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DOE OSTI2022

AMM: Adaptive Multilinear Meshes

Adaptive representations are increasingly indispensable for reducing the in-memory and on-disk footprints of large-scale data. Usual solutions are designed broadly along two themes: reducing data precision, e.g., through compression, or adapting data resolution, e.g., using spatial hierarchies. Additionally, recent research suggests that combining the two approaches, i.e., adapting both resolution and precision simultaneously, can offer significant gains over using them individually. However, there currently exist no practical solutions to creating and evaluating such representations at scale. In this work, we present a new resolution-precision-adaptive representation to support hybrid data reduction schemes and offer an interface to existing tools and algorithms. Through novelties in spatial hierarchy, our representation, Adaptive Multilinear Meshes (AMM), provides considerable reduction in the mesh size. AMM creates a piecewise multilinear representation of uniformly sampled scalar data and can selectively relax or enforce constraints on conformity, continuity, and coverage, delivering a flexible adaptive representation. AMM also supports representing the function using mixed-precision values to further the achievable gains in data reduction. We describe a practical approach to creating AMM incrementally using arbitrary orderings of data and demonstrate AMM on six types of resolution and precision datastreams. By interfacing with state-of-the-art rendering tools through VTK, we demonstrate the practical and computational advantages of our representation for visualization techniques. With an open-source release of our tool to create AMM, we make such evaluation of data reduction accessible to the community, which we hope will foster new opportunities and future data reduction schemes.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

A Hybrid Lagrangian/Eulerian Collocated Velocity Advection and Projection Method for Fluid Simulation

Abstract We present a hybrid particle/grid approach for simulating incompressible fluids on collocated velocity grids. Our approach supports both particle‐based Lagrangian advection in very detailed regions of the flow and efficient Eulerian grid‐based advection in other regions of the flow. A novel Backward Semi‐Lagrangian method is derived to improve accuracy of grid based advection. Our approach utilizes the implicit formula associated with solutions of the inviscid Burgers’ equation. We solve this equation using Newton's method enabled by C 1 continuous grid interpolation. We enforce incompressibility over collocated, rather than staggered grids. Our projection technique is variational and designed for B‐spline interpolation over regular grids where multiquadratic interpolation is used for velocity and multilinear interpolation for pressure. Despite our use of regular grids, we extend the variational technique to allow for cut‐cell definition of irregular flow domains for both Dirichlet and free surface boundary conditions.

Gagniere, S.↗

DOE OSTI2023

Direct Numerical Simulation of Transitional and Turbulent Flows Over Multi-Scale Surface Roughness—Part I: Methodology and Challenges

Abstract High-fidelity simulation of transitional and turbulent flows over multi-scale surface roughness presents several challenges. For instance, the complex and irregular geometrical nature of surface roughness makes it impractical to employ conforming structured grids, commonly adopted in large-scale numerical simulations due to their high computational efficiency. One possible solution to overcome this problem is offered by immersed boundary methods, which allow wall boundary conditions to be enforced on grids that do not conform to the geometry of the solid boundary. To this end, a three-dimensional, second-order accurate boundary data immersion method (BDIM) is adopted. A novel mapping algorithm that can be applied to general three-dimensional surfaces is presented, together with a newly developed data-capturing methodology to extract and analyze on-surface flow quantities of interest. A rigorous procedure to compute gradient quantities such as the wall shear stress and the heat flux on complex non-conforming geometries is also introduced. The new framework is validated by performing a direct numerical simulation (DNS) of fully developed turbulent channel flow over sinusoidal egg-carton roughness in a minimal-span domain. For this canonical case, the averaged streamwise velocity profiles are compared against results from the literature obtained with a body-fitted grid. General guidelines on the BDIM resolution requirements for multi-scale roughness simulation are given. Momentum and energy balance methods are used to validate the calculation of the overall skin friction and heat transfer at the wall. The BDIM is then employed to investigate the effect of irregular homogeneous surface roughness on the performance of an LS-89 high-pressure turbine blade at engine-relevant conditions using DNS. This is the first application of the BDIM to realize multi-scale roughness for transitional flow in transonic conditions in the context of high-pressure turbines. The methodology adopted to generate the desired roughness distribution and to apply it to the reference blade geometry is introduced. The results are compared to the case of an equivalent smooth blade.

Engineering↗

DOE OSTI2024

Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks and Operators in Scientific Computing: Fluid and Solid Mechanics

Abstract Advancements in computing power have recently made it possible to utilize machine learning and deep learning to push scientific computing forward in a range of disciplines, such as fluid mechanics, solid mechanics, materials science, etc. The incorporation of neural networks is particularly crucial in this hybridization process. Due to their intrinsic architecture, conventional neural networks cannot be successfully trained and scoped when data are sparse, which is the case in many scientific and engineering domains. Nonetheless, neural networks provide a solid foundation to respect physics-driven or knowledge-based constraints during training. Generally speaking, there are three distinct neural network frameworks to enforce the underlying physics: (i) physics-guided neural networks (PgNNs), (ii) physics-informed neural networks (PiNNs), and (iii) physics-encoded neural networks (PeNNs). These methods provide distinct advantages for accelerating the numerical modeling of complex multiscale multiphysics phenomena. In addition, the recent developments in neural operators (NOs) add another dimension to these new simulation paradigms, especially when the real-time prediction of complex multiphysics systems is required. All these models also come with their own unique drawbacks and limitations that call for further fundamental research. This study aims to present a review of the four neural network frameworks (i.e., PgNNs, PiNNs, PeNNs, and NOs) used in scientific computing research. The state-of-the-art architectures and their applications are reviewed, limitations are discussed, and future research opportunities are presented in terms of improving algorithms, considering causalities, expanding applications, and coupling scientific and deep learning solvers.

Computer Science↗

DOE OSTI2024

Uncertainty-Aware, Structure-Preserving Machine Learning Approach for Domain Shift Detection From Nonlinear Dynamic Responses of Structural Systems

Complex structural systems deployed for aerospace, civil, or mechanical applications must operate reliably under varying operational conditions. Structural health monitoring (SHM) systems help ensure the reliability of these systems by providing continuous monitoring of the state of the structure. SHM relies on synthesizing measured data with a predictive model to make informed decisions about structural states. However, these models—which may be thought of as a form of a digital twin—need to be updated continuously as structural changes (e.g., due to damage) arise. We propose an uncertainty-aware machine learning model that enforces distance preservation of the original input state space and then encodes a distance-aware mechanism via a Gaussian process (GP) kernel. The proposed approach leverages the spectral-normalized neural GP algorithm to combine the flexibility of neural networks with the advantages of GP, subjected to structure-preserving constraints, to produce an uncertainty-aware model. This model is used to detect domain shift due to structural changes that cannot be observed directly because they may be spatially isolated (e.g., inside a joint or localized damage). This work leverages detection theory to detect domain shift systematically given statistical features of the prediction variance produced by the model. The proposed approach is demonstrated on a nonlinear structure being subjected to damage conditions. In conclusion, it is shown that the proposed approach is able to rely on distances of the transformed input state space to predict increased variance in shifted domains while being robust to normative changes.

Algorithms↗

DOE OSTI2024

Optimization of District Heating Network Parameters in Steady-State Operation

Here we examine the modeling, simulation, and optimization of district heating systems, which are widely used for thermal transport using steam or hot water as a carrier. We propose a generalizable framework to specify network models and scenario parameters, and develop an optimization method for evaluating system states including pressures, fluid flowrates, and temperatures throughout the network. The network modeling includes pipes, thermal plants, pumps, and passive or controllable loads as system components. We propose basic models for thermodynamic fluid transport and enforce the balance of physical quantities in steady-state flow over co-located outgoing and return networks. We formulate an optimization problem with steam and hot water as the outgoing and return carriers, as in legacy twentieth century systems. The physical laws and engineering limitations are specified for each component type, and the thermal network flow optimization problem is formulated and solved for a realistic test network under several scenarios.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2019

Coordinated Model Predictive Control on Multi-Lane Roads

There are significant economic, environmental, energy, and other societal costs incurred by the road transportation sector. With the advent and penetration of connected and autonomous vehicles there are vast opportunities to optimize the control of individual vehicles for reducing energy consumption and increasing traffic flow. Model predictive control is a useful tool to achieve such goals, while accommodating ego-centric objectives typical of heterogeneous traffic and explicitly enforcing collision and other constraints. In this paper, we describe a multi-agent distributed maneuver planning and lane selection model predictive controller that includes an information sharing and coordination scheme. The energy saving potential of the proposed coordination scheme is then evaluated via large scale microscopic traffic simulations considering different penetration levels of connected and automated vehicles.

Goulet, Nathan↗

DOE OSTI2016

Order-of-magnitude differences in retention of low-energy Ar implanted in Si and SiO{sub 2}

The retention of 1 and 5 keV Ar implanted at 45° in Si and 4.3 nm SiO{sub 2} on Si was studied at fluences between 3 × 10{sup 14} and 1.5 × 10{sup 16} cm{sup −2}. X-ray photoelectron spectroscopy (XPS) served to monitor the accumulation of Ar as well as the removal of SiO{sub 2}. Bombardment induced changes in oxygen chemistry caused the O 1s peak position to move toward lower binding energies by as much as 2.2 eV. Plotted versus depth of erosion, the fluence dependent changes in oxygen content, and peak position were similar at 1 and 5 keV. The Ar content of Si increased with increasing exposure, saturating at fluences of ∼2 × 10{sup 15} cm{sup −2} (1 keV) and ∼6 × 10{sup 15} cm{sup −2} (5 keV). Much less Ar was retained in the SiO{sub 2}/Si sample, notably at 1 keV, in which case the low-fluence Ar signal amounted to only 8% of the Si reference. The results imply that essentially no Ar was trapped in undamaged SiO{sub 2}, i.e., the Ar atoms initially observed by XPS were located underneath the oxide. At the lowest fluence of 5 keV Ar, the retention ratio was much higher (43%) because the oxide was already highly damaged, with an associated loss of oxygen. The interpretation was assisted by TRIM(SRIM) calculations of damage production. Partial maloperation of the ion beam raster unit, identified only at a late stage of this work, enforced a study on the uniformity of bombardment. The desired information could be obtained by determining x,y line scan profiles of O 1s across partially eroded SiO{sub 2}/Si samples. Fluence dependent Ar retention in Si was described using an extended version of the rapid relocation model which takes into account that insoluble implanted rare-gas atoms tend to migrate to the surface readily under ongoing bombardment. The range parameters required for the modeling were determined using TRIM(SRIM); sputtering yields were derived from the literature. The other three parameters determining the Ar signal, i.e., (1) the thickness w of the near-surface Si region devoid of Ar, (2) the relocation efficiency Ψ{sub rlc}, and (3) the effective attenuation length L in XPS analysis were varied within reasonable limits until the calculated retention curves for 1 and 5 keV Ar in Si agreed with experimental data to better than 8%, using the same XPS sensitivity factor throughout. Results: w = 1.4 ± 0.1 nm, Ψ{sub rlc} = 6.6 ± 0.5, and L = 2.7 ± 0.2 nm. Combining experimental and calculated data, it was found that the Ar trapping efficiency of the damaged oxide is intimately correlated with the loss of oxygen. The calculated stationary areal densities of all retained Ar are compared with results obtained by high-resolution medium-energy ion scattering spectrometry. Attractive areas of future research in rare gas retention and nanobubble formation are sketched briefly.

42 ENGINEERING↗

DOE OSTI2025

Chemical speciation correlated with microstructural heterogeneity of interdicted uranium materials

Two uranium powders seized by law enforcement in Victoria, Australia, have been characterized by established nuclear forensic methods in a previously published study. Here, in this work, the results of further characterization by a scanning transmission x-ray microscope (STXM) operating in the soft x-ray regime are reported. STXM images are used to estimate the elemental distribution in micrometer-scale particles of each powder, and oxygen K-edge absorption spectra are used to determine the chemical state of uranium. The results of the current study are consistent with the previous analysis; the first powder is found to be a potassium-uranium hydrate, while the second powder is determined to be a mixture of uranium oxides primarily consisting of UO 3 .

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2016

High-resolution broadband spectroscopy using externally dispersed interferometry at the Hale telescope: Part 1, data analysis and results

High-resolution broadband spectroscopy at near-infrared wavelengths (950 to 2450 nm) has been performed using externally dispersed interferometry (EDI) at the Hale telescope at Mt. Palomar. Observations of stars were performed with the “TEDI” interferometer mounted within the central hole of the 200-in. primary mirror in series with the comounted TripleSpec near-infrared echelle spectrograph. These are the first multidelay EDI demonstrations on starlight, as earlier measurements used a single delay or laboratory sources. We demonstrate very high (10×) resolution boost, from original 2700 to 27,000 with current set of delays (up to 3 cm), well beyond the classical limits enforced by the slit width and detector pixel Nyquist limit. Significantly, the EDI used with multiple delays rather than a single delay as used previously yields an order of magnitude or more improvement in the stability against native spectrograph point spread function (PSF) drifts along the dispersion direction. We observe a dramatic (20×) reduction in sensitivity to PSF shift using our standard processing. A recently realized method of further reducing the PSF shift sensitivity to zero is described theoretically and demonstrated in a simple simulation which produces a 350× times reduction. We demonstrate superb rejection of fixed pattern noise due to bad detector pixels—EDI only responds to changes in pixel intensity synchronous to applied dithering. This part 1 describes data analysis, results, and instrument noise. Lastly, a section on theoretical photon limited sensitivity is in a companion paper, part 2.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2015

Radiation Anomaly Detection Algorithms for Field-Acquired Gamma Energy Spectra

The Remote Sensing Laboratory (RSL) is developing a tactical, networked radiation detection system that will be agile, reconfigurable, and capable of rapid threat assessment with a high degree of fidelity and certainty. Our design is driven by the needs of users such as law enforcement personnel who must make decisions by evaluating threat signatures in urban settings. The most efficient tool available to identify the nature of the threat object is real-time gamma spectroscopic analysis, as it is fast and has a very low probability of producing false positive alarm conditions. Urban radiological searches are inherently challenged by the rapid and large spatial variation of background gamma radiation, the presence of benign radioactive materials in terms of the normally occurring radioactive materials (NORM), and shielded and/or masked threat sources. Multiple spectral anomaly detection algorithms have been developed by national laboratories and commercial vendors. For example, the Gamma Detector Response and Analysis Software (GADRAS) a one-dimensional deterministic radiation transport software capable of calculating gamma ray spectra using physics-based detector response functions, was developed at Sandia National Laboratories. The nuisance-rejection spectral comparison ratio anomaly detection algorithm (or N-SCRAD), developed at Pacific Northwest National Laboratory, uses spectral comparison ratios to detect deviation from benign medical and NORM radiation source and can work in spite of strong presence of NORM and or medical sources. RSL has developed its own wavelet-based gamma energy spectral anomaly detection algorithm called WAVRAD. Test results and relative merits of these different algorithms will be discussed and demonstrated.

networked sensor, gamma spectroscopy, false alarm ↗

DOE OSTI2015

Networked Gamma Radiation System for Tactical Deployment

A networked gamma radiation detection system with directional sensitivity and energy spectral data acquisition capability is being developed by the National Security Technologies, LLC, Remote Sensing Laboratory to support the close and intense tactical engagement of law enforcement who carry out counterterrorism missions. In the proposed design, three clusters of 2" × 4" × 16" sodium iodide crystals (4 each) with digiBASE-E (for list mode data collection) would be placed on the passenger side of a minivan. To enhance localization and facilitate rapid identification of isotopes, advanced smart real-time localization and radioisotope identification algorithms like WAVRAD (wavelet-assisted variance reduction for anomaly detection) and NSCRAD (nuisance-rejection spectral comparison ratio anomaly detection) will be incorporated. We will test a collection of algorithms and analysis that centers on the problem of radiation detection with a distributed sensor network. We will study the basic characteristics of a radiation sensor network and focus on the trade-offs between false positive alarm rates, true positive alarm rates, and time to detect multiple radiation sources in a large area. Empirical and simulation analyses of critical system parameters, such as number of sensors, sensor placement, and sensor response functions, will be examined. This networked system will provide an integrated radiation detection architecture and framework with (i) a large nationally recognized search database equivalent that would help generate a common operational picture in a major radiological crisis; (ii) a robust reach back connectivity for search data to be evaluated by home teams; and, finally, (iii) a possibility of integrating search data from multi-agency responders.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2012

Noise suppression in reconstruction of low-Z target megavoltage cone-beam CT images

Purpose: To improve the image contrast-to-noise (CNR) ratio for low-Z target megavoltage cone-beam CT (MV CBCT) using a statistical projection noise suppression algorithm based on the penalized weighted least-squares (PWLS) criterion. Methods: Projection images of a contrast phantom, a CatPhan{sup Registered-Sign} 600 phantom and a head phantom were acquired by a Varian 2100EX LINAC with a low-Z (Al) target and low energy x-ray beam (2.5 MeV) at a low-dose level and at a high-dose level. The projections were then processed by minimizing the PWLS objective function. The weighted least square (WLS) term models the noise of measured projection and the penalty term enforces the smoothing constraints of the projection image. The variance of projection data was chosen as the weight for the PWLS objective function and it determined the contribution of each measurement. An anisotropic quadratic form penalty that incorporates the gradient information of projection image was used to preserve edges during noise reduction. Low-Z target MV CBCT images were reconstructed by the FDK algorithm after each projection was processed by the PWLS smoothing. Results: Noise in low-Z target MV CBCT images were greatly suppressed after the PWLS projection smoothing, without noticeable sacrifice of the spatial resolution. Depending on the choice of smoothing parameter, the CNR of selected regions of interest in the PWLS processed low-dose low-Z target MV CBCT image can be higher than the corresponding high-dose image.Conclusion: The CNR of low-Z target MV CBCT images was substantially improved by using PWLS projection smoothing. The PWLS projection smoothing algorithm allows the reconstruction of high contrast low-Z target MV CBCT image with a total dose of as low as 2.3 cGy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

Iterative image-domain decomposition for dual-energy CT

Purpose: Dual energy CT (DECT) imaging plays an important role in advanced imaging applications due to its capability of material decomposition. Direct decomposition via matrix inversion suffers from significant degradation of image signal-to-noise ratios, which reduces clinical values of DECT. Existing denoising algorithms achieve suboptimal performance since they suppress image noise either before or after the decomposition and do not fully explore the noise statistical properties of the decomposition process. In this work, the authors propose an iterative image-domain decomposition method for noise suppression in DECT, using the full variance-covariance matrix of the decomposed images. Methods: The proposed algorithm is formulated in the form of least-square estimation with smoothness regularization. Based on the design principles of a best linear unbiased estimator, the authors include the inverse of the estimated variance-covariance matrix of the decomposed images as the penalty weight in the least-square term. The regularization term enforces the image smoothness by calculating the square sum of neighboring pixel value differences. To retain the boundary sharpness of the decomposed images, the authors detect the edges in the CT images before decomposition. These edge pixels have small weights in the calculation of the regularization term. Distinct from the existing denoising algorithms applied on the images before or after decomposition, the method has an iterative process for noise suppression, with decomposition performed in each iteration. The authors implement the proposed algorithm using a standard conjugate gradient algorithm. The method performance is evaluated using an evaluation phantom (Catphan©600) and an anthropomorphic head phantom. The results are compared with those generated using direct matrix inversion with no noise suppression, a denoising method applied on the decomposed images, and an existing algorithm with similar formulation as the proposed method but with an edge-preserving regularization term. Results: On the Catphan phantom, the method maintains the same spatial resolution on the decomposed images as that of the CT images before decomposition (8 pairs/cm) while significantly reducing their noise standard deviation. Compared to that obtained by the direct matrix inversion, the noise standard deviation in the images decomposed by the proposed algorithm is reduced by over 98%. Without considering the noise correlation properties in the formulation, the denoising scheme degrades the spatial resolution to 6 pairs/cm for the same level of noise suppression. Compared to the edge-preserving algorithm, the method achieves better low-contrast detectability. A quantitative study is performed on the contrast-rod slice of Catphan phantom. The proposed method achieves lower electron density measurement error as compared to that by the direct matrix inversion, and significantly reduces the error variation by over 97%. On the head phantom, the method reduces the noise standard deviation of decomposed images by over 97% without blurring the sinus structures. Conclusions: The authors propose an iterative image-domain decomposition method for DECT. The method combines noise suppression and material decomposition into an iterative process and achieves both goals simultaneously. By exploring the full variance-covariance properties of the decomposed images and utilizing the edge predetection, the proposed algorithm shows superior performance on noise suppression with high image spatial resolution and low-contrast detectability.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2014

SU-E-J-246: A Deformation-Field Map Based Liver 4D CBCT Reconstruction Method Using Gold Nanoparticles as Constraints

Purpose: To investigate the feasibility of using nanoparticle markers to validate liver tumor motion together with a deformation field map-based four dimensional (4D) cone-beam computed tomography (CBCT) reconstruction method. Methods: A technique for lung 4D-CBCT reconstruction has been previously developed using a deformation field map (DFM)-based strategy. In this method, each phase of the 4D-CBCT is considered as a deformation of a prior CT volume. The DFM is solved by a motion modeling and free-form deformation (MM-FD) technique, using a data fidelity constraint and the deformation energy minimization. For liver imaging, there is low contrast of a liver tumor in on-board projections. A validation of liver tumor motion using implanted gold nanoparticles, along with the MM-FD deformation technique is implemented to reconstruct onboard 4D CBCT liver radiotherapy images. These nanoparticles were placed around the liver tumor to reflect the tumor positions in both CT simulation and on-board image acquisition. When reconstructing each phase of the 4D-CBCT, the migrations of the gold nanoparticles act as a constraint to regularize the deformation field, along with the data fidelity and the energy minimization constraints. In this study, multiple tumor diameters and positions were simulated within the liver for on-board 4D-CBCT imaging. The on-board 4D-CBCT reconstructed by the proposed method was compared with the “ground truth” image. Results: The preliminary data, which uses reconstruction for lung radiotherapy suggests that the advanced reconstruction algorithm including the gold nanoparticle constraint will Resultin volume percentage differences (VPD) between lesions in reconstructed images by MM-FD and “ground truth” on-board images of 11.5% (± 9.4%) and a center of mass shift of 1.3 mm (± 1.3 mm) for liver radiotherapy. Conclusion: The advanced MM-FD technique enforcing the additional constraints from gold nanoparticles, results in improved accuracy for reconstructing on-board 4D-CBCT of liver tumor. Varian medical systems research grant.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

SU-E-J-263: Dosimetric Analysis On Breast Brachytherapy Based On Deformable Image Registration

Purpose: To quantitatively compare and evaluate the dosimetry difference between breast brachytherapy protocols with different fractionation using deformable image registration. Methods: The accumulative dose distribution for multiple breast brachytherapy patients using four different applicators: Contura, Mammosite, Savi, and interstitial catheters, under two treatment protocols: 340cGy by 10 fractions in 5 days and 825cGy by 3 fractions in 2days has been reconstructed using a two stage deformable image registration approach. For all patients, daily CT was acquired with the same slice thickness (2.5mm). In the first stage, the daily CT images were rigidly registered to the initial planning CT using the registration module in Eclipse (Varian) to align the applicators. In the second stage, the tissues surrounding the applicator in the rigidly registered daily CT image were non-rigidly registered to the initial CT using a combination of image force and the local constraint that enforce zero normal motion on the surface of the applicator, using a software developed in house. We calculated the dose distribution in the daily CTs and deformed them using the final registration to convert into the image domain of the initial planning CT. The accumulative dose distributions were evaluated by dosimetry parameters including D90, V150 and V200, as well as DVH. Results: Dose reconstruction results showed that the two day treatment has a significant dosimetry improvement over the five day protocols. An average daily drop of D90 at 1.3% of the prescription dose has been observed on multiple brachytherapy patients. There is no significant difference on V150 and V200 between those two protocols. Conclusion: Brachytherapy with higher fractional dose and less fractions has an improved performance on being conformal to the dose distribution in the initial plan. Elongated brachytherapy treatments need to consider the dose uncertainty caused by the temporal changes of the soft tissue.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

TH-A-BRF-08: Deformable Registration of MRI and CT Images for MRI-Guided Radiation Therapy

Purpose: To evaluate the quality of a commercially available MRI-CT image registration algorithm and then develop a method to improve the performance of this algorithm for MRI-guided prostate radiotherapy. Methods: Prostate contours were delineated on ten pairs of MRI and CT images using Eclipse. Each pair of MRI and CT images was registered with an intensity-based B-spline algorithm implemented in Velocity. A rectangular prism that contains the prostate volume was partitioned into a tetrahedral mesh which was aligned to the CT image. A finite element method (FEM) was developed on the mesh with the boundary constraints assigned from the Velocity generated displacement vector field (DVF). The resultant FEM displacements were used to adjust the Velocity DVF within the prism. Point correspondences between the CT and MR images identified within the prism could be used as additional boundary constraints to enforce the model deformation. The FEM deformation field is smooth in the interior of the prism, and equal to the Velocity displacements at the boundary of the prism. To evaluate the Velocity and FEM registration results, three criteria were used: prostate volume conservation and center consistence under contour mapping, and unbalanced energy of their deformation maps. Results: With the DVFs generated by the Velocity and FEM simulations, the prostate contours were warped from MRI to CT images. With the Velocity DVFs, the prostate volumes changed 10.2% on average, in contrast to 1.8% induced by the FEM DVFs. The average of the center deviations was 0.36 and 0.27 cm, and the unbalance energy was 2.65 and 0.38 mJ/cc3 for the Velocity and FEM registrations, respectively. Conclusion: The adaptive FEM method developed can be used to reduce the error of the MIbased registration algorithm implemented in Velocity in the prostate region, and consequently may help improve the quality of MRI-guided radiation therapy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

Interactive prostate segmentation using atlas-guided semi-supervised learning and adaptive feature selection

Purpose: Accurate prostate segmentation is necessary for maximizing the effectiveness of radiation therapy of prostate cancer. However, manual segmentation from 3D CT images is very time-consuming and often causes large intra- and interobserver variations across clinicians. Many segmentation methods have been proposed to automate this labor-intensive process, but tedious manual editing is still required due to the limited performance. In this paper, the authors propose a new interactive segmentation method that can (1) flexibly generate the editing result with a few scribbles or dots provided by a clinician, (2) fast deliver intermediate results to the clinician, and (3) sequentially correct the segmentations from any type of automatic or interactive segmentation methods. Methods: The authors formulate the editing problem as a semisupervised learning problem which can utilize a priori knowledge of training data and also the valuable information from user interactions. Specifically, from a region of interest near the given user interactions, the appropriate training labels, which are well matched with the user interactions, can be locally searched from a training set. With voting from the selected training labels, both confident prostate and background voxels, as well as unconfident voxels can be estimated. To reflect informative relationship between voxels, location-adaptive features are selected from the confident voxels by using regression forest and Fisher separation criterion. Then, the manifold configuration computed in the derived feature space is enforced into the semisupervised learning algorithm. The labels of unconfident voxels are then predicted by regularizing semisupervised learning algorithm. Results: The proposed interactive segmentation method was applied to correct automatic segmentation results of 30 challenging CT images. The correction was conducted three times with different user interactions performed at different time periods, in order to evaluate both the efficiency and the robustness. The automatic segmentation results with the original average Dice similarity coefficient of 0.78 were improved to 0.865–0.872 after conducting 55–59 interactions by using the proposed method, where each editing procedure took less than 3 s. In addition, the proposed method obtained the most consistent editing results with respect to different user interactions, compared to other methods. Conclusions: The proposed method obtains robust editing results with few interactions for various wrong segmentation cases, by selecting the location-adaptive features and further imposing the manifold regularization. The authors expect the proposed method to largely reduce the laborious burdens of manual editing, as well as both the intra- and interobserver variability across clinicians.

07 ISOTOPE AND RADIATION SOURCES↗