Joint AAPM Task Group 282/EFOMP Working Group Report: Breast dosimetry for standard and contrast-enhanced mammography and breast tomosynthesis

Currently, there are multiple breast dosimetry estimation methods for mammography and its variants in use throughout the world. This fact alone introduces uncertainty, since it is often impossible to distinguish which model is internally used by a specific imaging system. In addition, all current models are hampered by various limitations, in terms of overly simplified models of the breast and its composition, as well as simplistic models of the imaging system. Many of these simplifications were necessary, for the most part, due to the need to limit the computational cost of obtaining the required dose conversion coefficients decades ago, when these models were first implemented. With the advancements in computational power, and to address most of the known limitations of previous breast dosimetry methods, a new breast dosimetry method, based on new breast models, has been developed, implemented, and tested. This model, developed jointly by the American Association of Physicists in Medicine and the European Federation for Organizations of Medical Physics, is applicable to standard mammography, digital breast tomosynthesis, and their contrast-enhanced variants. In addition, it includes models of the breast in both the cranio-caudal and the medio-lateral oblique views. Special emphasis was placed on the breast and system models used being based on evidence, either by analysis of large sets of patient data or by performing measurements on imaging devices from a range of manufacturers. Due to the vast number of dose conversion coefficients resulting from the developed model, and the relative complexity of the calculations needed to apply it, a software program has been made available for download or online use, free of charge, to apply the developed breast dosimetry method. The program is available for download or it can be used directly online. A separate User's Guide is provided with the software.

Task-based assessment of digital mammography microcalcification detection with deep learning denoising algorithmss using in silico and physical phantom studies

Purpose

Recent research suggests that image quality degradation with reduced radiation exposure in mammography can be mitigated by postprocessing mammograms with denoising algorithms based on convolutional neural networks. Breast microcalcifications, along with extended soft-tissue lesions, are the primary breast cancer biomarkers in a clinical x-ray examination, with the former being more sensitive to quantum noise. We test one such publicly available denoising method to observe if an improvement in detection of small microcalcifications can be achieved when deep learning-based denoising is applied to half-dose phantom scans.

Approach

An existing denoiser model (that was previously trained on clinical data) was applied to mammograms of an anthropomorphic physical phantom with hydroxyapatite microcalcifications. In addition, another model trained and tested using all synthetic (Monte Carlo) data was applied to a similar digital compressed breast phantom. Human reader studies were conducted to assess and compare image quality in a set of binary signal detection 4-AFC experiments, with proportion of correct responses used as a performance metric.

Results

In both physical phantom/clinical system and simulation studies, we saw no apparent improvement in small microcalcification signal detection in denoised half-dose mammograms. However, in a Monte Carlo study, we observed a noticeable jump in 4-AFC scores, when readers analyzed denoised half-dose images processed by the neural network trained on a dataset composed of 50% signal-present (SP) and 50% signal-absent regions of interest (ROIs).

Conclusions

Our findings conjecture that deep-learning denoising algorithms may benefit from enriching training datasets with SP ROIs, at least in cases with clusters of 5 to 10 microcalcifications, each of size ≲ 240    μ m .

Inclusion of a GaAs detector model in the Photon Counting Toolkit software for the study of breast imaging systems

We present an upgraded version of the Photon Counting Toolkit (PcTK), a freely available by request MATLAB tool for the simulation of semiconductor-based photon counting detectors (PCD), which has been extended and validated to account for gallium arsenide (GaAs)-based PCD(s). The modified PcTK version was validated by performing simulations and acquiring experimental data for three different cases. The LAMBDA 60 K module planar detector (X-Spectrum GmbH, Germany) based on the Medipix3 ASIC technology was used in all cases. This detector has a 500-μm thick GaAs sensor and a 256 × 256-pixel array with 55 μm pixel size. The first validation was a comparison between simulated and measured spectra from a 109Cd radionuclide source. In the second validation study, experimental measurements and simulations of mammography spectra were generated to observe the performance of the GaAs version of the PcTK with polychromatic radiation used in conventional x-ray imaging systems. The third validation study used single event analysis to validate the spatio-energetic model of the extended PcTK version. Overall, the software provided a good agreement between simulated and experimental data, validating the accuracy of the GaAs model. The software could be an attractive tool for accurate simulation of breast imaging modalities relying on photon counting detectors and therefore could assist in their characterization and optimization.

Classification of breast microcalcifications with GaAs photon-counting spectral mammography using an inverse problem approach

The purpose of this study was to investigate the use of a Gallium Arsenide (GaAs) photon-counting spectral mammography system to differentiate between Type I and Type II calcifications. Type I calcifications, consisting of calcium oxalate dihydrate (CO) or weddellite compounds are more often associated with benign lesions in the breast, and Type II calcifications containing hydroxyapatite (HA) are associated with both benign and malignant lesions in the breast. To be able to differentiate between these two calcification types, it is necessary to be able to estimate the full spectrum of the x-ray beam transmitted through the breast. We propose a novel method for estimating the energy-dependent x-ray transmission fraction of a beam using a photon counting detector with a limited number of energy bins. Using the estimated x-ray transmission through microcalcifications, it was observed that calcification type can be accurately estimated with machine learning. The study was carried out on a custom-built laboratory benchtop system using the SANTIS 0804 GaAs detector prototype system from DECTRIS Ltd with two energy thresholds enabled. Four energy thresholds detector was simulated by taking two separate acquisitions in which two energy thresholds were enabled for each acquisition and set at (12 keV, 21 keV) and then (29 keV, 36 keV). Measurements were performed using BR3D (CIRS, Norfolk, VA) breast imaging phantoms mimicking 100% adipose and 100% glandular tissues swirled together in an approximate 50/50 ratio by weight with the addition of in-house-developed synthetic microcalcifications. First, an inverse problem-based approach was used to estimate the full energy x-ray transmission fraction factor using known basis transmission factors from varying thicknesses of aluminum and polymethyl methacrylate (PMMA). Second, the classification of Type I and Type II calcifications was performed using the estimated energy-dependent transmission fraction factors for the pixels containing calcifications. The results were analyzed using receiver operating characteristic (ROC) analysis and demonstrated good discrimination performance with the area under the ROC curve greater than 84%. They indicated that GaAs photon-counting spectral mammography has potential use as a non-invasive method for discrimination between Type I and Type II calcifications. Results from this study suggested that GaAs-based spectral mammography could serve as a non-invasive measure for ruling out malignancy of calcifications found in the breast. Additional studies in more clinically realistic conditions involving breast tissues samples with smaller microcalcification specks should be performed to further explore the feasibility of this approach.

Theoretical comparison and optimization of cadmium telluride and gallium arsenide photon-counting detectors for contrast-enhanced spectral mammography

Purpose

Most photon-counting detectors (PCDs) being developed use cadmium telluride (CdTe), which has nonoptimal characteristic x-ray emission with energies in the range used for breast imaging. New PCD using a gallium arsenide (GaAs) has been developed. Since GaAs has characteristic x-rays lower in energy than those of CdTe, it is hypothesized that this new PCD might be beneficial for spectral x-ray breast imaging.

Approach

We performed simulations using realistic mammography x-ray spectra with both CdTe and GaAs PCDs. Five different experiments were conducted, each comparing the performance of CdTe and GaAs: (1) sensitivity of iodine quantification to charge cloud size and electronic noise, (2) effect of photon spectrum on iodine quantification, (3) effect of varying the number of energy bins, (4) a dose analysis to assess any possible dose reduction from using either detector, and (5) spectral performance of ideal CdTe and GaAs PCDs. For each study, 3 sets of 5000 noise realizations were used to calculate the Cramer-Rao lower bound (CRLB) of iodine quantification.

Results

For all spectra studied, GaAs gave a lower CRLB for iodine quantification, with 10 of the 12 spectra showing a statistically significant difference ( p ≤ 0.05 ). The photon energy spectrum that optimized iodine detection for both detector materials was the 40 kVp beam with 2-mm Al filtration, which produced CRLBs of 0.282    cm 2 and 0.257    cm 2 for CdTe and GaAs, respectively, when using five energy bins.

Conclusion

GaAs is a promising detector material for contrast-enhanced spectral mammography that offers better spectral performance than CdTe.

4D digital anthropomorphic breast phantom for iodinated contrast-enhanced imaging

Purpose

Differentiating between benign and malignant masses is one of the biggest challenges in breast imaging. The challenge is ingrained in the similarity of the attenuation coefficients between different types of lesion tissues and fibroglandular tissues. Contrast-enhanced imaging techniques can take advantage of the differing metabolism in different tissues, therefore, potentially allowing better differentiation of malignant and benign lesions. To facilitate the development and optimization of such technologies, we propose a fully digital 4D phantom that features time-varying enhancement patterns for different tissue types.

Approach

The 4D model is based on a static, anthropomorphic 3D digital breast phantom. Masses inserted into the 3D phantom are based on a previously published model. Physiological parameters that capture the key characteristics of masses, e.g., wash-in and wash-out rates indicating metabolic level, are employed in the model to simulate fundamental features for categorizing mass types. The two-compartmental model, a well-known model in the field of pharmacokinetics, is used to depict the diffusion process of the contrast agent. Two methods are proposed to allow for the simulations of lesions with necrotic cores of varying shapes and sizes.

Results

The fourth dimension of the phantom models different time-varying enhancement patterns for different materials including fibroglandular tissue and lesion tissue. Metabolic characteristics of mass models can be adjusted to provide different enhancement patterns. The parameters of the 4D phantom can also be adjusted to fit different scenarios. The usage of the phantom is demonstrated by simulating mammograms at different time frames.

Conclusion

A 4D digital anthropomorphic breast phantom that models different time-varying contrast enhancement patterns is presented. This phantom could be an integral tool for use in in silico trials to assess image quality of iodinated contrast-enhanced mammography, digital breast tomosynthesis, and breast computed tomography systems.

An empirical method for geometric calibration of a photon counting detector-based cone beam CT system

BACKGROUND

Geometric calibration is essential in developing a reliable computed tomography (CT) system. It involves estimating the geometry under which the angular projections are acquired. Geometric calibration of cone beam CTs employing small area detectors, such as currently available photon counting detectors (PCDs), is challenging when using traditional-based methods due to detectors' limited areas.

OBJECTIVE

This study presented an empirical method for the geometric calibration of small area PCD-based cone beam CT systems.

METHODS

Unlike the traditional methods, we developed an iterative optimization procedure to determine geometric parameters using the reconstructed images of small metal ball bearings (BBs) embedded in a custom-built phantom. An objective function incorporating the sphericities and symmetries of the embedded BBs was defined to assess performance of the reconstruction algorithm with the given initial estimated set of geometric parameters. The optimal parameter values were those which minimized the objective function. The TIGRE toolbox was employed for fast tomographic reconstruction. To evaluate the proposed method, computer simulations were carried out using various numbers of spheres placed in various locations. Furthermore, efficacy of the method was experimentally assessed using a custom-made benchtop PCD-based cone beam CT.

RESULTS

Computer simulations validated the accuracy and reproducibility of the proposed method. The precise estimation of the geometric parameters of the benchtop revealed high-quality imaging in CT reconstruction of a breast phantom. Within the phantom, the cylindrical holes, fibers, and speck groups were imaged in high fidelity. The CNR analysis further revealed the quantitative improvements of the reconstruction performed with the estimated parameters using the proposed method.

CONCLUSION

Apart from the computational cost, we concluded that the method was easy to implement and robust.

Fabrication of microcalcifications for insertion into phantoms used to evaluate x-ray breast imaging systems

Physical breast phantoms can be used to evaluate x-ray imaging systems such as mammography, digital breast tomosynthesis and dedicated breast computed tomography (bCT). These phantoms typically attempt to mimic x-ray attenuation properties of adipose and fibroglandular tissues within the breast. In order to use these phantoms for task-based objective assessment of image quality, relevant diagnostic features should be modeled within the phantom, such as mass lesions and/or microcalcifications. Evaluating imaging system performance in detecting microcalcifications is of particular interest due to its' clinical significance. Many previously-developed phantoms have used materials that model microcalcifications using unrealistic chemical composition, which do not accurately portray their desired x-ray attenuation and scatter properties. We report here on a new method for developing real microcalcification simulants that can be embedded in breast phantoms. This was achieved in several steps, including cross-linking hydroxyapatite and calcium oxalate powders with a binder called polyvinylpyrrolidone (PVP), and mechanical compression. The fabricated microcalcifications were evaluated by measuring their x-ray attenuation and scatter properties using x-ray spectroscopy and x-ray diffraction systems, respectively, and were demonstrated with x-ray mammography and bCT images. Results suggest that using these microcalcification models will make breast phantoms more realistic for use in evaluating task-based detection performance of the abovementioned breast imaging techniques, and bode well for extending their use to spectral imaging and x-ray coherent scatter computed tomography.

Erratum: Characterization of a GaAs photon-counting detector for mammography

The erratum corrects an error in Fig. 6 of the originally published article.

Using convolutional neural networks to discriminate between cysts and masses in Monte Carlo-simulated dual-energy mammography

PURPOSE

A substantial percentage of recalls (up to 20%) in screening mammography is attributed to extended round lesions. Benign fluid-filled breast cysts often appear similar to solid tumors in conventional mammograms. Spectral imaging (dual-energy or photon-counting mammography) has been shown to discriminate between cysts and solid masses with clinically acceptable accuracy. This work explores the feasibility of using convolutional neural networks (CNNs) for this task.

METHODS

A series of Monte Carlo experiments was conducted with digital breast phantoms and embedded synthetic lesions to produce realistic dual-energy images of both lesion types. We considered such factors as nonuniform anthropomorphic background, size of the mass, breast compression thickness, and variability in lesion x-ray attenuation. These data then were used to train a deep neural network (ResNet-18) to learn the differences in x-ray attenuation of cysts and masses.

RESULTS

Our simulation results showed that the CNN-based classifier could reliably discriminate between cystic and solid mass round lesions in dual-energy images with an area under the receiver operating characteristic curve (ROC AUC) of 0.98 or greater.

CONCLUSIONS

The proposed approach showed promising performance and ease of implementation, and could be applied to novel photon-counting detector-based spectral mammography systems.

Exploring CNN potential in discriminating benign and malignant calcifications in conventional and dual-energy FFDM: simulations and experimental observations

Purpose: Deep convolutional neural networks (CNN) have demonstrated impressive success in various image classification tasks. We investigated the use of CNNs to distinguish between benign and malignant microcalcifications, using either conventional or dual-energy mammography x-ray images. The two kinds of calcifications, known as type-I (calcium oxalate crystals) and type-II (calcium phosphate aggregations), have different attenuation properties in the mammographic energy range. However, variations in microcalcification shape, size, and density as well as compressed breast thickness and breast tissue background make this a challenging discrimination task for the human visual system. Approach: Simulations (conventional and dual-energy mammography) and phantom experiments (conventional mammography only) were conducted using the range of breast thicknesses and randomly shaped microcalcifications. The off-the-shelf Resnet-18 CNN was trained on the regions of interest with calcification clusters of the two kinds. Results: Both Monte Carlo simulations and experimental phantom data suggest that deep neural networks can be trained to separate the two classes of calcifications with high accuracy, using dual-energy mammograms. Conclusions: Our work shows the encouraging results of using the CNNs for non-invasive testing for type-I and type-II microcalcifications and may stimulate further research in this area with expanding presence of the novel breast imaging modalities like dual-energy mammography or systems using photon-counting detectors.

Characterization of a GaAs photon-counting detector for mammography

Purpose: The purpose of this study was to evaluate the potential of a prototype gallium arsenide (GaAs) photon-counting detector (PCD) for imaging of the breast. Approach: First, the contrast-to-noise ratio (CNR) using different aluminum/poly(methyl methacrylate) (PMMA) phantoms of different thicknesses were measured. Second, microcalcification detection accuracy using a receiver operating characteristic study with three observers reading an ensemble of images was measured. Finally, the feasibility of using a GaAs system with two energy bins for contrast-enhanced mammography was investigated. Results: For the first two studies, the GaAs detector was compared with a commercial mammography system. The CNR was estimated by imaging 18-, 36-, and 110 - μ m -thick aluminum targets placed on top of 6 cm of PMMA plates and was found to be similar or better over a range of exposures. We observed a similar performance of detecting microcalcifications with the GaAs detector over a range of clinically applicable dose levels with a small increase at lower dose levels. The results also showed that contrast-enhanced spectral mammography using a GaAs PCD is feasible and beneficial. Conclusions: Results from this study suggest that performance with this new detector seems either slightly improved or equivalent to a commercial mammography system that used an energy-integrated detector. No attempt at optimizing exposure techniques for the GaAs detector was performed. Further research is needed to determine optimal acquisition parameters for the GaAs detector and to develop more sophisticated material decomposition algorithms that promise to provide improved quantitative estimates of iodine uptake.

Assessment of task-based performance from five clinical DBT systems using an anthropomorphic breast phantom

PURPOSE

Digital breast tomosynthesis (DBT) is a limited-angle tomographic breast imaging modality that can be used for breast cancer screening in conjunction with full-field digital mammography (FFDM) or synthetic mammography (SM). Currently, there are five commercial DBT systems that have been approved by the U.S. FDA for breast cancer screening, all varying greatly in design and imaging protocol. Because the systems are different in technical specifications, there is a need for a quantitative approach for assessing them. In this study, the DBT systems are assessed using a novel methodology with an inkjet-printed anthropomorphic phantom and four alternative forced choice (4AFC) study scheme.

METHOD

A breast phantom was fabricated using inkjet printing and parchment paper. The phantom contained 5-mm spiculated masses fabricated with potassium iodide (KI)-doped ink and microcalcifications (MCs) made with calcium hydroxyapatite. Images of the phantom were acquired on all five systems with DBT, FFDM, and SM modalities where available using beam settings under automatic exposure control. A 4AFC study was conducted to assess reader performance with each signal under each modality. Statistical analysis was performed on the data to determine proportion correct (PC), standard deviations, and levels of significance.

RESULTS

For masses, overall detection was highest with DBT. The difference in PC was statistically significant between DBT and SM for most systems. A relationship was observed between increasing PC and greater gantry span. For MCs, performance was highest with DBT and FFDM compared to SM. The difference between PC of DBT and PC of SM was statistically significant for all manufacturers.

CONCLUSIONS

This methodology represents a novel approach for evaluating systems. This study is the first of its kind to use an inkjet-printed anthropomorphic phantom with realistic signals to assess performance of clinical DBT imaging systems.

Computational reader design and statistical performance evaluation of an in-silico imaging clinical trial comparing digital breast tomosynthesis with full-field digital mammography

A recent study reported on an in-silico imaging trial that evaluated the performance of digital breast tomosynthesis (DBT) as a replacement for full-field digital mammography (FFDM) for breast cancer screening. In this in-silico trial, the whole imaging chain was simulated, including the breast phantom generation, the x-ray transport process, and computational readers for image interpretation. We focus on the design and performance characteristics of the computational reader in the above-mentioned trial. Location-known lesion (spiculated mass and clustered microcalcifications) detection tasks were used to evaluate the imaging system performance. The computational readers were designed based on the mechanism of a channelized Hotelling observer (CHO), and the reader models were selected to trend human performance. Parameters were tuned to ensure stable lesion detectability. A convolutional CHO that can adapt a round channel function to irregular lesion shapes was compared with the original CHO and was found to be suitable for detecting clustered microcalcifications but was less optimal in detecting spiculated masses. A three-dimensional CHO that operated on the multiple slices was compared with a two-dimensional (2-D) CHO that operated on three versions of 2-D slabs converted from the multiple slices and was found to be optimal in detecting lesions in DBT. Multireader multicase reader output analysis was used to analyze the performance difference between FFDM and DBT for various breast and lesion types. The results showed that DBT was more beneficial in detecting masses than detecting clustered microcalcifications compared with FFDM, consistent with the finding in a clinical imaging trial. Statistical uncertainty smaller than 0.01 standard error for the estimated performance differences was achieved with a dataset containing approximately 3000 breast phantoms. The computational reader design methodology presented provides evidence that model observers can be useful in-silico tools for supporting the performance comparison of breast imaging systems.

Objective assessment of task performance: a comparison of two FFDM detectors using an anthropomorphic breast phantom

Current digital mammography systems primarily employ one of two types of detectors: indirect conversion, typically using a cesium-iodine scintillator integrated with an amorphous silicon photodiode matrix, or direct conversion, using a photoconductive layer of amorphous selenium (a-Se) combined with thin-film transistor array. The goal of this study was to evaluate a methodology for objectively assessing image quality to compare human observer task performance in detecting microcalcification clusters and extended mass-like lesions achieved with different detector types. The proposed assessment methodology uses a novel anthropomorphic breast phantom fabricated with ink-jet printing. In addition to human observer detection performance, standard linear metrics such as modulation transfer function, noise power spectrum, and detective quantum efficiency (DQE) were also measured to assess image quality. An Analogic Anrad AXS-2430 a-Se detector used in a commercial FFDM/DBT system and a Teledyne Dalsa Xineos-2329 with CMOS pixel readout were evaluated and compared. The DQE of each detector was similar over a range of exposures. Similar task performance in detecting microcalcifications and masses was observed between the two detectors over a range of clinically applicable dose levels, with some perplexing differences in the detection of microcalcifications at the lowest dose measurement. The evaluation approach presented seems promising as a new technique for objective assessment of breast imaging technology.

Classification of breast microcalcifications using dual-energy mammography

The potential of dual-energy mammography for microcalcification classification was investigated with simulation and phantom studies. Classification of type I/II calcifications was performed using the tissue attenuation ratio as a performance metric. The simulation and phantom studies were carried out using breast phantoms of 50% fibroglandular and 50% adipose tissue composition and thicknessess ranging from 3 to 6 cm. The phantoms included models of microcalcifications ranging in size between 200 and 900    μ m . The simulation study was carried out with fixed MGD of 1.5 mGy using various low- and high-kVp spectra, aluminum filtration thicknesses, and exposure distribution ratios to predict an optimized imaging protocol for the phantom study. Attenuation ratio values were calculated for microcalcification signals of different types at two different voltage settings. ROC analysis showed that classification performance as indicated by the area under the ROC curve was always greater than 0.95 for 1.5 mGy deposited mean glandular dose. This study provides encouraging first results in classifying malignant and benign microcalcifications based solely on dual-energy mammography images.

Evaluation of Digital Breast Tomosynthesis as Replacement of Full-Field Digital Mammography Using an In Silico Imaging Trial

IMPORTANCE

Expensive and lengthy clinical trials can delay regulatory evaluation of innovative technologies, affecting patient access to high-quality medical products. Simulation is increasingly being used in product development but rarely in regulatory applications.

OBJECTIVES

To conduct a computer-simulated imaging trial evaluating digital breast tomosynthesis (DBT) as a replacement for digital mammography (DM) and to compare the results with a comparative clinical trial.

DESIGN, SETTING, AND PARTICIPANTS

The simulated Virtual Imaging Clinical Trial for Regulatory Evaluation (VICTRE) trial was designed to replicate a clinical trial that used human patients and radiologists. Images obtained with in silico versions of DM and DBT systems via fast Monte Carlo x-ray transport were interpreted by a computational reader detecting the presence of lesions. A total of 2986 synthetic image-based virtual patients with breast sizes and radiographic densities representative of a screening population and compressed thicknesses from 3.5 to 6 cm were generated using an analytic approach in which anatomical structures are randomly created within a predefined breast volume and compressed in the craniocaudal orientation. A positive cohort contained a digitally inserted microcalcification cluster or spiculated mass.

MAIN OUTCOMES AND MEASURES

The trial end point was the difference in area under the receiver operating characteristic curve between modalities for lesion detection. The trial was sized for an SE of 0.01 in the change in area under the curve (AUC), half the uncertainty in the comparative clinical trial.

RESULTS

In this trial, computational readers analyzed 31 055 DM and 27 960 DBT cases from 2986 virtual patients with the following Breast Imaging Reporting and Data System densities: 286 (9.6%) extremely dense, 1200 (40.2%) heterogeneously dense, 1200 (40.2%) scattered fibroglandular densities, and 300 (10.0%) almost entirely fat. The mean (SE) change in AUC was 0.0587 (0.0062) (P < .001) in favor of DBT. The change in AUC was larger for masses (mean [SE], 0.0903 [0.008]) than for calcifications (mean [SE], 0.0268 [0.004]), which was consistent with the findings of the comparative trial (mean [SE], 0.065 [0.017] for masses and -0.047 [0.032] for calcifications).

CONCLUSIONS AND RELEVANCE

The results of the simulated VICTRE trial are consistent with the performance seen in the comparative trial. While further research is needed to assess the generalizability of these findings, in silico imaging trials represent a viable source of regulatory evidence for imaging devices.

Feasibility of estimating volumetric breast density from mammographic x-ray spectra using a cadmium telluride photon-counting detector

PURPOSE

Mammographic density of glandular breast tissue has a masking effect that can reduce lesion detection accuracy and is also a strong risk factor for breast cancer. Therefore, accurate quantitative estimation of breast density is clinically important. In this study, we investigate experimentally the feasibility of quantifying volumetric breast density with spectral mammography using a CdTe-based photon-counting detector.

METHODS

To demonstrate proof-of-principle, this study was carried out using the single pixel Amptek XR-100T-CdTe detector. The total number of x rays recorded by the detector from a single pencil-beam projection through 50%/50% of adipose/glandular mass fraction-equivalent phantoms was measured. Material decomposition assuming two, four, and eight energy bins was then applied to characterize the inspected phantom into adipose and glandular using log-likelihood estimation, taking into account the polychromatic source, the detector response function, and the energy-dependent attenuation.

RESULTS

Measurement tests were carried out for different doses, kVp settings, and different breast sizes. For dose of 1 mGy and above, the percent relative root mean square (RMS) errors of the estimated breast density was measured below 7% for all three phantom studies. It was also observed that some decrease in RMS errors was achieved using eight energy bins. For 3 and 4 cm thick phantoms, performance at 40 and 45 kVp showed similar performance. However, it was observed that 45 kVp showed better performance for a phantom thickness of 6 cm at low dose levels due to increased statistical variation at lower photon count levels with 40 kVp.

CONCLUSION

The results of the current study suggest that photon-counting spectral mammography systems using CdTe detectors have the potential to be used for accurate quantification of volumetric breast density on a pixel-to-pixel basis, with an RMS error of less than 7%.

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.

Assessing task performance in FFDM, DBT, and synthetic mammography using uniform and anthropomorphic physical phantoms

PURPOSE

The purpose of this study is to quantify the differences in detectability between full field digital mammography (FFDM), digital breast tomosynthesis (DBT), and synthetic mammography (SM) for challenging, low contrast signals, in the context of both a uniform and an anthropomorphic, textured phantom.

METHODS

Images of the phantoms were acquired using a Hologic Selenia Dimensions system. Images were taken at 50%, 100%, and 200% of the dose delivered under automatic exposure control (AEC). Low-contrast disks, created using an inkjet printer with iodine-doped ink, were inserted into the phantom. The disks varied in diameter from 210 to 630 μm, and in local contrast from 1.1% to 2.8% in regular increments. Human observers located the disks in a 4 alternative forced choice experiment. Proportion correct (PC) was computed as the number of correct localizations out of the total number of tries.

RESULTS

Overall, scores from FFDM and DBT were consistently greater than scores from SM. At an exposure corresponding to the AEC setting, mean PC scores for the largest disks with the uniform phantom were 0.80 for FFDM, 0.83 for DBT, and 0.66 for SM, with the same rank ordering at other doses. Scores were similar but lower for the nonuniform background. At an exposure twice the AEC setting, however, the difference between uniform and nonuniform scores was most pronounced for DBT alone. Differences between scores for FFDM and SM were statistically significant, while those between FFDM and DBT were not. Scores were used to compute the minimum contrast level needed to reach 62.5% detection rate. The minimum contrast for SM was 36%-81% higher compared to FFDM or DBT, in either background.

CONCLUSIONS

This study shows that an anthropomorphic phantom and lesions inserts may be used to conduct a reader study. Detectability was significantly lower for synthetic mammography than for FFDM or DBT, for all conditions. Additionally, observer performance was consistently lower for the anthropomorphic phantom, indicating the greater challenge due to anatomical background. Because of this, it may be important to use realistic phantoms in observer studies in order to draw conclusions that are more clinically relevant.

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.

Evaluation of position-estimation methods applied to CZT-based photon-counting detectors for dedicated breast CT

Semiconductor photon-counting detectors based on high atomic number, high density materials [cadmium zinc telluride (CZT)/cadmium telluride (CdTe)] for x-ray computed tomography (CT) provide advantages over conventional energy-integrating detectors, including reduced electronic and Swank noise, wider dynamic range, capability of spectral CT, and improved signal-to-noise ratio. Certain CT applications require high spatial resolution. In breast CT, for example, visualization of microcalcifications and assessment of tumor microvasculature after contrast enhancement require resolution on the order of [Formula: see text]. A straightforward approach to increasing spatial resolution of pixellated CZT-based radiation detectors by merely decreasing the pixel size leads to two problems: (1) fabricating circuitry with small pixels becomes costly and (2) inter-pixel charge spreading can obviate any improvement in spatial resolution. We have used computer simulations to investigate position estimation algorithms that utilize charge sharing to achieve subpixel position resolution. To study these algorithms, we model a simple detector geometry with a [Formula: see text] array of [Formula: see text] pixels, and use a conditional probability function to model charge transport in CZT. We used COMSOL finite element method software to map the distribution of charge pulses and the Monte Carlo package PENELOPE for simulating fluorescent radiation. Performance of two x-ray interaction position estimation algorithms was evaluated: the method of maximum-likelihood estimation and a fast, practical algorithm that can be implemented in a readout application-specific integrated circuit and allows for identification of a quadrant of the pixel in which the interaction occurred. Both methods demonstrate good subpixel resolution; however, their actual efficiency is limited by the presence of fluorescent [Formula: see text]-escape photons. Current experimental breast CT systems typically use detectors with a pixel size of [Formula: see text], with [Formula: see text] binning during the acquisition giving an effective pixel size of [Formula: see text]. Thus, it would be expected that the position estimate accuracy reported in this study would improve detection and visualization of microcalcifications as compared to that with conventional detectors.

Iterative reconstruction using a Monte Carlo based system transfer matrix for dedicated breast positron emission tomography

To maximize sensitivity, it is desirable that ring Positron Emission Tomography (PET) systems dedicated for imaging the breast have a small bore. Unfortunately, due to parallax error this causes substantial degradation in spatial resolution for objects near the periphery of the breast. In this work, a framework for computing and incorporating an accurate system matrix into iterative reconstruction is presented in an effort to reduce spatial resolution degradation towards the periphery of the breast. The GATE Monte Carlo Simulation software was utilized to accurately model the system matrix for a breast PET system. A strategy for increasing the count statistics in the system matrix computation and for reducing the system element storage space was used by calculating only a subset of matrix elements and then estimating the rest of the elements by using the geometric symmetry of the cylindrical scanner. To implement this strategy, polar voxel basis functions were used to represent the object, resulting in a block-circulant system matrix. Simulation studies using a breast PET scanner model with ring geometry demonstrated improved contrast at 45% reduced noise level and 1.5 to 3 times resolution performance improvement when compared to MLEM reconstruction using a simple line-integral model. The GATE based system matrix reconstruction technique promises to improve resolution and noise performance and reduce image distortion at FOV periphery compared to line-integral based system matrix reconstruction.

Investigation of statistical iterative reconstruction for dedicated breast CT

PURPOSE

Dedicated breast CT has great potential for improving the detection and diagnosis of breast cancer. Statistical iterative reconstruction (SIR) in dedicated breast CT is a promising alternative to traditional filtered backprojection (FBP). One of the difficulties in using SIR is the presence of free parameters in the algorithm that control the appearance of the resulting image. These parameters require tuning in order to achieve high quality reconstructions. In this study, the authors investigated the penalized maximum likelihood (PML) method with two commonly used types of roughness penalty functions: hyperbolic potential and anisotropic total variation (TV) norm. Reconstructed images were compared with images obtained using standard FBP. Optimal parameters for PML with the hyperbolic prior are reported for the task of detecting microcalcifications embedded in breast tissue.

METHODS

Computer simulations were used to acquire projections in a half-cone beam geometry. The modeled setup describes a realistic breast CT benchtop system, with an x-ray spectra produced by a point source and an a-Si, CsI:Tl flat-panel detector. A voxelized anthropomorphic breast phantom with 280 μm microcalcification spheres embedded in it was used to model attenuation properties of the uncompressed woman's breast in a pendant position. The reconstruction of 3D images was performed using the separable paraboloidal surrogates algorithm with ordered subsets. Task performance was assessed with the ideal observer detectability index to determine optimal PML parameters.

RESULTS

The authors' findings suggest that there is a preferred range of values of the roughness penalty weight and the edge preservation threshold in the penalized objective function with the hyperbolic potential, which resulted in low noise images with high contrast microcalcifications preserved. In terms of numerical observer detectability index, the PML method with optimal parameters yielded substantially improved performance (by a factor of greater than 10) compared to FBP. The hyperbolic prior was also observed to be superior to the TV norm. A few of the best-performing parameter pairs for the PML method also demonstrated superior performance for various radiation doses. In fact, using PML with certain parameter values results in better images, acquired using 2 mGy dose, than FBP-reconstructed images acquired using 6 mGy dose.

CONCLUSIONS

A range of optimal free parameters for the PML algorithm with hyperbolic and TV norm-based potentials is presented for the microcalcification detection task, in dedicated breast CT. The reported values can be used as starting values of the free parameters, when SIR techniques are used for image reconstruction. Significant improvement in image quality can be achieved by using PML with optimal combination of parameters, as compared to FBP. Importantly, these results suggest improved detection of microcalcifications can be obtained by using PML with lower radiation dose to the patient, than using FBP with higher dose.

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.

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.

Scaling-law for the energy dependence of anatomic power spectrum in dedicated breast CT

PURPOSE

To determine the x-ray photon energy dependence of the anatomic power spectrum of the breast when imaged with dedicated breast computed tomography (CT).

METHODS

A theoretical framework for scaling the empirically determined anatomic power spectrum at one x-ray photon energy to that at any given x-ray photon energy when imaged with dedicated breast CT was developed. Theory predicted that when the anatomic power spectrum is fitted with a power curve of the form k f(-β), where k and β are fit coefficients and f is spatial frequency, the exponent β would be independent of x-ray photon energy (E), and the amplitude k scales with the square of the difference in energy-dependent linear attenuation coefficients of fibroglandular and adipose tissues. Twenty mastectomy specimens based numerical phantoms that were previously imaged with a benchtop flat-panel cone-beam CT system were converted to 3D distribution of glandular weight fraction (f(g)) and were used to verify the theoretical findings. The 3D power spectrum was computed in terms of f(g) and after converting to linear attenuation coefficients at monoenergetic x-ray photon energies of 20-80 keV in 5 keV intervals. The 1D power spectra along the axes were extracted and fitted with a power curve of the form k f(-β). The energy dependence of k and β were analyzed.

RESULTS

For the 20 mastectomy specimen based numerical phantoms used in the study, the exponent β was found to be in the range of 2.34-2.42, depending on the axis of measurement. Numerical simulations agreed with the theoretical predictions that for a power-law anatomic spectrum of the form k f(-β), β was independent of E and k(E) = k(1)[μ(g)(E) - μ(a)(E)](2), where k(1) is a constant, and μ(g)(E) and μ(a)(E) represent the energy-dependent linear attenuation coefficients of fibroglandular and adipose tissues, respectively.

CONCLUSIONS

Numerical simulations confirmed the theoretical predictions that in dedicated breast CT, the spatial frequency dependence of the anatomic power spectrum will be independent of x-ray photon energy, and the amplitude of the anatomic power spectrum scales by the square of difference in linear attenuation coefficients of fibroglandular and adipose tissues.

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.

Penalized maximum likelihood reconstruction for improved microcalcification detection in breast tomosynthesis

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

Evaluation of a variable dose acquisition technique for microcalcification and mass detection in digital breast tomosynthesis

In this article the authors evaluate a recently proposed variable dose (VD)-digital breast tomosynthesis (DBT) acquisition technique in terms of the detection accuracy for breast masses and microcalcification (MC) clusters. With this technique, approximately half of the total dose is used for one center projection and the remaining dose is split among the other tomosynthesis projection views. This acquisition method would yield both a projection view and a reconstruction view. One of the aims of this study was to evaluate whether the center projection alone of the VD acquisition can provide equal or superior MC detection in comparison to the 3D images from uniform dose (UD)-DBT. Another aim was to compare the mass-detection capabilities of 3D reconstructions from VD-DBT and UD-DBT. In a localization receiver operating characteristic (LROC) observer study of MC detection, the authors compared the center projection of a VD acquisitioh scheme (at 2 mGy dose) with detector pixel size of 100 microm with the UD-DBT reconstruction (at 4 mGy dose) obtained with a voxel size of 100 microm. MCs with sizes of 150 and 180 microm were used in the study, with each cluster consisting of seven MCs distributed randomly within a small volume. Reconstructed images in UD-DBT were obtained from a projection set that had a total of 4 mGy dose. The current study shows that for MC detection, using the center projection alone of VD acquisition scheme performs worse with area under the LROC curve (AL) of 0.76 than when using the 3D reconstructed image using the UD acquisition scheme (AL=0.84). A 2D ANOVA found a statistically significant difference (p=0.038) at a significance level of 0.05. In the current study, although a reconstructed image was also available using the VD acquisition scheme, it was not used to assist the MC detection task which was done using the center projection alone. In the case of evaluation of detection accuracy of masses, the reconstruction with VD-DBT (AL=0.71) was compared to that obtained from the UD-DBT (AL=0.78). The authors found no statistically significant difference between the two (p-value=0.22), although all the observers performed better for UD-DBT.

Characterization of scatter in cone-beam CT breast imaging: comparison of experimental measurements and Monte Carlo simulation

It is commonly understood that scattered radiation in x-ray computed tomography (CT) degrades the reconstructed image. As a precursor to developing scatter compensation methods, it is important to characterize this scatter using both empirical measurements and Monte Carlo simulations. Previous studies characterizing scatter using both experimental measurements and Monte Carlo simulations have been reported in diagnostic radiology and conventional mammography. The emerging technology of cone-beam CT breast imaging (CTBI) differs significantly from conventional mammography in the breast shape and imaging geometry, aspects that are important factors impacting the measured scatter. This study used a bench-top cone-beam CTBI system with an indirect flat-panel detector. A cylindrical phantom with equivalent composition of 50% fibroglandular and 50% adipose tissues was used, and scatter distributions were measured by beam stop and aperture methods. The GEANT4-based simulation package GATE was used to model x-ray photon interactions in the phantom and detector. Scatter to primary ratio (SPR) measurements using both the beam stop and aperture methods were consistent within 5% after subtraction of nonbreast scatter contributions and agree with the low energy electromagnetic model simulation in GATE. The validated simulation model was used to characterize the SPR in different CTBI conditions. In addition, a realistic, digital breast phantom was simulated to determine the characteristics of various scatter components that cannot be separated in measurements. The simulation showed that the scatter distribution from multiple Compton and Rayleigh scatterings, as well as from the single Compton scattering, has predominantly low-frequency characteristics. The single Rayleigh scatter was observed to be the primary contribution to the spatially variant scatter component.

Breast CT

Breast cancer is a serious disease that accounts for approximately 40,000 deaths per year in the United States. Unfortunately, there is no known cause of breast cancer, and therefore the best way to prevent mortality is early detection. In the past 15 years, breast cancer mortality has been reduced significantly, which is in part due to screening with film-screen mammography. Nonetheless, conventional mammography lacks sensitivity, especially for certain subgroups of women such as those with dense breast tissue, those under 50 years old, and pre- or perimenopausal women. In addition, mammography has a very poor positive predictive value for biopsy, with 70%-90% of biopsies performed turning out negative. By improving visualization of breast tissue, X-ray computerized tomography (CT) of the breast can potentially provide improvements in diagnostic accuracy over conventional mammography. Owing to recent technological developments in digital detector technology, flat-panel CT imagers dedicated to imaging of the breast are now feasible. A number of academic groups are currently researching dedicated breast CT and prototype systems are currently being evaluated in the clinical setting.

Evaluating the impact of X-ray spectral shape on image quality in flat-panel CT breast imaging

In recent years, there has been an increasing interest in exploring the feasibility of dedicated computed tomography (CT) breast imaging using a flat-panel digital detector in a truncated cone-beam imaging geometry. Preliminary results are promising and it appears as if three-dimensional tomographic imaging of the breast has great potential for reducing the masking effect of superimposed parenchymal structure typically observed with conventional mammography. In this study, a mathematical framework used for determining optimal design and acquisition parameters for such a CT breast imaging system is described. The ideal observer signal-to-noise ratio (SNR) is used as a figure of merit, under the assumptions that the imaging system is linear and shift invariant. Computation of the ideal observer SNR used a parallel-cascade model to predict signal and noise propagation through the detector, as well as a realistic model of the lesion detection task in breast imaging. For all evaluations, the total mean glandular dose for a CT breast imaging study was constrained to be approximately equivalent to that of a two-view conventional mammography study. The framework presented was used to explore the effect of x-ray spectral shape across an extensive range of kVp settings, filter material types, and filter thicknesses. The results give an indication of how spectral shape can affect image quality in flat-panel CT breast imaging.

Reconstruction of 2D PET data with Monte Carlo generated system matrix for generalized natural pixels

In discrete detector PET, natural pixels are image basis functions calculated from the response of detector pairs. By using reconstruction with natural pixel basis functions, the discretization of the object into a predefined grid can be avoided. Here, we propose to use generalized natural pixel reconstruction. Using this approach, the basis functions are not the detector sensitivity functions as in the natural pixel case but uniform parallel strips. The backprojection of the strip coefficients results in the reconstructed image. This paper proposes an easy and efficient way to generate the matrix M directly by Monte Carlo simulation. Elements of the generalized natural pixel system matrix are formed by calculating the intersection of a parallel strip with the detector sensitivity function. These generalized natural pixels are easier to use than conventional natural pixels because the final step from solution to a square pixel representation is done by simple backprojection. Due to rotational symmetry in the PET scanner, the matrix M is block circulant and only the first blockrow needs to be stored. Data were generated using a fast Monte Carlo simulator using ray tracing. The proposed method was compared to a listmode MLEM algorithm, which used ray tracing for doing forward and backprojection. Comparison of the algorithms with different phantoms showed that an improved resolution can be obtained using generalized natural pixel reconstruction with accurate system modelling. In addition, it was noted that for the same resolution a lower noise level is present in this reconstruction. A numerical observer study showed the proposed method exhibited increased performance as compared to a standard listmode EM algorithm. In another study, more realistic data were generated using the GATE Monte Carlo simulator. For these data, a more uniform contrast recovery and a better contrast-to-noise performance were observed. It was observed that major improvements in contrast recovery were obtained with MLEM when the correct system matrix was used instead of simple ray tracing. The correct modelling was the major cause of improved contrast for the same background noise. Less important factors were the choice of the algorithm (MLEM performed better than ART) and the basis functions (generalized natural pixels gave better results than pixels).

A computer simulation study comparing lesion detection accuracy with digital mammography, breast tomosynthesis, and cone-beam CT breast imaging

Although conventional mammography is currently the best modality to detect early breast cancer, it is limited in that the recorded image represents the superposition of a three-dimensional (3D) object onto a 2D plane. Recently, two promising approaches for 3D volumetric breast imaging have been proposed, breast tomosynthesis (BT) and CT breast imaging (CTBI). To investigate possible improvements in lesion detection accuracy with either breast tomosynthesis or CT breast imaging as compared to digital mammography (DM), a computer simulation study was conducted using simulated lesions embedded into a structured 3D breast model. The computer simulation realistically modeled x-ray transport through a breast model, as well as the signal and noise propagation through a CsI based flat-panel imager. Polyenergetic x-ray spectra of Mo/Mo 28 kVp for digital mammography, Mo/Rh 28 kVp for BT, and W/Ce 50 kVp for CTBI were modeled. For the CTBI simulation, the intensity of the x-ray spectra for each projection view was determined so as to provide a total average glandular dose of 4 mGy, which is approximately equivalent to that given in conventional two-view screening mammography. The same total dose was modeled for both the DM and BT simulations. Irregular lesions were simulated by using a stochastic growth algorithm providing lesions with an effective diameter of 5 mm. Breast tissue was simulated by generating an ensemble of backgrounds with a power law spectrum, with the composition of 50% fibroglandular and 50% adipose tissue. To evaluate lesion detection accuracy, a receiver operating characteristic (ROC) study was performed with five observers reading an ensemble of images for each case. The average area under the ROC curves (Az) was 0.76 for DM, 0.93 for BT, and 0.94 for CTBI. Results indicated that for the same dose, a 5 mm lesion embedded in a structured breast phantom was detected by the two volumetric breast imaging systems, BT and CTBI, with statistically significant higher confidence than with planar digital mammography, while the difference in lesion detection between BT and CTBI was not statistically significant.

Normalized glandular dose (DgN) coefficients for flat-panel CT breast imaging

The development of new digital mammography techniques such as dual-energy imaging, tomosynthesis and CT breast imaging will require investigation of optimal camera design parameters and optimal imaging acquisition parameters. In optimizing these acquisition protocols and imaging systems it is important to have knowledge of the radiation dose to the breast. This study presents a methodology for estimating the normalized glandular dose to the uncompressed breast using the geometry proposed for flat-panel CT breast imaging. The simulation uses the GEANT 3 Monte Carlo code to model x-ray transport and absorption within the breast phantom. The Monte Carlo software was validated for breast dosimetry by comparing results of the normalized glandular dose (DgN) values of the compressed breast to those reported in the literature. The normalized glandular dose was then estimated for a range of breast diameters from 10 cm to 18 cm using an uncompressed breast model with a homogeneous composition of adipose and glandular tissue, and for monoenergetic x-rays from 10 keV to 120 keV. These data were fit providing expressions for the normalized glandular dose. Using these expressions for the DgN coefficients and input variables such as the diameter, height and composition of the breast phantom, the mean glandular dose for any spectra can be estimated. A computer program to provide normalized glandular dose values has been made available online. In addition, figures displaying energy deposition maps are presented to better understand the spatial distribution of dose in CT breast imaging.

Noise characterization of block-iterative reconstruction algorithms: II. Monte Carlo simulations

In Soares et al. (2000), the ensemble statistical properties of the rescaled block-iterative expectation-maximization (RBI-EM) reconstruction algorithm and rescaled block-iterative simultaneous multiplicative algebraic reconstruction technique (RBI-SMART) were derived. Included in this analysis were the special cases of RBI-EM, maximum-likelihood EM (ML-EM) and ordered-subset EM (OS-EM), and the special case of RBI-SMART, SMART. Explicit expressions were found for the ensemble mean, covariance matrix, and probability density function of RBI reconstructed images, as a function of iteration number. The theoretical formulations relied on one approximation, namely that the noise in the reconstructed image was small compared to the mean image. In this paper, we evaluate the predictions of the theory by using Monte Carlo methods to calculate the sample statistical properties of each algorithm and then compare the results with the theoretical formulations. In addition, the validity of the approximation will be justified.

GATE: a simulation toolkit for PET and SPECT

Monte Carlo simulation is an essential tool in emission tomography that can assist in the design of new medical imaging devices, the optimization of acquisition protocols and the development or assessment of image reconstruction algorithms and correction techniques. GATE, the Geant4 Application for Tomographic Emission, encapsulates the Geant4 libraries to achieve a modular, versatile, scripted simulation toolkit adapted to the field of nuclear medicine. In particular, GATE allows the description of time-dependent phenomena such as source or detector movement, and source decay kinetics. This feature makes it possible to simulate time curves under realistic acquisition conditions and to test dynamic reconstruction algorithms. This paper gives a detailed description of the design and development of GATE by the OpenGATE collaboration, whose continuing objective is to improve, document and validate GATE by simulating commercially available imaging systems for PET and SPECT. Large effort is also invested in the ability and the flexibility to model novel detection systems or systems still under design. A public release of GATE licensed under the GNU Lesser General Public License can be downloaded at http:/www-lphe.epfl.ch/GATE/. Two benchmarks developed for PET and SPECT to test the installation of GATE and to serve as a tutorial for the users are presented. Extensive validation of the GATE simulation platform has been started, comparing simulations and measurements on commercially available acquisition systems. References to those results are listed. The future prospects towards the gridification of GATE and its extension to other domains such as dosimetry are also discussed.

Microcalcification detection using cone-beam CT mammography with a flat-panel imager

The purpose of this study was to investigate microcalcification detectability using CT mammography with a flat-panel imager. To achieve this, a computer simulation was developed to model an amorphous-silicon, CsI based flat-panel imager system using a linear cascaded model. The breast was modelled as a hemi-ellipsoid shape with composition of 50% adipose and 50% glandular tissue. Microcalcifications were modelled as small spheres having a composition of calcium carbonate. The results show that with a mean glandular dose equivalent to that typically used in two-view screening mammography, CT mammography with a flat-panel detector is capable of providing images where most microcalcifications are detectable. A receiver operating characteristic (ROC) study was conducted by five physicist observers viewing simulated CT mammography reconstructions. The results suggest that the microcalcification with its diameter equal to or greater than 0.175 mm can be detected with an average area under the ROC curve (AUC) greater than 0.95 using 0.1 or 0.2 mm pixelized detectors. The results also indicate that the optimal pixel size of the detector is around 0.2 mm for microcalcification detection, based on the trade-off between detectability of microcalcifications and the time required for data acquisition and reconstruction.

Characterization of tomographic sampling in hybrid PET using the Fourier crosstalk matrix

Hybrid positron emission tomography (PET) cameras can be used to measure the distribution of positron emitting radionuclides. An important system parameter for Hybrid PET is the appropriate tomographic sampling requirements. In this paper, a previously developed theoretical formulation for quantifying sampling in continuous-to-discrete tomographic systems, termed the "crosstalk matrix," is used to provide information on the recoverability of the Fourier coefficients that represent the continuous object. In addition, the crosstalk matrix can be related to image quality assessment. Here, we use the crosstalk matrix to evaluate tomographic sampling for Hybrid PET systems. Dual-and triple-head systems were compared, with emphasis placed on studying how system performance changes as the number of gantry stops is increased, and as the line-of-response acceptance angle is reduced. Examination of the crosstalk matrix, as well as figures-of-merit measuring task performance that are computed using the crosstalk matrix, show that increasing angular sampling improves Fourier coefficient recoverability and reduces aliasing.

Evaluation of geometric sensitivity for hybrid PET

Hybrid PET systems have spatially varying sensitivity profiles. These profiles are dependent on imaging parameters, namely, number of heads, head configuration, spacing between gantry stops, radius of rotation (RoR), and coincident head acceptance angle.

METHODS

Sensitivity profiles were calculated across a 500-mm field of view (FoV) for a representative set of existing and theoretic 2-, 3-, and 4-head hybrid PET systems. The head configuration was defined by alpha(n), which describes the angular separation between head 1 and head n. Simulated configurations were 2 head ([alpha(2)] = [180 degrees ]), 3 head ([alpha(2), alpha(3)] = [120 degrees, 240 degrees ] and [90 degrees, 180 degrees ]), and 4 head ([alpha(2), alpha(3), alpha(4)] = [90 degrees, 180 degrees, 270 degrees ]). Four transverse acceptance angles, measured from the perpendicular of the crystal to the surface, were simulated: 90 degrees, 45 degrees, 23 degrees, and 11 degrees. Two RoRs were considered: 250 and 300 mm. Each head was rotated through 360 degrees in 128 steps, and no physical collimation was modeled.

RESULTS

For a 250-mm RoR and 90 degrees acceptance angle, the sensitivities relative to [alpha(2)] = [180 degrees ] were [alpha(2), alpha(3)] = [120 degrees, 240 degrees ], 183%; [alpha(2), alpha(3)] = [90 degrees, 180 degrees ], 159%; and [alpha(2), alpha(3), alpha(4)] = [90 degrees, 180 degrees, 270 degrees ], 317%. Increasing RoR to 300 mm decreased [alpha(2)] = [180 degrees ] sensitivity by approximately 12%; all other configurations were decreased by approximately 75% of their 250-mm RoR sensitivities. Decreasing the acceptance angle to 45 degrees decreased sensitivities to [alpha(2), alpha(3)] = [120 degrees, 240 degrees ], 100%; [alpha(2), alpha(3)] = [90 degrees, 180 degrees ], 105%; and [alpha(2), alpha(3), alpha(4)] = [90 degrees, 180 degrees, 270 degrees ], 210%. The 2-head [alpha(2)] = [180 degrees ] system sensitivity was not affected. The configuration was the most important factor affecting the shape of the sensitivity profiles. For a 250-mm RoR and 90 degrees acceptance angle, [alpha(2)] = [180 degrees ] concentrated sensitivity in the FoV center, [alpha(2), alpha(3)] = [120 degrees, 240 degrees ] had a slightly increased peripheral sensitivity, and the profiles for both [alpha(2), alpha(3)] = [90 degrees, 180 degrees ] and [alpha(2), alpha(3), alpha(4)] = [90 degrees, 180 degrees, 270 degrees ] were completely flat.

CONCLUSION

Sensitivity profiles are affected strongly by imaging parameters; however, profiles can be shaped to concentrate on an annulus or distribute sensitivity uniformly over the FoV. Also, the 4-head system showed a markedly higher sensitivity than either of the 3-head systems.

Evaluation of linear and nonlinear tomosynthetic reconstruction methods in digital mammography

RATIONALE AND OBJECTIVES

The purpose of this study was to comparatively evaluate digital planar mammography and both linear and nonlinear tomosynthetic reconstruction methods.

MATERIALS AND METHODS

A "disk" (ie, target) identification study was conducted to compare planar and reconstruction methods. Projective data using a composite phantom with circular disks were acquired in both planar and tomographic modes by using a full-field, digital mammographic system. Two-dimensional projections were reconstructed with both linear (ie, backprojection) and nonlinear (ie, maximization and minimization) tuned-aperture computed tomographic (TACT) methods to produce three-dimensional data sets. Four board-certified radiologists and one 4th-year radiology resident participated as observers. All images were compared by these observers in terms of the number of disks identified.

RESULTS

Significant differences (P < .05, Bonferroni adjusted) were observed between all reconstruction and planar methods. No significant difference, however, was observed between the planar methods, and only a marginally significant difference (P < .054, Bonferroni adjusted) was observed between TACT-backprojection and TACT-minimization.

CONCLUSION

A combination of linear and nonlinear reconstruction schemes may have potential implications in terms of enhancing image visualization to provide radiologists with valuable diagnostic information.

Comparison of tomosynthesis methods used with digital mammography

RATIONALE AND OBJECTIVES

The authors performed this study to investigate the potential applicability of tomosynthesis to digital mammography. Four methods of tomosynthesis-tuned aperture computed tomography (TACT)-backprojection, TACT-iterative restoration, iterative reconstruction with expectation maximization, and Bayesian smoothing-were compared to planar mammography and analyzed in terms of their contrast-detail characteristics. Specific comparisons between the tomosynthesis methods were not attempted in this study.

MATERIALS AND METHODS

A full-field, amorphous, silicon-based, flat-panel digital mammographic system was used to obtain planar and tomosynthesis projection images. A composite tomosynthesis phantom with a centrally located contrast-detail insert was used as the object of interest. The total exposure for multiple views with tomosynthesis was always equal to or less than that for the planar technique. Algorithms were used to reconstruct the object from the acquired projections.

RESULTS

Threshold contrast characteristics with all tomosynthesis reconstruction methods were significantly better than those with planar mammography, even when planar mammography was performed at more than twice the exposure level. Reduction of out-of-plane structural components was observed in all the tomosynthesis methods analyzed.

CONCLUSION

The contrast-detail trends of all the tomosynthesis methods analyzed in this study were better than those of planar mammography. Further optimization of the algorithms could lead to better image reconstruction, which would improve visualization of valuable diagnostic information.