (image based) for iodine over PMMA and hydroxylapatite over PMMA, respectively. Corresponding simulation values were 1.81 and 1.48 (projection based) and 1.85 and 1.48 (image based). Dose reductions using the CZT detector were 52.05% and 49.45% for iodine and hydroxyapatite imaging, respectively. Image-based weighting was also found to have the least beam hardening effect.

CONCLUSIONS

The results showed that a CT system using an energy resolving detector reduces the dose to the patient while maintaining image quality for various breast imaging tasks.

An X-Ray computed tomography/positron emission tomography system designed specifically for breast imaging

Mammography has served the population of women who are at-risk for breast cancer well over the past 30 years. While mammography has undergone a number of changes as digital detector technology has advanced, other modalities such as computed tomography have experienced technological sophistication over this same time frame as well. The advent of large field of view flat panel detector systems enable the development of breast CT and several other niche CT applications, which rely on cone beam geometry. The breast, it turns out, is well suited to cone beam CT imaging because the lack of bones reduces artifacts, and the natural tapering of the breast anteriorly reduces the x-ray path lengths through the breast at large cone angle, reducing cone beam artifacts as well. We are in the process of designing a third prototype system which will enable the use of breast CT for image guided interventional procedures. This system will have several copies fabricated so that several breast CT scanners can be used in a multi-institutional clinical trial to better understand the role that this technology can bring to breast imaging.