Nooshin Kiarashi

Development of realistic physical breast phantoms matched to virtual breast phantoms based on human subject data

PURPOSE Physical phantoms are essential for the development, optimization, and evaluation of x-ray breast imaging systems. Recognizing the major effect of anatomy on image quality and clinical performance, such phantoms should ideally reflect the three-dimensional structure of the human breast. Currently, there is no commercially available three-dimensional physical breast phantom that is anthropomorphic. The authors present the development of a new suite of physical breast phantoms based on human data. METHODS The phantoms were designed to match the extended cardiac-torso virtual breast phantoms that were based on dedicated breast computed tomography images of human subjects. The phantoms were fabricated by high-resolution multimaterial additive manufacturing (3D printing) technology. The glandular equivalency of the photopolymer materials was measured relative to breast tissue-equivalent plastic materials. Based on the current state-of-the-art in the technology and available materials, two variations were fabricated. The first was a dual-material phantom, the Doublet. Fibroglandular tissue and skin were represented by the most radiographically dense material available; adipose tissue was represented by the least radiographically dense material. The second variation, the Singlet, was fabricated with a single material to represent fibroglandular tissue and skin. It was subsequently filled with adipose-equivalent materials including oil, beeswax, and permanent urethane-based polymer. Simulated microcalcification clusters were further included in the phantoms via crushed eggshells. The phantoms were imaged and characterized visually and quantitatively. RESULTS The mammographic projections and tomosynthesis reconstructed images of the fabricated phantoms yielded realistic breast background. The mammograms of the phantoms demonstrated close correlation with simulated mammographic projection images of the corresponding virtual phantoms. Furthermore, power-law descriptions of the phantom images were in general agreement with real human images. The Singlet approach offered more realistic contrast as compared to the Doublet approach, but at the expense of air bubbles and air pockets that formed during the filling process. CONCLUSIONS The presented physical breast phantoms and their matching virtual breast phantoms offer realistic breast anatomy, patient variability, and ease of use, making them a potential candidate for performing both system quality control testing and virtual clinical trials.

Development and Application of a Suite of 4-D Virtual Breast Phantoms for Optimization and Evaluation of Breast Imaging Systems

Mammography is currently the most widely utilized tool for detection and diagnosis of breast cancer. However, in women with dense breast tissue, tissue overlap may obscure lesions. Digital breast tomosynthesis can reduce tissue overlap. Furthermore, imaging with contrast enhancement can provide additional functional information about lesions, such as morphology and kinetics, which in turn may improve lesion identification and characterization. The performance of these imaging techniques is strongly dependent on the structural composition of the breast, which varies significantly among patients. Therefore, imaging system and imaging technique optimization should take patient variability into consideration. Furthermore, optimization of imaging techniques that employ contrast agents should include the temporally varying breast composition with respect to the contrast agent uptake kinetics. To these ends, we have developed a suite of 4-D virtual breast phantoms, which are incorporated with the kinetics of contrast agent propagation in different tissues and can realistically model normal breast parenchyma as well as benign and malignant lesions. This development presents a new approach in performing simulation studies using truly anthropomorphic models. To demonstrate the utility of the proposed 4-D phantoms, we present a simplified example study to compare the performance of 14 imaging paradigms qualitatively and quantitatively.

Decentralized Online Convex Programming with local information

This paper describes a novel approach to decentralized online optimization in a large network of agents. At each stage, the agents face a new objective function that reflects the effects of a changing environment, and each agent can share information pertaining to past decisions and cost functions only with his neighbors. These operating conditions arise in many practical applications, but introduce challenging questions related to the performance of distributed strategies relative to impractical centralized approaches. The proposed algorithm yields small regret (i.e., the difference between the total cost incurred using causally available information and the total cost that would have been incurred in hindsight had all the relevant information been available all at once) and is robust to evolving network topologies. It combines a subgradient-based sequential convex optimization scheme with decentralized decision-making via approximate dynamic programming.

Finite‐element modeling of compression and gravity on a population of breast phantoms for multimodality imaging simulation

PURPOSE The authors are developing a series of computational breast phantoms based on breast CT data for imaging research. In this work, the authors develop a program that will allow a user to alter the phantoms to simulate the effect of gravity and compression of the breast (craniocaudal or mediolateral oblique) making the phantoms applicable to multimodality imaging. METHODS This application utilizes a template finite-element (FE) breast model that can be applied to their presegmented voxelized breast phantoms. The FE model is automatically fit to the geometry of a given breast phantom, and the material properties of each element are set based on the segmented voxels contained within the element. The loading and boundary conditions, which include gravity, are then assigned based on a user-defined position and compression. The effect of applying these loads to the breast is computed using a multistage contact analysis in FEBio, a freely available and well-validated FE software package specifically designed for biomedical applications. The resulting deformation of the breast is then applied to a boundary mesh representation of the phantom that can be used for simulating medical images. An efficient script performs the above actions seamlessly. The user only needs to specify which voxelized breast phantom to use, the compressed thickness, and orientation of the breast. RESULTS The authors utilized their FE application to simulate compressed states of the breast indicative of mammography and tomosynthesis. Gravity and compression were simulated on example phantoms and used to generate mammograms in the craniocaudal or mediolateral oblique views. The simulated mammograms show a high degree of realism illustrating the utility of the FE method in simulating imaging data of repositioned and compressed breasts. CONCLUSIONS The breast phantoms and the compression software can become a useful resource to the breast imaging research community. These phantoms can then be used to evaluate and compare imaging modalities that involve different positioning and compression of the breast.

Population of 100 Realistic, Patient-Based Computerized Breast Phantoms for Multi-modality Imaging Research

Breast imaging is an important area of research with many new techniques being investigated to further reduce the morbidity and mortality of breast cancer through early detection. Computerized phantoms can provide an essential tool to quantitatively compare new imaging systems and techniques. Current phantoms, however, lack sufficient realism in depicting the complex 3D anatomy of the breast. In this work, we created one-hundred realistic and detailed 3D computational breast phantoms based on high-resolution CT datasets from normal patients. We also developed a finiteelement application to simulate different compression states of the breast, making the phantoms applicable to multimodality imaging research. The breast phantoms and tools developed in this work were packaged into user-friendly software applications to distribute for breast imaging research.

A quantitative metrology for performance characterization of five breast tomosynthesis systems based on an anthropomorphic phantom

PURPOSE In medical imaging systems, proper rendition of anatomy is essential in discerning normal tissue from disease. Currently, digital breast tomosynthesis (DBT) systems are evaluated using subjective evaluation of lesion visibility in uniform phantoms. This study involved the development of a new methodology to objectively measure the rendition of a 3D breast model by an anthropomorphic breast phantom, and its implementation on five clinical DBT systems of different makes and models. METHODS A 3D, patient-based breast phantom was fabricated based on XCAT breast models. This phantom was imaged on representative breast tomosynthesis systems. The ability of tomosynthesis systems to accurately reproduce the 3D structure of the breast was assessed by computational analysis of the resultant images in terms of three groups of indices: contrast index (CI), reflective of local difference between adipose and glandular material; adipose variability index (AVI), reflective of contributions of noise and artifacts within uniform adipose regions; and contrast detectability, which describes contrast against local background variability and is described by contrast variability index (CVI), coefficient of variation (COV), contrast to adipose variability index (CAVI), and contrast to noise ratio index (CNRI). The indices were obtained by comparing the image data to the gold standard 3D distribution of breast tissue in the model. Corresponding indices were measured within variable region of interest (ROI) sizes ranging from 10 to 37 mm. The characterization was performed on five tomosynthesis systems: Fuji Aspire Crystal, GE Essential, Hologic Dimension, IMS Giotto, and Siemens Inspiration, all evaluated at a fixed dose of 1.5 mGy average glandular dose, anonymized in random order from A to E. RESULTS Results are provided as a function of ROI size. The systems ranked orders in terms of CI with values of 7.4%, 7.0%, 6.9%, 6.4%, and 5.2% for systems A-E, respectively. This system ranking was identical for CNRI. Both CI and CNRI were constant over ROI size. The ranking was similar for CVI. The COV also changed little with ROI size and was similar across systems. For 10 mm ROIs, the average system COV was 0.7, which reduced to 0.5 with 37 mm ROIs. Two systems (A and B) exhibited highest AVI values when measured in 10 mm ROIs. This, however, was ROI-size-dependent with the three other systems (C-E) yielding higher AVI values when measured with 37 mm ROIs. Two systems (B and E) showed inferior CAVI compared to others. CONCLUSIONS The quality of rendition tracked with differences in image appearance across systems. The findings illustrate that the anthropomorphic phantom can be used as a basis to extract quantitative values of image attributes in DBT.

Development of matched virtual and physical breast phantoms based on patient data

Physical phantoms are essential for the development, optimization, and clinical evaluation of x-ray systems. These phantoms are used for various tests such as quality assurance testing, system characterization, reconstruction evaluation, and dosimetry. They should ideally be capable of serving as ground truth for purposes such as virtual clinical trials. Currently, there is no anthropomorphic 3D physical phantom commercially available. We present our development of a new suite of physical breast phantoms based on real patient data. The phantoms were generated from the NURBS-based extended cardiac-torso (XCAT) breast phantoms, which were segmented from patient dedicated breast computed tomography data. High-resolution multi-material 3D printing technology was used to fabricate the physical models. Glandular tissue and skin were presented by the most radiographically dense photopolymer available to the printer, mimicking a 75% glandular tissue. Adipose tissue was presented by the least radiographically dense photopolymer, mimicking a 35% glandular tissue. The glandular equivalency was measured by comparing x-ray images of samples of the photopolymers available to the printer with those of breast tissue-equivalent materials. The mammographic projections and tomosynthesis reconstructed images of fabricated models showed great improvement over available phantoms, presenting a more realistic breast background.

Development of a dynamic 4D anthropomorphic breast phantom for contrast-based breast imaging

Mammography is currently the most widely accepted tool for detection and diagnosis of breast cancer. However, the sensitivity of mammography is reduced in women with dense breast tissue due to tissue overlap, which may obscure lesions. Digital breast tomosynthesis with contrast enhancement reduces tissue overlap and provides additional functional information about lesions (i.e. morphology and kinetics), which in turn may improve lesion characterization. The performance of such techniques is highly dependent on the structural composition of the breast, which varies significantly across patients. Therefore, optimization of breast imaging systems should be done with respect to this patient versatility. Furthermore, imaging techniques that employ contrast require the inclusion of a temporally varying breast composition with respect to the contrast agent kinetics to enable the optimization of the system. To these ends, we have developed a dynamic 4D anthropomorphic breast phantom, which can be used for optimizing a breast imaging system by incorporating material characteristics. The presented dynamic phantom is based on two recently developed anthropomorphic breast phantoms, which can be representative of a whole population through their randomized anatomical feature generation and various compression levels. The 4D dynamic phantom is incorporated with the kinetics of contrast agent uptake in different tissues and can realistically model benign and malignant lesions. To demonstrate the utility of the proposed dynamic phantom, contrast-enhanced digital mammography and breast tomosynthesis were simulated where a ray-tracing algorithm emulated the projections, a filtered back projection algorithm was used for reconstruction, and dual-energy and temporal subtractions were performed and compared.

MO-A-141-02: Session In Memory of Fearghus O't Foghludha - Virtual Tools for Validation of X-Ray Breast Imaging Systems

Given the large number of design parameters available to an x-ray imaging system innovator (x-ray spectrum, number of projection angles, angular range, dose and reconstruction algorithm, to name a few), simulation methods offer significant advantages over clinical studies for evaluation of the alternatives in terms of reproducibility, reduced radiation exposures of patients and volunteers, a known reference standard, and the capability for studying patient and disease subpopulations through appropriate choice of breast and patient model parameters. For these reasons, the emergent use of Virtual Clinical Trials for preclinical assessment of x-ray breast imaging methods has introduced a demand to optimize protocols for simulation studies. The protocols should comprise of specifications for breast phantoms, the simulated data representation, methods for incorporating phantoms into a model for the imaging process, and appropriate statistical assessment methods applied to the resulting simulated images. This symposium explores simulation methods for evaluation of novel x-ray breast imaging systems - the subject of recently established AAPM taskgroup TG234. The focus is on the various approaches of developing software anthropomorphic phantoms and their use in the statistical assessment of novel imaging systems along with computational models for the x-ray image formation process. The symposium will review and discuss the state of the science of Virtual Clinical Trials for novel x-ray breast imaging systems. Presentations will discuss real-time simulation of breast anatomy, simulation of small-scale tissue properties, generation of breast phantoms based upon clinical datasets, and task-based evaluation of x-ray breast imaging systems using software phantoms. LEARNING OBJECTIVES 1. Review current approaches for simulation of breast phantoms for x-ray imaging, along with their strengths and limitations; 2. Understand the validation of software phantoms using object and image statistics; 3. Review methods for statistical and task-based image quality assessment and discuss their applications in x-ray breast imaging systems; 4. Illustrate methods for performing virtual clinical trials of emerging 3D x-ray breast imaging systems. P.R.B. is a PI on NSF grant IIS0916690 and co-investigator on NIH R01 CA154444, NIH R21 CA155906-01A1, and DoD HBCU PTA BC083639, and is also involved in research collaboration with Barco, Inc. and Hologic, Inc. N.K. is funded by NIH R01 CA134658 and R01 CA11243, and is involved in resaerch collaboration with Siement Medical Solution.

Impact of breast structure on lesion detection in breast tomosynthesis, a simulation study.

Abstract. This study aims to characterize the effect of background tissue density and heterogeneity on the detection of irregular masses in breast tomosynthesis, while demonstrating the capability of the sophisticated tools that can be used in the design, implementation, and performance analysis of virtual clinical trials (VCTs). Twenty breast phantoms from the extended cardiac-torso (XCAT) family, generated based on dedicated breast computed tomography of human subjects, were used to extract a total of 2173 volumes of interest (VOIs) from simulated tomosynthesis images. Five different lesions, modeled after human subject tomosynthesis images, were embedded in the breasts and combined with the lesion absent condition yielded a total of 6×2173 VOIs. Effects of background tissue density and heterogeneity on the detection of the lesions were studied by implementing a composite hypothesis signal detection paradigm with location known exactly, lesion known exactly or statistically, and background known statistically. Using the area under the receiver operating characteristic curve, detection performance deteriorated as density was increased, yielding findings consistent with clinical studies. A human observer study was performed on a subset of the simulated tomosynthesis images, confirming the detection performance trends with respect to density and serving as a validation of the implemented detector. Performance of the implemented detector varied substantially across the 20 breasts. Furthermore, background tissue density and heterogeneity affected the log-likelihood ratio test statistic differently under lesion absent and lesion present conditions. Therefore, considering background tissue variability in tissue models can change the outcomes of a VCT and is hence of crucial importance. The XCAT breast phantoms have the potential to address this concern by offering realistic modeling of background tissue variability based on a wide range of human subjects, comprising various breast shapes, sizes, and densities.