Evaluating Spectral Performance for Quantitative Contrast-Enhanced Breast CT with a GaAs based Photon Counting Detector: A Simulation Approach.

Quantitative contrast-enhanced breastcomputed tomography (CT) has the potential to improve the diagnosis and management of breast cancer. Traditional methods using energy-integrated detectors and dual-exposure images with different incident spectra for material discrimination can increase patient radiation dose and be susceptible to motion artifacts and spectral resolution loss. Photon Counting Detectors (PCDs) offer a promising alternative approach, enabling acquisition of multiple energy levels in a single exposure and potentially better energy resolution. Gallium arsenide (GaAs) is particularly promisingfor breast PCD-CT due to its high quantum efficiency and reduction of fluorescence X-rays escaping the pixel within the breast imaging energy range. In this study, the spectral performance of a GaAs PCD for quantitative iodine contrast-enhanced breast CT was evaluated. A GaAs detector with a pixel size of 100 µm, a thickness of 500 µm was simulated. Simulations were performed using cylindrical phantoms of varying diameters (10 cm, 12 cm, and 16 cm) with different concentrations and locations of iodine inserts, using incident spectra of 50, 55, and 60 kVp with 2 mm of added aluminum filtration and one exposure level corresponding to a Mean Glandular Doses (MGD) of approximately 10 mGy. We accounted for the effects of beam hardening and energy detector response using TIGRE CT open-source software and the publicly available Photon Counting Toolkit (PcTK). Material-specific images of the breast were produced using both projection and image-based material decomposition methods, and iodine component images were used to estimate iodine intake. Accuracy and precision of the proposed methods forestimating iodine concentration in breast CT images were assessed for different material decomposition methods, incident spectra, and breastphantom thicknesses. The results showed that both the beam hardening effect and imperfection in the detector response had a significant impact on performance in terms of Root Mean Squared Error (RMSE), precision, and accuracy of estimati.

Experimental Setup for Evaluating Depth Sensors in Augmented Reality Technologies Used in Medical Devices.

This paper presents a fully automated experimental setup tailored for evaluating the effectiveness of augmented and virtual reality technologies in healthcare settings for regulatory purposes, with a focus on the characterization of depth sensors. The setup is constructed as a modular benchtop platform that enables quantitative analysis of depth cameras essential for extended reality technologies in a controlled environment. We detail a design concept and considerations for an experimental configuration aimed at simulating realistic scenarios for head-mounted displays. The system includes an observation platform equipped with a three-degree-of-freedom motorized system and a test object stage. To accurately replicate real-world scenarios, we utilized an array of sensors, including commonly available range-sensing cameras and commercial augmented reality headsets, notably the Intel RealSense L515 LiDAR camera, integrated into the motion control system. The paper elaborates on the system architecture and the automated data collection process. We discuss several evaluation studies performed with this setup, examining factors such as spatial resolution, Z-accuracy, and pixel-to-pixel correlation. These studies provide valuable insights into the precision and reliability of these technologies in simulated healthcare environments.

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.

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.

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.

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.

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.

[This corrects the article DOI: 10.1117/1.JMI.8.3.033504.].

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.

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.

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.

Label-free X-ray estimation of brain amyloid burden.

Amyloid plaque deposits in the brain are indicative of Alzheimer's and other diseases. Measurements of brain amyloid burden in small animals require laborious post-mortem histological analysis or resource-intensive, contrast-enhanced imaging techniques. We describe a label-free method based on spectral small-angle X-ray scattering with a polychromatic beam for in vivo estimation of brain amyloid burden. Our findings comparing 5XFAD versus wild-type mice correlate well with histology, showing promise for a fast and practical in vivo technique.

Feasibility of a label-free X-ray method to estimate brain amyloid load in small animals.

BACKGROUND: Amyloid plaque in the brain is associated with a wide range of neurodegenerative diseases such as Alzheimer's and Parkinson's and defined as aggregates of amyloid fibrils rich in ß-sheet structures. NEW METHOD: We report a label-free method based on small-angle X-ray scattering (SAXS) to estimate amyloid load in an intact mouse head with skull. The method is based on recording and analyzing the X rays elastically scattered from the ß-sheets of amyloid plaques in a mouse head at angles smaller than 10° and energies between 30 and 45 keV. The method is demonstrated by acquiring the spectral SAXS data of an amyloid model and an excised head from a wild-type mouse for 600 s. RESULTS: We captured the distinct scattering peaks of the amyloid plaques at momentum transfer (q) of 6 and 13 nm-1 associated with ß-sheet structure. We first show linear correlation between the mass fraction of the amyloid target and the area under the peak (AUP) of the scattering curve. We report results for estimating amyloid load in a fixed mouse head by recovering the characteristic scattering signal from the amyloid target situated at various locations. The coefficient of variation in the amyloid load estimate is found to be less than 10%. COMPARISON WITH EXISTING METHODS: There are no previously described label-free X-ray methods for the estimation of amyloid load in an intact head. CONCLUSIONS: We demonstrated the feasibility of a label-free method based on SAXS to potentially estimate brain amyloid in small animals.

Identification of amyloid plaques in the brain using an x-ray photon-counting strip detector.

Brain aggregates of ß amyloid (ßA) protein plaques have been widely recognized as associated with many neurodegenerative diseases, and their identification can assist in the early diagnosis of Alzheimer's disease. We investigate the feasibility of using a spectral x-ray coherent scatter system with a silicon strip photon-counting detector for identifying brain ßA protein plaques. This approach is based on differences in the structure of amyloid, white and grey matter in the brain. We simulated an energy- and angular-dispersive X-ray diffraction system with an x-ray pencil beam and Silicon strip sensor, energy-resolving detectors. The polychromatic beam is geometrically focused toward a region of interest in the brain. First, the open-source MC-GPU code for Monte Carlo transport was modified to accommodate the detector model. Second, brain phantoms with and without ßA were simulated to assess the method and determine the radiation dose required to obtain acceptable statistical power. For ßA targets of 3, 4 and 5 mm sizes in a 15-cm brain model, the required incident exposure was about 0.44 mR from a 60 kVp tungsten spectrum and 3.5 mm of added aluminum filtration. The results suggest that the proposed x-ray coherent scatter technique enables the use of high energy x-ray spectra and therefore has the potential to be used for accurate in vivo detection and quantification of ßA in the brain within acceptable radiation dose levels.

A four-alternative forced choice (4AFC) methodology for evaluating microcalcification detection in clinical full-field digital mammography (FFDM) and digital breast tomosynthesis (DBT) systems using an inkjet-printed anthropomorphic phantom.

PURPOSE: The advent of three-dimensional breast imaging systems such as digital breast tomosynthesis (DBT) has great promise for improving the detection and diagnosis of breast cancer. With these new technologies comes an essential need for testing methods to assess the resultant image quality. Although randomized clinical trials are the gold standard for assessing image quality, phantom-based studies can provide a simpler and less burdensome approach. In this work, a complete framework is presented for task-based evaluation of microcalcification (MCs) detection performance for DBT imaging systems. METHODS: The framework consists of three parts. The first part is a realistic anthropomorphic physical breast phantom created through inkjet printing, with parchment paper and iodine-doped ink. The second is a method for inserting realistic MCs fabricated from calcium hydroxyapatite. The reproducibility and stability of the phantom materials were investigated through multiple samples of parchment and ink over 6 months. The final part is an analysis using a four-alternative forced choice (4AFC) reader study. To demonstrate the framework, a task-based 4AFC study was conducted using a clinical system to compare performance from DBT, synthetic mammography (SM), and full-field digital mammography (FFDM). Nine human observers read images containing MC clusters imaged with all three modalities and tried to correctly locate the MCs. The proportion correct (PC) was measured as the number of correctly detected clusters out of all trials. RESULTS: Overall, readers scored the highest with FFDM, (PC = 0.95 ± 0.03) then DBT (0.85 ± 0.04), and finally SM (0.44 ± 0.06). For the parchment and ink samples, the linear attenuation properties were very stable over 6 months. In addition, little difference was found between the various parchment and ink samples, indicating good reproducibility. CONCLUSIONS: This framework presents a promising methodology for evaluating diagnostic task performance of clinical breast DBT systems.

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.

Extrudability analysis of drug loaded pastes for 3D printing of modified release tablets.

The rheological characteristics of pastes for 3D printing of tablets may not be described fully by the traditional rheological tests generally used for other pastes. In the present study, extrudability testing of carbopol based 3D printing pastes was performed to establish a constitutive rheological model for micro-extrusion. This model was developed for pastes that exhibit a non-linear plasto-viscoelastic behavior and follow the generalized Herschel-Bulkley flow rule. An analytical model was applied to extrudability data obtained by micro-extrusion through nozzles of 0.4 and 0.6 mm diameters. For this purpose, nineteen pastes were prepared per a fractional factorial design using various concentrations of the active ingredient and soluble and insoluble excipients. Critical material parameters (σ0, k and n) of the pastes were then calculated by analyzing extrudability data using a constitutive equation relating flow rate, nozzle and cartridge diameters, printing pressure and slip-flow angle. The accuracy of the constitutive model to predict paste yield stress, consistency and flow indices was evident by low RMSE values of 0.0691 bar, 0.034 and 6.3 bar/sn, respectively. Yield stress, flow and consistency parameters of the pastes were significantly affected by percentages of soluble and swellable excipients. The nozzle diameter had significant effect on flow index (n) but not on the consistency index (k). Hence, this study provides a mechanistic model to characterize the complex rheological behavior of pastes for 3D printing of tablets by a micro-extrusion process.

Development of mechanistic models to identify critical formulation and process variables of pastes for 3D printing of modified release tablets.

The future of pharmaceutical manufacturing may be significantly transformed by 3-dimensional (3D) printing. As an emerging technology, the indicators of quality for materials and processes used in 3D printing have not been fully established. The objective of this study was to identify the critical material attributes of semisolid paste formulations filled into cartridges for 3D printing of personalized medicine. Nineteen semisolid formulations were prepared per a fractional factorial design with three replicates of the center point. The variables investigated included percent loading of API and various soluble and insoluble excipients. Pastes were characterized for viscoelastic characteristics during the 3D printing process including creep recovery, cross-modulus and extrudability models. Packing efficiency of pastes into 3D printing cartridges was also evaluated by X-ray tomography. Changes in composition of 3D printing pastes resulted in significant variations in their viscoelastic parameters, namely their elastic deformation, flow and relaxation behaviors. The percent of soluble excipients incorporated was the most significant factor affecting the creep behavior of pastes. Cross-over stresses were assessed to indicate the minimum pressure needed for the pastes to initiate flow. Increasing solid and swellable contents of the pastes from 7% to 63% w/w increased significantly (p < 0.05) the cross-over stress from 0.93 × 10-3 Pa to 9.47 × 10-3 Pa. Increasing soluble ingredients of paste from 30% to 80% w/w was found to increase flow of the paste from 0.41 × 10-3 to 3.85 × 10-3 %/s. X-ray tomography images revealed inclusion of air bubbles during packing of pastes into cartridges. These bubbles may affect the relaxation behavior of the pastes; hence bubbles should be eliminated. This study unveiled the critical material attributes that could be controlled for consistent 3D printing by microextrusion.

Reproducing two-dimensional mammograms with three-dimensional printed phantoms.

Mammography is currently the standard imaging modality used to screen women for breast abnormalities, and, as a result, it is a tool of great importance for the early detection of breast cancer. Physical phantoms are commonly used as surrogates of breast tissue to evaluate some aspects of the performance of mammography systems. However, most phantoms do not reproduce the anatomic heterogeneity of real breasts. New fabrication technologies, such as three-dimensional (3-D) printing, have created the opportunity to build more complex, anatomically realistic breast phantoms that could potentially assist in the evaluation of mammography systems. The reproducibility and relative low cost of 3-D printed objects might also enable the development of collections of representative patient models that could be used to assess the effect of anatomical variability on system performance, hence making bench testing studies a step closer to clinical trials. The primary objective of this work is to present a simple, easily reproducible methodology to design and print 3-D objects that replicate the attenuation profile observed in real two-dimensional mammograms. The secondary objective is to evaluate the capabilities and limitations of the competing 3-D printing technologies and characterize the x-ray properties of the different materials they use. Printable phantoms can be created using the open-source code introduced, which processes a raw mammography image to estimate the amount of x-ray attenuation at each pixel, and outputs a triangle mesh object that encodes the observed attenuation map. The conversion from the observed pixel gray value to a column of printed material with equivalent attenuation requires certain assumptions and knowledge of multiple imaging system parameters, such as x-ray energy spectrum, source-to-object distance, compressed breast thickness, and average breast material attenuation. To validate the proposed methodology, x-ray projections of printed phantoms were acquired with a clinical mammography system. The quality of the printing process was evaluated by comparing the mammograms of the printed phantoms and the original mammograms used to create the phantoms. The structural similarity index and the root-mean-square error were used as objective metrics to compare the two images. A detailed description of the software, a characterization of the printed materials using x-ray spectroscopy, and an evaluation of the realism of the sample printed phantoms are presented.

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%.