Results for “digital computing” — Page 31

DOE OSTI2025

Generative diffusion model surrogates for mechanistic agent-based biological models

Mechanistic, multicellular, agent-based models are commonly used to investigate tissue, organ, and organism-scale biology at single-cell resolution. The Cellular-Potts Model (CPM) is a powerful and popular framework for developing and interrogating these models. CPMs become computationally expensive at large space- and time- scales making application and investigation of developed models difficult. Surrogate models may allow for the accelerated evaluation of CPMs of complex biological systems. However, the stochastic nature of these models means each set of parameters may give rise to different model configurations, complicating surrogate model development. In this work, we leverage denoising diffusion probabilistic models (DDPMs) to train a generative AI surrogate of a CPM used to investigate in vitro vasculogenesis. We describe the use of an image classifier to learn the characteristics that define unique areas of a 2-dimensional parameter space. We then apply this classifier to aid in surrogate model selection and verification. Our CPM model surrogate generates model configurations 20,000 timesteps ahead of a reference configuration and demonstrates approximately a 22x reduction in computational time as compared to native code execution. Our work represents a step towards the implementation of DDPMs to develop digital twins of stochastic biological systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

A comparative study of calibration techniques for finite strain elastoplasticity: Numerically-exact sensitivities for FEMU and VFM

Accurate identification of material parameters is crucial for predictive modeling in computational mechanics. Here, the two primary approaches in the experimental mechanics community for calibration from full-field digital image correlation data are known as finite element model updating (FEMU) and the virtual fields method (VFM). In VFM, the objective function is a squared mismatch between internal and external virtual work or power. In FEMU, the objective function quantifies the weighted mismatch between model predictions and corresponding experimentally measured quantities of interest. It is minimized by iteratively updating the parameters of an FE model. While FEMU is seen as more flexible, VFM is commonly used instead of FEMU due to its considerably greater computational expense. However, comparisons between the two methods usually involve approximations of gradients or sensitivities with finite difference schemes, thereby making direct assessments difficult. Hence, in this study, we compare VFM and FEMU in the context of numerically-exact sensitivities obtained through local sensitivity analyses and the application of automatic differentiation software. To this end, we conduct a series of test cases to assess both methods under practical challenges using a finite strain elastoplasticity model.

Automatic differentiation↗

DOE OSTI2015

TU-AB-207-00: Digital Tomosynthesis

Digital Tomosynthesis (DT) is becoming increasingly common in breast imaging and many other applications. DT is a form of computed tomography in which a limited set of projection images are acquired over a small angular range and reconstructed into a tomographic data set. The angular range and number of projections is determined both by the imaging task and equipment manufacturer. For example, in breast imaging between 9 and 25 projections are acquired over a range of 15° to 60°. It is equally valid to treat DT as the digital analog of classical tomography - for example, linear tomography. In fact, the name “tomosynthesis” is an acronym for “synthetic tomography”. DT shares many common features with classical tomography, including the radiographic appearance, dose, and image quality considerations. As such, both the science and practical physics of DT systems is a hybrid between CT and classical tomographic methods. This lecture will consist of three presentations that will provide a complete overview of DT, including a review of the fundamentals of DT, a discussion of testing methods for DT systems, and a description of the clinical applications of DT. While digital breast tomosynthesis will be emphasized, analogies will be drawn to body imaging to illustrate and compare tomosynthesis methods. Learning Objectives: To understand the fundamental principles behind tomosynthesis, including the determinants of image quality and dose. To learn how to test the performance of tomosynthesis imaging systems. To appreciate the uses of tomosynthesis in the clinic and the future applications of tomosynthesis.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

TU-AB-207-01: Introduction to Tomosynthesis

Digital Tomosynthesis (DT) is becoming increasingly common in breast imaging and many other applications. DT is a form of computed tomography in which a limited set of projection images are acquired over a small angular range and reconstructed into a tomographic data set. The angular range and number of projections is determined both by the imaging task and equipment manufacturer. For example, in breast imaging between 9 and 25 projections are acquired over a range of 15° to 60°. It is equally valid to treat DT as the digital analog of classical tomography - for example, linear tomography. In fact, the name “tomosynthesis” is an acronym for “synthetic tomography”. DT shares many common features with classical tomography, including the radiographic appearance, dose, and image quality considerations. As such, both the science and practical physics of DT systems is a hybrid between CT and classical tomographic methods. This lecture will consist of three presentations that will provide a complete overview of DT, including a review of the fundamentals of DT, a discussion of testing methods for DT systems, and a description of the clinical applications of DT. While digital breast tomosynthesis will be emphasized, analogies will be drawn to body imaging to illustrate and compare tomosynthesis methods. Learning Objectives: To understand the fundamental principles behind tomosynthesis, including the determinants of image quality and dose. To learn how to test the performance of tomosynthesis imaging systems. To appreciate the uses of tomosynthesis in the clinic and the future applications of tomosynthesis.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

TU-AB-207-02: Testing of Body and Breast Tomosynthesis Sytems

Digital Tomosynthesis (DT) is becoming increasingly common in breast imaging and many other applications. DT is a form of computed tomography in which a limited set of projection images are acquired over a small angular range and reconstructed into a tomographic data set. The angular range and number of projections is determined both by the imaging task and equipment manufacturer. For example, in breast imaging between 9 and 25 projections are acquired over a range of 15° to 60°. It is equally valid to treat DT as the digital analog of classical tomography - for example, linear tomography. In fact, the name “tomosynthesis” is an acronym for “synthetic tomography”. DT shares many common features with classical tomography, including the radiographic appearance, dose, and image quality considerations. As such, both the science and practical physics of DT systems is a hybrid between CT and classical tomographic methods. This lecture will consist of three presentations that will provide a complete overview of DT, including a review of the fundamentals of DT, a discussion of testing methods for DT systems, and a description of the clinical applications of DT. While digital breast tomosynthesis will be emphasized, analogies will be drawn to body imaging to illustrate and compare tomosynthesis methods. Learning Objectives: To understand the fundamental principles behind tomosynthesis, including the determinants of image quality and dose. To learn how to test the performance of tomosynthesis imaging systems. To appreciate the uses of tomosynthesis in the clinic and the future applications of tomosynthesis.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

TU-AB-207-03: Tomosynthesis: Clinical Applications

Digital Tomosynthesis (DT) is becoming increasingly common in breast imaging and many other applications. DT is a form of computed tomography in which a limited set of projection images are acquired over a small angular range and reconstructed into a tomographic data set. The angular range and number of projections is determined both by the imaging task and equipment manufacturer. For example, in breast imaging between 9 and 25 projections are acquired over a range of 15° to 60°. It is equally valid to treat DT as the digital analog of classical tomography - for example, linear tomography. In fact, the name “tomosynthesis” is an acronym for “synthetic tomography”. DT shares many common features with classical tomography, including the radiographic appearance, dose, and image quality considerations. As such, both the science and practical physics of DT systems is a hybrid between CT and classical tomographic methods. This lecture will consist of three presentations that will provide a complete overview of DT, including a review of the fundamentals of DT, a discussion of testing methods for DT systems, and a description of the clinical applications of DT. While digital breast tomosynthesis will be emphasized, analogies will be drawn to body imaging to illustrate and compare tomosynthesis methods. Learning Objectives: To understand the fundamental principles behind tomosynthesis, including the determinants of image quality and dose. To learn how to test the performance of tomosynthesis imaging systems. To appreciate the uses of tomosynthesis in the clinic and the future applications of tomosynthesis.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

SU-E-J-119: Head-And-Neck Digital Phantoms for Geometric and Dosimetric Uncertainty Evaluation of CT-CBCT Deformable Image Registration

Purpose: To evaluate geometric and dosimetric uncertainties of CT-CBCT deformable image registration (DIR) algorithms using digital phantoms generated from real patients. Methods: We selected ten H&N cancer patients with adaptive IMRT. For each patient, a planning CT (CT1), a replanning CT (CT2), and a pretreatment CBCT (CBCT1) were used as the basis for digital phantom creation. Manually adjusted meshes were created for selected ROIs (e.g. PTVs, brainstem, spinal cord, mandible, and parotids) on CT1 and CT2. The mesh vertices were input into a thin-plate spline algorithm to generate a reference displacement vector field (DVF). The reference DVF was applied to CBCT1 to create a simulated mid-treatment CBCT (CBCT2). The CT-CBCT digital phantom consisted of CT1 and CBCT2, which were linked by the reference DVF. Three DIR algorithms (Demons, B-Spline, and intensity-based) were applied to these ten digital phantoms. The images, ROIs, and volumetric doses were mapped from CT1 to CBCT2 using the DVFs computed by these three DIRs and compared to those mapped using the reference DVF. Results: The average Dice coefficients for selected ROIs were from 0.83 to 0.94 for Demons, from 0.82 to 0.95 for B-Spline, and from 0.67 to 0.89 for intensity-based DIR. The average Hausdorff distances for selected ROIs were from 2.4 to 6.2 mm for Demons, from 1.8 to 5.9 mm for B-Spline, and from 2.8 to 11.2 mm for intensity-based DIR. The average absolute dose errors for selected ROIs were from 0.7 to 2.1 Gy for Demons, from 0.7 to 2.9 Gy for B- Spline, and from 1.3 to 4.5 Gy for intensity-based DIR. Conclusion: Using clinically realistic CT-CBCT digital phantoms, Demons and B-Spline were shown to have similar geometric and dosimetric uncertainties while intensity-based DIR had the worst uncertainties. CT-CBCT DIR has the potential to provide accurate CBCT-based dose verification for H&N adaptive radiotherapy. Z Shen: None; K Bzdusek: an employee of Philips Healthcare; S Koyfman: None; P Xia: received research grants from Philips Healthcare and Siemens Healthcare.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2020

Destructive Error Interference in Product-Formula Lattice Simulation

Quantum computers can efficiently simulate the dynamics of quantum systems. Here, we study the cost of digitally simulating the dynamics of several physically relevant systems using the first-order product-formula algorithm. We show that the errors from different Trotterization steps in the algorithm can interfere destructively, yielding a much smaller error than previously estimated. In particular, we prove that the total error in simulating a nearest-neighbor interacting system of n sites for time t using the first-order product formula with r time slices is O ( n t / r + n t 3 / r 2 ) when n t 2 / r is less than a small constant. Given an error tolerance ϵ , the error bound yields an estimate of max { O ( n 2 t / ϵ ) , O ( n 2 t 3 / 2 / ϵ 1 / 2 ) } for the total gate count of the simulation. The estimate is tighter than previous bounds and matches the empirical performance observed in Childs et al. [ Proc. Natl. Acad. Sci. U.S.A. 115 , 9456 (2018) ]. We also provide numerical evidence for potential improvements and conjecture an even tighter estimate for the gate count.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Artificial Intelligence for Multiphysics Nuclear Design Optimization with Additive Manufacturing

The geometric flexibility of additively manufactured metals and ceramics generates a very large and open design space that requires advanced modeling and simulation tools for physics simulations and the rigorous definition of design problems. This effort deploys artificial intelligence (AI) and machine learning (ML) algorithms to understand the design space, evaluate potential designs, and more efficiently generate optimized results. The Transformational Challenge Reactor (TCR) program is leveraging advances in several scientific areas—including materials, manufacturing, sensors and control systems, data analytics, and high-fidelity modeling and simulation—to accelerate the design, manufacturing, qualification, and deployment of advanced nuclear energy systems. Through a manufacturing-informed design approach, the TCR program seeks to integrate digital data for rapid nuclear innovation; accelerate the adoption of advances in manufacturing, materials, and computational sciences for nuclear applications; and dramatically reduce deployment costs and timelines for new nuclear reactor technologies. This report documents efforts under the TCR program to leverage advanced modeling and simulation techniques driven by AI/ML algorithms on high-performance computing (HPC) systems to yield more optimized TCR core designs. A multiphysics ML surrogate model was developed to run on the HPC architectures. The surrogate model is trained on high-fidelity simulation data of coupled neutronics and thermofluidics and is used to quickly evaluate thousands of candidate core designs in parallel, which drives the evolution of the cooling channel shapes to minimize temperature peaking and material stress. Outcomes from these activities provide design information and feedback into the core design efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

Unsupervised, supervised and reinforced learning via spiking computation

The present invention relates to unsupervised, supervised and reinforced learning via spiking computation. The neural network comprises a plurality of neural modules. Each neural module comprises multiple digital neurons such that each neuron in a neural module has a corresponding neuron in another neural module. An interconnection network comprising a plurality of edges interconnects the plurality of neural modules. Each edge interconnects a first neural module to a second neural module, and each edge comprises a weighted synaptic connection between every neuron in the first neural module and a corresponding neuron in the second neural module.

Modha, Dharmendra S.↗

DOE OSTI2022

General overview of multiphysics modeling and simulation at CEA/DES/IRESNE supporting safety, operating and development of nuclear reactors and facilities

This paper presents a general overview of the innovative multiphysics research/development activities carried on by the CEA/DES/IRESNE institute. Eight main different multiphysics fields addressed by IRESNE have been listed (among others: fuel fabrication, behavior of fuels, of reactors, of corium, radionuclide transfer to environment,...) in relation with the involved coupled physics: neutron transport, fluid mechanics, electro-magnetism, heat transfer, solid mechanics and (physico-)chemistry. The major mission of IRESNE institute is to study current and future nuclear systems integrated into a low carbon energy system. To fulfill this mission, IRESNE's activities rely on the development and the implementation of multiphysics modeling, advanced coupling scheme, data transfer techniques and dedicated multiphysics experimentations. Indeed, such multiphysics simulation approach of nuclear systems is today required to deal with the complex intrinsic features of studied phenomena. This approach aims at taking into account physical phenomena interactions, at each interest modeling scale, in order to reach an accurate and predictive behavior representation of a system component or of the whole system. The final objective is to contribute to the development of digital reactor, core and fuel at CEA, by progressing on applied mathematics, high-performance computing and physical mechanism understanding.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2022

A Dataset of 3D Structural and Simulated Transport Properties of Complex Porous Media

Physical processes that occur within porous materials have wide-ranging applications including - but not limited to - carbon sequestration, battery technology, membranes, oil and gas, geothermal energy, nuclear waste disposal, water resource management. The equations that describe these physical processes have been studied extensively; however, approximating them numerically requires immense computational resources due to the complex behavior that arises from the geometrically-intricate solid boundary conditions in porous materials. Here, we introduce a new dataset of unprecedented scale and breadth, DRP-372: a catalog of 3D geometries, simulation results, and structural properties of samples hosted on the Digital Rocks Portal. The dataset includes 1736 flow and electrical simulation results on 217 samples, which required more than 500 core years of computation. This data can be used for many purposes, such as constructing empirical models, validating new simulation codes, and developing machine learning algorithms that closely match the extensive purely-physical simulation. This article offers a detailed description of the contents of the dataset including the data collection, simulation schemes, and data validation.

3D images↗

DOE OSTI2016

MO-DE-209-04: Radiation Dosimetry in Breast Tomosynthesis

Digital Breast Tomosynthesis (DBT) is rapidly replacing mammography as the standard of care in breast cancer screening and diagnosis. DBT is a form of computed tomography, in which a limited set of projection images are acquired over a small angular range and reconstructed into tomographic data. The angular range varies from 15° to 50° and the number of projections varies between 9 and 25 projections, as determined by the equipment manufacturer. It is equally valid to treat DBT as the digital analog of classical tomography – that is, linear tomography. In fact, the name “tomosynthesis” stands for “synthetic tomography.” DBT shares many common features with classical tomography, including the radiographic appearance, dose, and image quality considerations. As such, both the science and practical physics of DBT systems is a hybrid between computed tomography and classical tomographic methods. In this lecture, we will explore the continuum from radiography to computed tomography to illustrate the characteristics of DBT. This lecture will consist of four presentations that will provide a complete overview of DBT, including a review of the fundamentals of DBT acquisition, a discussion of DBT reconstruction methods, an overview of dosimetry for DBT systems, and summary of the underlying image theory of DBT thereby relating image quality and dose. Learning Objectives: To understand the fundamental principles behind tomosynthesis image acquisition. To understand the fundamentals of tomosynthesis image reconstruction. To learn the determinants of image quality and dose in DBT, including measurement techniques. To learn the image theory underlying tomosynthesis, and the relationship between dose and image quality. ADM is a consultant to, and holds stock in, Real Time Tomography, LLC. ADM receives research support from Hologic Inc., Analogic Inc., and Barco NV.; ADM is a member of the Scientific Advisory Board for Gamma Medica Inc.; A. Maidment, Research Support, Hologic, Inc.; Research Support, Barco, Inc.; Scientific Advisory Board, Gamma Medica, Inc.; Scientific Advisory Board, Real-Time Tomography, LLC.; Shareholder, Real-Time Tomography, LLC; J. Mainprize, Our lab has a research agreement with GE Healthcare on various topics in digital mammography and digital tomosynthesis; W. Zhao, Research grant from Siemens Health Care.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

MO-DE-209-00: DBT Physics Basic to Advanced

Digital Breast Tomosynthesis (DBT) is rapidly replacing mammography as the standard of care in breast cancer screening and diagnosis. DBT is a form of computed tomography, in which a limited set of projection images are acquired over a small angular range and reconstructed into tomographic data. The angular range varies from 15° to 50° and the number of projections varies between 9 and 25 projections, as determined by the equipment manufacturer. It is equally valid to treat DBT as the digital analog of classical tomography – that is, linear tomography. In fact, the name “tomosynthesis” stands for “synthetic tomography.” DBT shares many common features with classical tomography, including the radiographic appearance, dose, and image quality considerations. As such, both the science and practical physics of DBT systems is a hybrid between computed tomography and classical tomographic methods. In this lecture, we will explore the continuum from radiography to computed tomography to illustrate the characteristics of DBT. This lecture will consist of four presentations that will provide a complete overview of DBT, including a review of the fundamentals of DBT acquisition, a discussion of DBT reconstruction methods, an overview of dosimetry for DBT systems, and summary of the underlying image theory of DBT thereby relating image quality and dose. Learning Objectives: To understand the fundamental principles behind tomosynthesis image acquisition. To understand the fundamentals of tomosynthesis image reconstruction. To learn the determinants of image quality and dose in DBT, including measurement techniques. To learn the image theory underlying tomosynthesis, and the relationship between dose and image quality. ADM is a consultant to, and holds stock in, Real Time Tomography, LLC. ADM receives research support from Hologic Inc., Analogic Inc., and Barco NV.; ADM is a member of the Scientific Advisory Board for Gamma Medica Inc.; A. Maidment, Research Support, Hologic, Inc.; Research Support, Barco, Inc.; Scientific Advisory Board, Gamma Medica, Inc.; Scientific Advisory Board, Real-Time Tomography, LLC.; Shareholder, Real-Time Tomography, LLC; J. Mainprize, Our lab has a research agreement with GE Healthcare on various topics in digital mammography and digital tomosynthesis; W. Zhao, Research grant from Siemens Health Care.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

MO-DE-209-01: Primer On Tomosynthesis

Digital Breast Tomosynthesis (DBT) is rapidly replacing mammography as the standard of care in breast cancer screening and diagnosis. DBT is a form of computed tomography, in which a limited set of projection images are acquired over a small angular range and reconstructed into tomographic data. The angular range varies from 15° to 50° and the number of projections varies between 9 and 25 projections, as determined by the equipment manufacturer. It is equally valid to treat DBT as the digital analog of classical tomography – that is, linear tomography. In fact, the name “tomosynthesis” stands for “synthetic tomography.” DBT shares many common features with classical tomography, including the radiographic appearance, dose, and image quality considerations. As such, both the science and practical physics of DBT systems is a hybrid between computed tomography and classical tomographic methods. In this lecture, we will explore the continuum from radiography to computed tomography to illustrate the characteristics of DBT. This lecture will consist of four presentations that will provide a complete overview of DBT, including a review of the fundamentals of DBT acquisition, a discussion of DBT reconstruction methods, an overview of dosimetry for DBT systems, and summary of the underlying image theory of DBT thereby relating image quality and dose. Learning Objectives: To understand the fundamental principles behind tomosynthesis image acquisition. To understand the fundamentals of tomosynthesis image reconstruction. To learn the determinants of image quality and dose in DBT, including measurement techniques. To learn the image theory underlying tomosynthesis, and the relationship between dose and image quality. ADM is a consultant to, and holds stock in, Real Time Tomography, LLC. ADM receives research support from Hologic Inc., Analogic Inc., and Barco NV.; ADM is a member of the Scientific Advisory Board for Gamma Medica Inc.; A. Maidment, Research Support, Hologic, Inc.; Research Support, Barco, Inc.; Scientific Advisory Board, Gamma Medica, Inc.; Scientific Advisory Board, Real-Time Tomography, LLC.; Shareholder, Real-Time Tomography, LLC; J. Mainprize, Our lab has a research agreement with GE Healthcare on various topics in digital mammography and digital tomosynthesis; W. Zhao, Research grant from Siemens Health Care.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

MO-DE-209-02: Tomosynthesis Reconstruction Methods

Digital Breast Tomosynthesis (DBT) is rapidly replacing mammography as the standard of care in breast cancer screening and diagnosis. DBT is a form of computed tomography, in which a limited set of projection images are acquired over a small angular range and reconstructed into tomographic data. The angular range varies from 15° to 50° and the number of projections varies between 9 and 25 projections, as determined by the equipment manufacturer. It is equally valid to treat DBT as the digital analog of classical tomography – that is, linear tomography. In fact, the name “tomosynthesis” stands for “synthetic tomography.” DBT shares many common features with classical tomography, including the radiographic appearance, dose, and image quality considerations. As such, both the science and practical physics of DBT systems is a hybrid between computed tomography and classical tomographic methods. In this lecture, we will explore the continuum from radiography to computed tomography to illustrate the characteristics of DBT. This lecture will consist of four presentations that will provide a complete overview of DBT, including a review of the fundamentals of DBT acquisition, a discussion of DBT reconstruction methods, an overview of dosimetry for DBT systems, and summary of the underlying image theory of DBT thereby relating image quality and dose. Learning Objectives: To understand the fundamental principles behind tomosynthesis image acquisition. To understand the fundamentals of tomosynthesis image reconstruction. To learn the determinants of image quality and dose in DBT, including measurement techniques. To learn the image theory underlying tomosynthesis, and the relationship between dose and image quality. ADM is a consultant to, and holds stock in, Real Time Tomography, LLC. ADM receives research support from Hologic Inc., Analogic Inc., and Barco NV.; ADM is a member of the Scientific Advisory Board for Gamma Medica Inc.; A. Maidment, Research Support, Hologic, Inc.; Research Support, Barco, Inc.; Scientific Advisory Board, Gamma Medica, Inc.; Scientific Advisory Board, Real-Time Tomography, LLC.; Shareholder, Real-Time Tomography, LLC; J. Mainprize, Our lab has a research agreement with GE Healthcare on various topics in digital mammography and digital tomosynthesis; W. Zhao, Research grant from Siemens Health Care.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

MO-DE-209-03: Assessing Image Quality

Digital Breast Tomosynthesis (DBT) is rapidly replacing mammography as the standard of care in breast cancer screening and diagnosis. DBT is a form of computed tomography, in which a limited set of projection images are acquired over a small angular range and reconstructed into tomographic data. The angular range varies from 15° to 50° and the number of projections varies between 9 and 25 projections, as determined by the equipment manufacturer. It is equally valid to treat DBT as the digital analog of classical tomography – that is, linear tomography. In fact, the name “tomosynthesis” stands for “synthetic tomography.” DBT shares many common features with classical tomography, including the radiographic appearance, dose, and image quality considerations. As such, both the science and practical physics of DBT systems is a hybrid between computed tomography and classical tomographic methods. In this lecture, we will explore the continuum from radiography to computed tomography to illustrate the characteristics of DBT. This lecture will consist of four presentations that will provide a complete overview of DBT, including a review of the fundamentals of DBT acquisition, a discussion of DBT reconstruction methods, an overview of dosimetry for DBT systems, and summary of the underlying image theory of DBT thereby relating image quality and dose. Learning Objectives: To understand the fundamental principles behind tomosynthesis image acquisition. To understand the fundamentals of tomosynthesis image reconstruction. To learn the determinants of image quality and dose in DBT, including measurement techniques. To learn the image theory underlying tomosynthesis, and the relationship between dose and image quality. ADM is a consultant to, and holds stock in, Real Time Tomography, LLC. ADM receives research support from Hologic Inc., Analogic Inc., and Barco NV.; ADM is a member of the Scientific Advisory Board for Gamma Medica Inc.; A. Maidment, Research Support, Hologic, Inc.; Research Support, Barco, Inc.; Scientific Advisory Board, Gamma Medica, Inc.; Scientific Advisory Board, Real-Time Tomography, LLC.; Shareholder, Real-Time Tomography, LLC; J. Mainprize, Our lab has a research agreement with GE Healthcare on various topics in digital mammography and digital tomosynthesis; W. Zhao, Research grant from Siemens Health Care.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

Unitary qubit lattice algorithm for three-dimensional vortex solitons in hyperbolic self-defocusing media

To obtain stable vortex structures in three-dimensional (3D) nonlinear optics, Efremidis et al. (2007) have introduced a generalized Nonlinear Schrodinger equation (NLS) in which the transverse quantum vortex components are stabilized by a longitudinal bright soliton: i.e., the elliptic operator ∇ 2 is replaced by its hyperbolic counterpart ∇ ⊥ 2 - ∂ 2 / ∂ z 2 . A new 3D mesoscopic qubit unitary lattice algorithm is developed for this generalized NLS. One introduces 2 qubits for each lattice site and entangles them with a local unitary collision operator. This entanglement is then spread throughout the lattice by nearest neighbor streaming. These interwined operators lead to an extremely well parallelized code on classical supcomputers while their unitary structure will permit encoding onto a quantum computer. Somewhat unexpectedly, the hyperbolic operator can be realized from variations in the collision operator, without introducing variations in the streaming operator. The initial line vortices are generated by Pade asymptotics. The energy constraint is conserved to 10 digit accuracy.

3D solitons↗