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At least 127 records · Page 7

Kernel reconstruction methods for Doppler broadening — Temperature interpolation by linear combination of reference cross sections at optimally chosen temperatures

This paper establishes a new family of methods to perform temperature interpolation of nuclear interactions cross sections, reaction rates, or cross sections times the energy. One of these quantities at temperature T is approximated as a linear combination of quantities at reference temperatures (T j ). The problem is formalized in a cross section independent fashion by considering the kernels of the different operators that convert cross section related quantities from a temperature T 0 to a higher temperature T — namely the Doppler broadening operation. Doppler broadening interpolation of nuclear cross sections is thus here performed by reconstructing the kernel of the operation at a given temperature T by means of linear combination of kernels at reference temperatures (T j ). The choice of the L 2 metric yields optimal linear interpolation coefficients in the form of the solutions of a linear algebraic system inversion. The optimization of the choice of reference temperatures (T j ) is then undertaken so as to best reconstruct, in the L∞ sense, the kernels over a given temperature range [T min ,T max ]. The performance of these kernel reconstruction methods is then assessed in light of previous temperature interpolation methods by testing them upon isotope 238 U. Temperature-optimized free Doppler kernel reconstruction significantly outperforms all previous interpolation-based methods, achieving 0.1% relative error on temperature interpolation of 238 U total cross section over the temperature range [300 K,3000 K] with only 9 reference temperatures.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Inverse design of hypoeutectoid pearlite steel microstructures using a deep learning and genetic algorithm optimization framework

Goal-oriented microstructure design in metallic materials is a challenging task due to complex structure-property relationships. Traditional experimental and computational approaches are time-intensive and economically inefficient, limiting their applicability for large-scale design space exploration. Here, in this work, we propose an end-to-end framework that integrates deep learning models with genetic optimization to design microstructures with targeted mechanical properties. Deep learning models enable accurate forward design, while their integration with genetic optimization enables efficient inverse design within a few hours, compared to days or weeks using conventional finite element simulations. The framework combines experimental characterization and finite element modeling to analyze the influence of microstructural features on the mechanical behavior of hypoeutectoid steels. Data from both experiments and simulations are used to train the deep learning models. To demonstrate its effectiveness, we apply the framework to 0.63% carbon steel with proeutectoid ferrite and pearlite phases, commonly used in industrial applications. In this study, 2D microstructures were used for modeling, selected primarily for computational efficiency and to establish proof of concept. The framework successfully optimizes microstructures for targeted yield strength, ultimate strength, and stress concentration factors while significantly reducing computational time. Beyond hypoeutectoid steels, this scalable framework can be extended to other material systems and integrated with additive manufacturing, offering an efficient approach for accelerating microstructure design for specific engineering applications.

ConvLSTM↗

Impact of leaf motion constraints on IMAT plan quality, deliver accuracy, and efficiency

Purpose: Intensity modulated arc therapy (IMAT) is a radiation therapy delivery technique that combines the efficiency of arc based delivery with the dose painting capabilities of intensity modulated radiation therapy (IMRT). A key challenge in developing robust inverse planning solutions for IMAT is the need to account for the connectivity of the beam shapes as the gantry rotates from one beam angle to the next. To overcome this challenge, inverse planning solutions typically impose a leaf motion constraint that defines the maximum distance a multileaf collimator (MLC) leaf can travel between adjacent control points. The leaf motion constraint ensures the deliverability of the optimized plan, but it also impacts the plan quality, the delivery accuracy, and the delivery efficiency. In this work, the authors have studied leaf motion constraints in detail and have developed recommendations for optimizing the balance between plan quality and delivery efficiency. Methods: Two steps were used to generate optimized IMAT treatment plans. The first was the direct machine parameter optimization (DMPO) inverse planning module in the Pinnacle{sup 3} planning system. Then, a home-grown arc sequencer was applied to convert the optimized intensity maps into deliverable IMAT arcs. IMAT leaf motion constraints were imposed using limits of between 1 and 30 mm/deg. Dose distributions were calculated using the convolution/superposition algorithm in the Pinnacle{sup 3} planning system. The IMAT plan dose calculation accuracy was examined using a finer sampling calculation and the quality assurance verification. All plans were delivered on an Elekta Synergy with an 80-leaf MLC and were verified using an IBA MatriXX 2D ion chamber array inserted in a MultiCube solid water phantom. Results: The use of a more restrictive leaf motion constraint (less than 1-2 mm/deg) results in inferior plan quality. A less restrictive leaf motion constraint (greater than 5 mm/deg) results in improved plan quality but can lead to less accurate dose distribution as evidenced by increasing discrepancies between the planned and the delivered doses. For example, the results from our patient-specific quality assurance measurements demonstrated that the average gamma analysis passing rate decreased from 98% to 80% when the allowable leaf motion increased from 3 to 20 mm/deg. Larger leaf motion constraints also led to longer treatment delivery times (2 to 4 folds) due to the additional MLC leaf motion. Conclusions: Leaf motion constraints significantly impact IMAT plans in terms of plan quality, delivery accuracy, and delivery efficiency with the impact magnified for more complex cases. Our studies indicate that a leaf motion constraint of 2 to 3 mm/deg of gantry rotation can provide an optimal balance between plan quality, delivery accuracy, and efficiency.

61 RADIATION PROTECTION AND DOSIMETRY↗

Invertible neural networks for real-time control of extrusion additive manufacturing

Material extrusion additive manufacturing (AM) has enabled an elegant fabrication pathway for a vast material library. Nonetheless, each material requires optimization of printing parameters generally determined through significant trial-and-error testing. To eliminate arduous, iteration-based optimization approaches, many researchers have used machine learning (ML) algorithms which provide opportunities for automated process optimization. Here, in this work, we demonstrate the use of an ML-driven approach for real-time material extrusion print-parameter optimization through in-situ monitoring of printed line geometry. To do this, we use deep invertible neural networks (INNs) which can solve both forward and inverse, or optimization, problems using a single network. By combining in-situ computer vision and deep INNs, the printing parameters can be autonomously optimized to print a target line width in 1.2 s. Furthermore, defects that occur during printing can be rapidly identified and corrected autonomously. The methods developed and presented in this work eliminate user-intensive, time-consuming, and iterative parameter discovery approaches that currently limit accelerated implementation of extrusion-based AM processes. Furthermore, the presented approach can be generalized to provide real-time monitoring and optimization pathways for increasingly complex AM environments.

36 MATERIALS SCIENCE↗

Scalable Matrix-Free Adaptive Product-Convolution Approximation for Locally Translation-Invariant Operators

We present an adaptive grid matrix-free operator approximation scheme based on a "product-convolution" interpolation of convolution operators. This scheme is appropriate for operators that are locally translation-invariant, even if these operators are high rank or full rank. Such operators arise in Schur complement methods for solving partial differential equations (PDEs), as Hessians in PDE-constrained optimization and inverse problems, as integral operators, as covariance operators, and as Dirichlet-to-Neumann maps. Constructing the approximation requires computing the impulse responses of the operator to point sources centered on nodes in an adaptively refined grid of sample points. A randomized a posteriori error estimator drives the adaptivity. Once constructed, the approximation can be efficiently applied to vectors using the fast Fourier transform. The approximation can be efficiently converted to hierarchical matrix (H-matrix) format, then inverted or factorized using scalable H-matrix arithmetic. The quality of the approximation degrades gracefully as fewer sample points are used, allowing cheap lower quality approximations to be used as preconditioners. Furthermore, this yields an automated method to construct preconditioners for locally translation-invariant Schur complements. We directly address issues related to boundaries and prove that our scheme eliminates boundary artifacts. We test the scheme on a spatially varying blurring kernel, on the nonlocal component of an interface Schur complement for the Poisson operator, and on the data misfit Hessian for an advection dominated advection-diffusion inverse problem. Numerical results show that the scheme outperforms existing methods.

97 MATHEMATICS AND COMPUTING↗

Interlocked optimization and fast gradient algorithm for a seismic inverse problem

Highlights: {yields} A 2D extension of the 1D nonlinear inversion of well-seismic data is given. {yields} Appropriate regularization yields a well-determined large scale inverse problem. {yields} An interlocked optimization loop acts as an efficient preconditioner. {yields} The adjoint state method is used to compute the misfit function gradient. {yields} Domain decomposition method yields an efficient parallel implementation. - Abstract: We give a nonlinear inverse method for seismic data recorded in a well from sources at several offsets from the borehole in a 2D acoustic framework. Given the velocity field, approximate values of the impedance are recovered. This is a 2D extension of the 1D inversion of vertical seismic profiles . The inverse problem generates a large scale undetermined ill-conditioned problem. Appropriate regularization terms render the problem well-determined. An interlocked optimization algorithm yields an efficient preconditioning. A gradient algorithm based on the adjoint state method and domain decomposition gives a fast parallel numerical method. For a realistic test case, convergence is attained in an acceptable time with 128 processors.

97 MATHEMATICS AND COMPUTING↗

A geometric projection method for designing three-dimensional open lattices with inverse homogenization

Topology optimization is a methodology for assigning material or void to each point in a design domain in a way that extremizes some objective function, such as the compliance of a structure under given loads, subject to various imposed constraints, such as an upper bound on the mass of the structure. Geometry projection is a means to parameterize the topology optimization problem, by describing the design in a way that is independent of the mesh used for analysis of the design's performance; it results in many fewer design parameters, necessarily resolves the ill-posed nature of the topology optimization problem, and provides sharp descriptions of the material interfaces. We extend previous geometric projection work to 3 dimensions and design unit cells for lattice materials using inverse homogenization. We perform a sensitivity analysis of the geometric projection and show it has smooth derivatives, making it suitable for use with gradient-based optimization algorithms. The technique is demonstrated by designing unit cells comprised of a single constituent material plus void space to obtain light, stiff materials with cubic and isotropic material symmetry. Here, we also design a single-constituent isotropic material with negative Poisson's ratio and a light, stiff material comprised of 2 constituent solids plus void space.

42 ENGINEERING↗

Diagnostics for the optimization of an 11 keV inverse Compton scattering x-ray source

In a scope of a collaboration between Thales Communications and Security and CEA DAM DIF, 11 keV Xrays were produced by inverse Compton scattering on the ELSA facility. In this type of experiment, X-ray observation lies in the use of accurate electron and laser beam interaction diagnostics and on fitted X-ray detectors. The low interaction probability between < 100 {mu}m width, 12 ps [rms] length electron and photon pulses requires careful optimization of pulse spatial and temporal covering. Another issue was to observe 11 keV X-rays in the ambient radioactive noise of the linear accelerator. For that, we use a very sensitive detection scheme based on radio luminescent screens.

43 PARTICLE ACCELERATORS↗

SU-E-T-409: Intensity Modulated Robotic Radiotherapy

Purpose: As compared with the IRIS-based models, the MLC-based CyberKnife system allows more efficient treatment delivery due to its improved coverage of large lesions and intensity modulation. The treatment delivery efficiency is mainly determined by the number of selected nodes. This study aimed to demonstrate that relatively small sets of optimally selected nodes could produce high-quality plans. Methods: The full body path of the CyberKnife system consists of 110 nodes, from which we selected various sets for 4 prostate cancer cases using our in-house beamselection software. With the selected nodes we generated IMRT plans using our in-house beamlet-based inverse-planning optimization program. We also produced IMRT plans using the MultiPlan treatment planning system (version 5.0) for the same cases. Furthermore, the nodes selected by MultiPlan were used to produce plans with our own optimization software so that we could compare the quality of the selected sets of nodes. Results: Our beam-selection program selected one node-set for each case, with the number of nodes ranging from 23 to 34. The IMRT plans based on the selected nodes and our in-house optimization program showed adequate target coverage, with favorable critical structure sparing for the cases investigated. Compared with the plans using the nodes selected by MultiPlan, the plans generated with our selected beams provided superior rectum/bladder sparing for 75% of the cases. The plans produced by MultiPlan with various numbers of nodes also suggested that the plan quality was not compromised significantly when the number of nodes was reduced. Conclusion: Our preliminary results showed that with beamletbased planning optimization, one could produce high-quality plans with an optimal set of nodes for MLC-based robotic radiotherapy. Furthermore, our beam-selection strategy could help further improve critical structure sparing.

60 APPLIED LIFE SCIENCES↗

CO 2 storage site characterization using ensemble-based approaches with deep generative models

Estimating spatially distributed properties such as permeability from available sparse measurements is a great challenge in efficient subsurface CO 2 storage operations. In this paper, a deep generative model that can accurately capture complex subsurface structure is tested with an ensemble-based inversion method for accurate and accelerated characterization of CO 2 storage sites. We chose Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) for its realistic reservoir property representation and Ensemble Smoother with Multiple Data Assimilation (ES-MDA) for its robust data fitting and uncertainty quantification capability. WGAN-GP are trained to generate high-dimensional permeability fields from a low-dimensional latent space and ES-MDA then updates the latent variables by assimilating available measurements. Several subsurface site characterization examples including Gaussian, channelized, and fractured reservoirs are used to evaluate the accuracy and computational efficiency of the proposed method and the main features of the unknown permeability fields are characterized accurately with reliable uncertainty quantification. Furthermore, the estimation performance is compared with a widely-used variational, i.e., optimization-based, inversion approach, and the proposed approach outperforms the variational inversion method in several benchmark cases. We explain such superior performance by visualizing the objective function in the latent space: because of nonlinear and aggressive dimension reduction via generative modeling, the objective function surface becomes extremely complex while the ensemble approximation can smooth out the multi-modal surface during the minimization. This suggests that the ensemble-based approach works well over the variational approach when combined with deep generative models at the cost of forward model runs unless convergence-ensuring modifications are implemented in the variational inversion.

42 ENGINEERING↗

SU-E-T-555: A Protontherapy Inverse Treatment Planning System Prototype with Linear Energy Transfer (LET) Optimization

Purpose: Develop and benchmark an inverse treatment planning system (TPS) for proton radiotherapy integrating fast analytical dose and LET calculations in patient geometries and a dual objective function with both dose and LET components, enabling us to apply optimization techniques to improve the predicted outcome of treatments based on radiobiological models. Methods: The software package was developed in MATLAB and implements a fluence-dose calculation technique based on a pencil beam model for dose calculations and a 3D LET model based on the extension of the LET in the radial direction as a function of the predicted radiological pathway. Both models were benchmarked against commissioning data from our institution, dose calculations performed with a commercial treatment planning system and Monte Carlo simulations. The optimization is based on the adaptive simulated annealing approach . Results: The dose and LET calculations were tested in a water phantom and several real patient treatments. The pass rate for the gamma index analysis (3%/3mm) test was above 90% for all test cases analyzed, and the calculation time was of the order of seconds. The inverse planning module produced plans with a significantly higher mean LET in the target compared to traditional plans, without any loss of target coverage. The clinical relevance of this improvement is under consideration . Conclusion: The developed treatment planning system is a valuable clinical and research tool that enables us to incorporate LET effects into proton radiotherapy planning in a streamlined fashion.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Inverse-collimated proton radiography for imaging thin materials

Relativistic, magnetically-focused proton radiography was invented at Los Alamos National Laboratory using the 800 MeV LANSCE beam, and is inherently well-suited to imaging dense objects, at areal densities >20 g cm -2 . However, if the unscattered portion of the transmitted beam is removed at the Fourier plane through inverse-collimation, this system becomes highly sensitive to very thin media, of areal densities <100 mg cm -2 . Here, this inversecollimation scheme is described in detail and demonstrated by imaging Xe gas with a shockwave generated by an aluminum plate compressing the gas at Mach 8.8. With a 5-mrad inverse collimator, an areal density change of just 49 mg cm-2 across the shock front is discernible with a contrast-to-noise ratio of 3. Geant4 modeling of idealized and realistic proton transports can guide the design of inverse-collimators optimized for specific experimental conditions and show that this technique performs better for thin targets with reduced incident proton beam emittance. This work increases the range of areal densities to which the system is sensitive to span from ~25 mg cm -2 to 100 g cm -2 , exceeding three orders of magnitude. This enables the simultaneous imaging of a dense system, as well as thin jets and ejecta material that are otherwise difficult to characterize with high-energy proton radiography.

47 OTHER INSTRUMENTATION↗

A stochastic approach to quantifying the blur with uncertainty estimation for high-energy X-ray imaging systems

One of the primary causes of blur in a high-energy X-ray imaging system is the shape and extent of the radiation source, or ‘spot’. It is important to be able to quantify the size of the spot as it provides a lower bound on the recoverable resolution for a radiograph, and penumbral imaging methods – which involve the analysis of blur caused by a structured aperture – can be used to obtain the spot’s spatial profile. We present a Bayesian approach for estimating the spot shape that, unlike variational methods, is robust to the initial choice of parameters. The posterior is obtained from a normal likelihood, which was constructed from a weighted least squares approximation to a Poisson noise model, and prior assumptions that enforce both smoothness and non-negativity constraints. A Markov chain Monte Carlo algorithm is used to obtain samples from the target posterior, and the reconstruction and uncertainty estimates are the computed mean and variance of the samples, respectively. Lastly, synthetic data-sets are used to demonstrate accurate reconstruction, while real data taken with high-energy X-ray imaging systems are used to demonstrate applicability and feasibility.

97 MATHEMATICS AND COMPUTING↗

Three-dimensional single-cell imaging with X-ray waveguides in the holographic regime

X-ray tomography at the level of single biological cells is possible in a low-dose regime, based on full-field holographic recordings, with phase contrast originating from free-space wave propagation. Building upon recent progress in cellular imaging based on the illumination by quasi-point sources provided by X-ray waveguides, here this approach is extended in several ways. First, the phase-retrieval algorithms are extended by an optimized deterministic inversion, based on a multi-distance recording. Second, different advanced forms of iterative phase retrieval are used, operational for single-distance and multi-distance recordings. Results are compared for several different preparations of macrophage cells, for different staining and labelling. As a result, it is shown that phase retrieval is no longer a bottleneck for holographic imaging of cells, and how advanced schemes can be implemented to cope also with high noise and inconsistencies in the data.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Increasing dose gradient and uniformity in small fields using modulation: Theory and prototypes for cone-based stereotactic radiosurgery

Purpose: To investigate the theoretical limits to the tradeoff between dose gradient and uniformity when modulation is used in the context of cone based SRS, and to design a prototype collimation device that allows for steeper dose gradients and/or higher target uniformity as compared to a standard circular collimator. Methods: An inverse planning optimization is performed in the context of idealized phantom geometry to determine the ideal fluence pattern that best approximates a “rect function” dose distribution. Ideal fluence patterns were approximated in a prototype device and radiochromic film dosimetry was utilized to compare the prototype device to a standard circular collimator. Results: For choices of prescription isodose lines above approximately 50%, utilizing modulation allows for an improved tradeoff between dose gradient index and dose heterogeneity index. Compensators placed within the circular collimator can achieve the necessary modulation. Conclusions: Using modulation with features on a submillimeter distance scale, it is possible to increase the dose gradient and/or uniformity in small fields.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

SU-D-19A-06: The Effect of Beam Parameters On Very High-Energy Electron Radiotherapy: A Planning Study

Purpose: We evaluated the effect of very high-energy electron (VHEE) beam parameters on the planning of a lung cancer case by means of Monte Carlo simulations. Methods: We simulated VHEE radiotherapy plans using the EGSnrc/BEAMnrc-DOSXYZnrc code. We selected a lung cancer case that was treated with 6MV photon VMAT to be planned with VHEE. We studied the effect of beam energy (80 MeV, 100 MeV, and 120 MeV), number of equidistant beams (16 or 32), and beamlets sizes (3 mm, 5 mm or 7 mm) on PTV coverage, sparing of organs at risk (OARs) and dose conformity. Inverse-planning optimization was performed in a research version of RayStation (RaySearch Laboratories AB) using identical objective functions and constraints for all VHEE plans. Results: Similar PTV coverage and dose conformity was achieved by all the VHEE plans. The 100 MeV and 120 MeV VHEE plans were equivalent amongst them and were superior to the 80 MeV plan in terms of OARs sparing. The effect of using 16 or 32 equidistant beams was a mean difference in average dose of 2.4% (0%–7.7%) between the two plans. The use of 3 mm beamlet size systematically reduced the dose to all the OARs. Based on these results we selected the 100MeV-16beams-3mm-beamlet-size plan to compare it against VMAT. The selected VHEE plan was more conformal than VMAT and improved OAR sparing (heart and trachea received 125% and 177% lower dose, respectively) especially in the low-dose region. Conclusion: We determined the VHEE beam parameters that maximized the OAR dose sparing and dose conformity of the actually delivered VMAT plan of a lung cancer case. The selected parameters could be used for the planning of other treatment sites with similar size, shape, and location. For larger targets, a larger beamlet size might be used without significantly increasing the dose. B Palma: None. M Bazalova: None. B Hardemark: Employee, RaySearch Americas. E Hynning: Employee, RaySearch Americas. B Qu: None. B Loo Jr.: Research support, RaySearch, Varian. P Maxim: Research support, RaySearch, Varian.

60 APPLIED LIFE SCIENCES↗

TU-CD-304-01: FEATURED PRESENTATION and BEST IN PHYSICS (THERAPY): Trajectory Modulated Arc Therapy: Development of Novel Arc Delivery Techniques Integrating Dynamic Table Motion for Extended Volume Treatments

Purpose: Integration of coordinated robotic table motion with inversely-planned arc delivery has the potential to resolve table-top delivery limitations of large-field treatments such as Total Body Irradiation (TBI), Total Lymphoid Irradiation (TLI), and Cranial-Spinal Irradiation (CSI). We formulate the foundation for Trajectory Modulated Arc Therapy (TMAT), and using Varian Developer Mode capabilities, experimentally investigate its practical implementation for such techniques. Methods: A MATLAB algorithm was developed for inverse planning optimization of the table motion, MLC positions, and gantry motion under extended-SSD geometry. To maximize the effective field size, delivery trajectories for TMAT TBI were formed with the table rotated at 270° IEC and dropped vertically to 152.5cm SSD. Preliminary testing of algorithm parameters was done through retrospective planning analysis. Robotic delivery was programmed using custom XML scripting on the TrueBeam Developer Mode platform. Final dose was calculated using the Eclipse AAA algorithm. Initial verification of delivery accuracy was measured using OSLDs on a solid water phantom of varying thickness. Results: A comparison of DVH curves demonstrated that dynamic couch motion irradiation was sufficiently approximated by static control points spaced in intervals of less than 2cm. Optimized MLC motion decreased the average lung dose to 68.5% of the prescription dose. The programmed irradiation integrating coordinated table motion was deliverable on a TrueBeam STx linac in 6.7 min. With the couch translating under an open 10cmx20cm field angled at 10°, OSLD measurements along the midline of a solid water phantom at depths of 3, 5, and 9cm were within 3% of the TPS AAA algorithm with an average deviation of 1.2%. Conclusion: A treatment planning and delivery system for Trajectory Modulated Arc Therapy of extended volumes has been established and experimentally demonstrated for TBI. Extension to other treatment techniques such as TLI and CSI is readily achievable through the developed platform. Grant Funding by Varian Medical Systems.

60 APPLIED LIFE SCIENCES↗