Results for “Trajectory optimization” — Page 14

DOE OSTI2013

Extending molecular simulation time scales: Parallel in time integrations for high-level quantum chemistry and complex force representations

Parallel in time simulation algorithms are presented and applied to conventional molecular dynamics (MD) and ab initio molecular dynamics (AIMD) models of realistic complexity. Assuming that a forward time integrator, f (e.g., Verlet algorithm), is available to propagate the system from time t{sub i} (trajectory positions and velocities x{sub i} = (r{sub i}, v{sub i})) to time t{sub i+1} (x{sub i+1}) by x{sub i+1} = f{sub i}(x{sub i}), the dynamics problem spanning an interval from t{sub 0}…t{sub M} can be transformed into a root finding problem, F(X) = [x{sub i} − f(x{sub (i−1})]{sub i} {sub =1,M} = 0, for the trajectory variables. The root finding problem is solved using a variety of root finding techniques, including quasi-Newton and preconditioned quasi-Newton schemes that are all unconditionally convergent. The algorithms are parallelized by assigning a processor to each time-step entry in the columns of F(X). The relation of this approach to other recently proposed parallel in time methods is discussed, and the effectiveness of various approaches to solving the root finding problem is tested. We demonstrate that more efficient dynamical models based on simplified interactions or coarsening time-steps provide preconditioners for the root finding problem. However, for MD and AIMD simulations, such preconditioners are not required to obtain reasonable convergence and their cost must be considered in the performance of the algorithm. The parallel in time algorithms developed are tested by applying them to MD and AIMD simulations of size and complexity similar to those encountered in present day applications. These include a 1000 Si atom MD simulation using Stillinger-Weber potentials, and a HCl + 4H{sub 2}O AIMD simulation at the MP2 level. The maximum speedup ((serial execution time)/(parallel execution time) ) obtained by parallelizing the Stillinger-Weber MD simulation was nearly 3.0. For the AIMD MP2 simulations, the algorithms achieved speedups of up to 14.3. The parallel in time algorithms can be implemented in a distributed computing environment using very slow transmission control protocol/Internet protocol networks. Scripts written in Python that make calls to a precompiled quantum chemistry package (NWChem) are demonstrated to provide an actual speedup of 8.2 for a 2.5 ps AIMD simulation of HCl + 4H{sub 2}O at the MP2/6-31G* level. Implemented in this way these algorithms can be used for long time high-level AIMD simulations at a modest cost using machines connected by very slow networks such as WiFi, or in different time zones connected by the Internet. The algorithms can also be used with programs that are already parallel. Using these algorithms, we are able to reduce the cost of a MP2/6-311++G(2d,2p) simulation that had reached its maximum possible speedup in the parallelization of the electronic structure calculation from 32 s/time step to 6.9 s/time step.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

TH-AB-BRB-01: Trajectory Modulated Arc Therapy: Application to Partial Breast Irradiation

Current state-of-the art digital C-arm medical linear accelerators are capable of delivering radiation treatments with high level of automation, which affords coordinated motions of gantry, couch, and multileaf collimator (MLC) with dose rate modulations. The new machine capacity has shown the potential to bring substantially improved radiation dosimetry and/or delivery efficiency to many challenging diseases. Combining an integrated beam orientation optimization algorithm with automated machine navigation, markedly improved dose conformity has been achieved using 4ρ therapy. Trajectory modulated radiation therapy (TMAT) can be used to deliver highly conformal dose to partial breast or to carve complex dose distribution for therapy involving extended volumes such as total marrow and total lymph node treatment. Dynamic electron arc radiotherapy (DEAR) not only overcomes the deficiencies of conventional electron therapy in dose conformity and homogeneity but also achieves so without patient-specific shields. The combination of MLC and couch tracking provides improved motion management of thoracic and abdominal tumors. A substantial body of work has been done in these technological advances for clinical translation. The proposed symposium will provide a timely review of these exciting opportunities. Learning Objectives: Recognize the potential of using digitally controlled linacs for clinically significant improvements in delivered dose distributions for various treatment sites. Identify existing approaches to treatment planning, optimization and delivery for treatment techniques utilizing the advanced functions of digital linacs and venues for further development and improvement. Understand methods for testing and validating delivery system performance. Identify tools available on current delivery systems for implementation and control for such treatments. Obtain the update in clinical applications, trials and regulatory approval. K. Sheng, NIH U19AI067769, NIH R43CA183390, NIH R01CA188300, Varian Medical Systems V. Yu, Varian Medical Systems, AAPM Summer Undergraduate Fellowship, NSF graduate fellowship S. Nill, Elekta AB. Cancer Research UK under Programme C33589/A19727, NIHR Biomedical Research Centre at The Royal Marsden and The Institute of Cancer Research.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Stability-Constrained Learning for Frequency Regulation in Power Grids With Variable Inertia

The increasing penetration of converter-based renewable generation has resulted in faster frequency dynamics, and low and variable inertia. As a result, there is a need for frequency control methods that are able to stabilize a disturbance in the power system at timescales comparable to the fast converter dynamics. This paper proposes a combined linear and neural network controller for inverter-based primary frequency control that is stable at time-varying levels of inertia. We model the time-variance in inertia via a switched affine hybrid system model. We derive stability certificates for the proposed controller via a quadratic candidate Lyapunov function. We test the proposed control on a 12-bus 3-area test network, and compare its performance with a base case linear controller, optimized linear controller, and finite-horizon Linear Quadratic Regulator (LQR). Our proposed controller achieves faster mean settling time and over 50% reduction in average control cost across 100 inertia scenarios compared to the optimized linear controller. Unlike LQR which requires complete knowledge of the inertia trajectories and system dynamics over the entire control time horizon, our proposed controller is real-time tractable, and achieves comparable performance to LQR.

data-driven control↗

DOE OSTI2021

Transportation Hub Infrastructure Expansion: Decision Support Under Uncertainty

The Athena project (www.athena-mobility.org) has worked to investigate the relationship between the Dallas-Fort Worth Airport (DFW) and the greater Dallas area in order to better understand and therefore better inform future decision-making regarding the critical infrastructure that influence mobility between the airport and the city. Through this work, infrastructure related to curbside pickup and drop-off, parking, public transit, and the road network congestion were identified as critical to the operation of the DFW transportation hub. The infrastructure analysis and expansion aspect of the Athena project is focused on the restructuring of the CTA curb as a hierarchical curb and the building or repurposing of parking infrastructure as the interplay between these two areas. Many sources of uncertainty exist that may impact future airport and transportation hub operations, such as passenger volume growth, population demographic changes over time, electric vehicle (EV) adoption rates, and autonomous vehicle (AV) adoption rates. Due to these sources of uncertainty, we have selected for our research a modeling framework that can capture various types of uncertainty and hedge against those uncertainties in the optimization process. We analyze road network and curb congestion, the rise of transportation networking companies, trends in parking usage, existing policies around this infrastructure, airport revenue streams, and other contributing factors to enable infrastructure decision making with less uncertainty. To accomplish this wholistic analysis, we have developed a novel multi-stage, multi-period stochastic optimization model which considers the airport's decisions from 2025-2045 under different possible future macro trajectories and day-to-day variations in operational conditions captured as "annual representation of operations" scenarios with respective probabilities. This model has also been designed to leverage the outputs of various efforts under the Athena project to create a combined decision framework for infrastructure decisions. These various efforts include the route optimization model, the ASPIRES simulation, the mode choice model, and the SUMO traffic simulation. Our computational experiments of this system at scale have resulted in a working version of our infrastructure model which enables the explicit representation and consideration of various sources of uncertainty in the decision process to enable robust, flexible decision-making. This model has been effectively run on NREL's HPC system, Eagle, with large numbers of stochastic scenarios and shows promise as a scalable tool for robust consideration of uncertainties in airport planning. We have tested our model using 30,240 operational circumstances in total, resulting in a problem with more 200 million variables. This model was solved in several different configurations, and a workflow to simulate the performance of the infrastructure model results was developed and deployed. In general, our results indicate that a combination of remote parking, remote curb infrastructure, and dynamic pricing can generate revenue, reduce emissions, accommodate emerging technologies such as AVs and EVs, and manage airport passenger growth over time. We note the success of the proposed strategy depends on the data collection and forecasting abilities of DFW. We have also seen that the AV adoption by TNCs might necessitate larger amounts of remote curb. The results of this work inform strategies for airport infrastructure decision making, as well as demonstrate the value of an adaptable model, but also indicate that there are avenues remaining where further research would be of value.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Formation and reconfiguration of tight multi-lane platoons

Advances in vehicular communication technologies are expected to facilitate cooperative driving in the future. Connected and Automated Vehicles (CAVs) are able to collaboratively plan and execute driving maneuvers by sharing their perceptual knowledge and future plans. In this paper, an architecture for autonomous navigation of tight multi-lane platoons traveling on public roads is presented. Using the proposed approach, CAVs are able to form single or multi-lane platoons of various geometrical configurations. They are able to reshape and adjust their configurations according to changes in the environment. The proposed architecture consists of two main components: an offline motion planner system and an online hierarchical control system. The motion planner uses an optimization-based approach for cooperative formation and reconfiguration in tight spaces. A constrained optimization scheme is used to plan smooth, dynamically feasible and collision-free trajectories for all the vehicles within the platoon. The paper addresses online computation limitations by employing a family of maneuvers precomputed offline and stored on a look-up table on the vehicles. The online hierarchical control system is composed of three levels: a traffic operation system (TOS), a decision-maker, and a path-follower. The TOS determines the desired platoon reconfiguration. The decision-maker checks the feasibility of the reconfiguration plan based on real-time information about the surrounding traffic. The reconfiguration maneuver is executed by a low-level path-following feedback controller in real-time. The effectiveness of the approach is demonstrated through simulations of three case studies: (1) formation reconfiguration (2) obstacle avoidance, and (3) benchmarking against behavior-based planning in which the desired formation is achieved using a sequence of motion primitives.

42 ENGINEERING↗

DOE OSTI2026

Active space selection with self-healing diffusion Monte Carlo algorithms for periodic solids

Multideterminant Diffusion Monte Carlo (DMC) displays improved accuracy over single determinant DMC. Self-Healing Diffusion Monte Carlo (SHDMC) is a DMC based method that iteratively improves a multideterminant trial wavefunction. Although configuration interaction or complete active space (CAS) methods are very accurate and computationally feasible for many systems, they are not optimal for application to solids. SHDMC is accurate and designed for application to solids, so developing SHDMC based active space selection algorithms is a worthy endeavor. Here, we present and compare active space selection algorithms that are designed for use in conjunction with SHDMC, without relying on external approaches. For benchmarking, we calculated the ground state energy of a small unit cell of graphene and compared the results with a complete basis set extrapolated selected CI and a reference SHDMC trajectory. We found that systematically expanding the active space using an “auto-branching” algorithm optimally balances accuracy with computational practicality. To the best of our knowledge, this is the first work that demonstrates completely self-contained DMC-based active space selection algorithms that do not depend on external methods for determinant selection.

Spanedda, Nicole [ORNL]↗

DOE OSTI2020

MorphOT: transport-based interpolation between EM maps with UCSF ChimeraX

Cryogenic electron microscopy (cryo-EM) offers the unique potential to capture conformational heterogeneity, by solving multiple three-dimensional classes that co-exist within a single cryo-EM image dataset. To investigate the extent and implications of such heterogeneity, we propose to use an optimal-transport-based metric to interpolate barycenters between EM maps and produce morphing trajectories.

47 OTHER INSTRUMENTATION↗

DOE OSTI2014

WE-G-17A-09: Novel Magnetic Shielding Design for Inline and Perpendicular Integrated 6 MV Linac and 1.0 T MRI Systems

Purpose: The influence of fringe magnetic fields delivered by magnetic resonance imaging (MRI) on the beam generation and transportation in Linac is still a major challenge for the integration of linear accelerator and MRI (Linac-MRI). In this study, we investigated an optimal magnetic shielding design for Linac-MRI and further characterized the beam trajectory in electron gun. Methods: Both inline and perpendicular configurations were analyzed in this study. The configurations, comprising a Linac-MRI with a 100cm SAD and an open 1.0 T superconductive magnet, were simulated by the 3D finite element method (FEM). The steel shielding around the Linac was included in the 3D model, the thickness of which was varied from 1mm to 20mm, and magnetic field maps were acquired with and without additional shielding. The treatment beam trajectory in electron gun was evaluated using OPERA 3d SCALA with and without shielding cases. Results: When Linac was not shielded, the uniformity of diameter sphere volume (DSV) (30cm) was about 5 parts per million (ppm) and the fringe magnetic fields in electron gun were more than 0.3 T. With shielding, the magnetic fields in electron gun were reduced to less than 0.01 T. For the inline configuration, the radial magnetic fields in the Linac were about 0.02T. A cylinder steel shield used (5mm thick) altered the uniformity of DSV to 1000 ppm. For the perpendicular configuration, the Linac transverse magnetic fields were more than 0.3T, which altered the beam trajectory significantly. A 8mm-thick cylinder steel shield surrounding the Linac was used to compensate the output losses of Linac, which shifted the magnetic fields' uniformity of DSV to 400 ppm. Conclusion: For both configurations, the Linac shielding was used to ensure normal operation of the Linac. The effect of magnetic fields on the uniformity of DSV could be modulated by the shimming technique of the MRI magnet. NIH/NIGMS grant U54 GM104944, Lincy Endowed Assistant Professorship.

43 PARTICLE ACCELERATORS↗

DOE OSTI2016

Analysis of Nonlinear Dynamics by Square Matrix Method

The nonlinear dynamics of a system with periodic structure can be analyzed using a square matrix. In this paper, we show that because the special property of the square matrix constructed for nonlinear dynamics, we can reduce the dimension of the matrix from the original large number for high order calculation to low dimension in the first step of the analysis. Then a stable Jordan decomposition is obtained with much lower dimension. The transformation to Jordan form provides an excellent action-angle approximation to the solution of the nonlinear dynamics, in good agreement with trajectories and tune obtained from tracking. And more importantly, the deviation from constancy of the new action-angle variable provides a measure of the stability of the phase space trajectories and their tunes. Thus the square matrix provides a novel method to optimize the nonlinear dynamic system. The method is illustrated by many examples of comparison between theory and numerical simulation. Finally, in particular, we show that the square matrix method can be used for optimization to reduce the nonlinearity of a system.

43 PARTICLE ACCELERATORS↗

DOE OSTI2016

SU-F-T-503: Trajectory Modulated Arc Therapy of Intracranial Lesions: Development of a Standardized Path-Based Technique for Fully Dynamic Couch-Gantry Modulated Treatments

Purpose: The integration of couch motion during arc delivery is necessitated to enable irradiation trajectories such as coronal arcs, and to enhance the geometrical sampling for dynamic deliveries to the highest extent. To enable such capability, a platform of Trajectory Modulated Arc Therapy (TMAT) is developed in conjunction with standardized noncollisional dynamic path-set for irradiation of intracranial lesions. Methods: A generalized path-set was constructed through the combination of sagittal arcs (45 degrees from the CAX), axial arcs, and coronal arcs produced through modulation of the dynamic rotation of couch. The standardized path was implemented in a contiguous manner enabling the formation of fully automated sub-trajectories to provide maximal geometrical convergence with minimal number of arcs. Progressive sampling technique is used for direct aperture optimization of the MLCs and the selection of couch positions across the control points. Dosimetry of the resulting plans was assessed relative to clinically delivered plans. Using the TrueBeam Developer Mode, plan deliverability was tested. Results: Treatment planning of TMAT sub-trajectories for central, anterior and posterior tumor sites with volumes ranging from 4.75cc to 107cc demonstrated radically reduced doses to the critical OARs when compared to the clinically treated VMAT. Specifically, percentage reduction in mean dose for critical organs such as brainstem, cochlea, and optic nerve are found to be as low as 74±15%, 50±26% and 74±30% respectively as compared to VMAT. Conformity Index, defined as the ratio of tumor volume (VPTV) and 100% dose volume (V(D100%)), was reduced up to 12% while the Gradient Index, defined as V(D100%)/V(D50%), was concurrently improved by up to 14%. Conclusion: An automated standardized trajectory with dynamically modulated couch-gantry arcs has been developed for intracranial radiotherapy. Through the incorporation of coronal arcs, it is demonstrated that significantly reduced OAR doses can be achieved relative to clinically treated patient plans via VMAT. Research Grant Funding Support by Varian Medical Systems.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

A Preferences Corpus and Annotation Scheme for Human-Guided Alignment of Time-Series GPTs

The process of time-series forecasting such as predicting trajectories of silicon content in blast furnaces is a difficult task. Most time-series approaches today focus on scalar-type MSE loss optimization. This optimization approach, while widely common, could benefit from the use of human expert or process-level preferences. In this paper, we introduce a novel alignment and fine-tuning approach that involves learning from a corpus of preferred and dis-preferred time-series prediction trajectories. Our contributions include (1) a preference annotation pipeline for time-series forecasts, (2) the application of Score-based Preference Optimization (SPO) to train decoder-only transformers from preferences, and (3) results showing improvements in forecast quality. The approach is validated on both proprietary blast furnace data and the UCI Appliances Energy dataset. The proposed preference corpus and training strategy offer a new option for fine-tuning sequence models in industrial settings.

DPO↗

DOE OSTI2017

Prospective Clinical Implementation of a Novel Magnetic Resonance Tracking Device for Real-Time Brachytherapy Catheter Positioning

Purpose: We designed and built dedicated active magnetic resonance (MR)-tracked (MRTR) stylets. We explored the role of MRTR in a prospective clinical trial. Methods and Materials: Eleven gynecologic cancer patients underwent MRTR to rapidly optimize interstitial catheter placement. MRTR catheter tip location and orientation were computed and overlaid on images displayed on in-room monitors at rates of 6 to 16 frames per second. Three modes of actively tracked navigation were analyzed: coarse navigation to the approximate region around the tumor; fine-tuning, bringing the stylets to the desired location; and pullback, with MRTR stylets rapidly withdrawn from within the catheters, providing catheter trajectories for radiation treatment planning (RTP). Catheters with conventional stylets were inserted, forming baseline locations. MRTR stylets were substituted, and catheter navigation was performed by a clinician working inside the MRI bore, using monitor feedback. Results: Coarse navigation allowed repositioning of the MRTR catheters tips by 16 mm (mean), relative to baseline, in 14 ± 5 s/catheter (mean ± standard deviation [SD]). The fine-tuning mode repositioned the catheter tips by a further 12 mm, in 24 ± 17 s/catheter. Pullback mode provided catheter trajectories with RTP point resolution of ∼1.5 mm, in 1 to 9 s/catheter. Conclusions: MRTR-based navigation resulted in rapid and optimal placement of interstitial brachytherapy catheters. Catheters were repositioned compared with the initial insertion without tracking. In pullback mode, catheter trajectories matched computed tomographic precision, enabling their use for RTP.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2016

TU-FG-BRB-04: A New Optimization Method for Pre-Treatment Patient-Specific Stopping-Power by Combining Proton Radiography and X-Ray CT

Purpose: The relative stopping power (RSP) uncertainty is the largest contributor to the range uncertainty in proton therapy. The purpose of this work is to develop a robust and systematic method that yields accurate patient specific RSPs by combining pre-treatment X-ray CT and daily proton radiography. Methods: The method is formulated as a penalized least squares optimization (PLSO) problem min(|Ax-B|). The matrix A represents the cumulative path-length crossed in each material computed by calculating proton trajectories through the X-ray CT. The material RSPs are denoted by x and B is the pRad, expressed as water equivalent thickness. The equation is solved using a convex-conic optimizer. Geant4 simulations were made to assess the feasibility of the method. RSP extracted from the Geant4 materials were used as a reference and the clinical HU-RSP curve as a comparison. The PLSO was first tested on a Gammex RMI-467 phantom. Then, anthropomorphic phantoms of the head, pelvis and lung were studied and resulting RSPs were evaluated. A pencil beam was generated in each phantom to evaluate the proton range accuracy achievable by using the optimized RSPs. Finally, experimental data of a pediatric head phantom (CIRS) were acquired using a recently completed experimental pCT scanner. Results: Numerical simulations showed precise RSP (<0.75%) for Gammex materials except low-density lung (LN-300) (1.2%). Accurate RSP have been obtained for the head (µ=−0.10%, 1.5σ=1.12%), lung (µ=−0.33, 1.5σ=1.02%) and pelvis anthropomorphic phantoms (µ=0.12, 1.5σ=0,99%). The range precision has been improved with an average R80 difference to the reference (µ±1.5σ) of −0.20±0.35%, −0.47±0.92% and −0.06±0.17% in the head, lung and pelvis phantoms respectively, compared to the 3.5% clinical margin. Experimental HU-RSP curve have been produced on the CIRS pediatric head. Conclusion: The proposed PLSO with prior knowledge X-ray CT shows promising potential (R80 σ<1.0% over all sites) to decrease the range uncertainty.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Understanding latent timescales in neural ordinary differential equation models of advection-dominated dynamical systems

The neural ordinary differential equation (ODE) framework has shown considerable promise in recent years in developing highly accelerated surrogate models for complex physical systems characterized by partial differential equations (PDEs). For PDE-based systems, state-of-the-art neural ODE strategies leverage a two-step procedure to achieve this acceleration: a nonlinear dimensionality reduction step provided by an autoencoder, and a time integration step provided by a neural-network based model for the resultant latent space dynamics (the neural ODE). This work explores the applicability of such autoencoder-based neural ODE strategies for PDEs in which advection terms play a critical role. More specifically, alongside predictive demonstrations, physical insight into the sources of model acceleration (i.e., how the neural ODE achieves its acceleration) is the scope of the current study. Such investigations are performed by quantifying the effects of both autoencoder and neural ODE components on latent system time-scales using eigenvalue analysis of dynamical system Jacobians. To this end, the sensitivity of various critical training parameters – de-coupled versus end-to-end training, latent space dimensionality, and the role of training trajectory length, for example – to both model accuracy and the discovered latent system timescales is quantified. Furthermore, this work specifically uncovers the key role played by the training trajectory length (the number of rollout steps in the loss function during training) on the latent system timescales: larger trajectory lengths correlate with an increase in limiting neural ODE time-scales, and optimal neural ODEs are found to recover the largest time-scales of the full-order (ground-truth) system. Demonstrations are performed across fundamentally different unsteady fluid dynamics configurations influenced by advection: (1) the Kuramoto–Sivashinsky equations (2) Hydrogen-Air channel detonations (the compressible reacting Navier–Stokes equations with detailed chemistry), and (3) 2D Atmospheric flow.

Advection-dominated dynamical systems↗

DOE OSTI2015

SU-C-204-04: Patient Specific Proton Stopping Powers Estimation by Combining Proton Radiography and Prior-Knowledge X-Ray CT Information

Purpose: The material relative stopping power (RSP) uncertainty is the highest contributor to the range uncertainty in proton therapy. The purpose of this work is to develop a robust and systematic method that yields accurate, patient specific, RSP by combining 1) pre-treatment x-ray CT and 2) daily proton radiograph of the patient. Methods: The method is formulated as a linear least-square optimization problem (min||Ax-B||2). The parameter A represents the pathlength crossed by the proton in each material. The RSPs for the materials (water equivalent thickness (WET)/physical thickness) are denoted by x. B is the proton radiograph expressed as WET crossed. The problem is minimized using a convex-conic optimization algorithm with xi<xi+1 constraint. The Gammex RMI-465 phantom, with 13 material inserts, is used to investigate the optimal RSPs. The proton radiograph (B) is produced using Geant4 simulation. The matrix A is computed by calculating proton trajectories through the CT with the cubic-spline approach. Geant4 RSPs are used as reference and the clinical HU-RSP calibration curve is used as comparison. The convergence is investigated as a function of the number of angles and protons used. The radiographs angle which yield the most accurate RSPs are investigated. Results: Optimization with 9 angles and 104 protons/angle yields precise RSP (<0.75%) for all materials, except plastic lung (1.5%). Average deviation relative to Geant4 RSP is 0.5% and 3.5% using HU-RSP. Using more than two angles or more than 4×103 proton does not further increase the RSP estimate accuracy. The insert distance to the beam initial plane is related to the RSP error for this insert material. Specific filters are applied on the proton angle and energy loss to remove nuclear interaction artefacts. Conclusion: The proposed formulation of the problem with prior knowledge x-ray CT demonstrates serious potential to increase the accuracy of present RSP estimates.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Determining Optimal Running Conditions for TinyTPC Detector

Liquid argon time projection chambers, (LArTPCs), are particle detectors used to collect ionization charge information from particle trajectories, facilitating detailed particle track analysis. They are currently used as particle detectors in major physics projects such as the deep underground neutrino experiment (DUNE), to detect and study the nature of the elusive neutrino particle. TinyTPC is a small scale LArTPC featuring a pixelated readout system (LArPix) that we will use to study liquid argon doping. We expect this doping to enhance the resolution of LAr detectors, especially for low energy particles below 10 MeV which would expand the capabilities of currently running experiments. Housed within a cryostat filled with liquid argon, the TinyTPC will rely on a high and a low voltage system to collect data. In preparation for deployment we found and resolved issues in the HV and LV systems and we determined optimal running conditions in a test vessel. This presentation will go over the detector commissioning process that enabled data taking with the TinyTPC.

Gonzalez, Rebecca↗

DOE OSTI2016

TH-AB-BRB-00: Research Opportunities with Digital Linear Accelerators

Current state-of-the art digital C-arm medical linear accelerators are capable of delivering radiation treatments with high level of automation, which affords coordinated motions of gantry, couch, and multileaf collimator (MLC) with dose rate modulations. The new machine capacity has shown the potential to bring substantially improved radiation dosimetry and/or delivery efficiency to many challenging diseases. Combining an integrated beam orientation optimization algorithm with automated machine navigation, markedly improved dose conformity has been achieved using 4ρ therapy. Trajectory modulated radiation therapy (TMAT) can be used to deliver highly conformal dose to partial breast or to carve complex dose distribution for therapy involving extended volumes such as total marrow and total lymph node treatment. Dynamic electron arc radiotherapy (DEAR) not only overcomes the deficiencies of conventional electron therapy in dose conformity and homogeneity but also achieves so without patient-specific shields. The combination of MLC and couch tracking provides improved motion management of thoracic and abdominal tumors. A substantial body of work has been done in these technological advances for clinical translation. The proposed symposium will provide a timely review of these exciting opportunities. Learning Objectives: Recognize the potential of using digitally controlled linacs for clinically significant improvements in delivered dose distributions for various treatment sites. Identify existing approaches to treatment planning, optimization and delivery for treatment techniques utilizing the advanced functions of digital linacs and venues for further development and improvement. Understand methods for testing and validating delivery system performance. Identify tools available on current delivery systems for implementation and control for such treatments. Obtain the update in clinical applications, trials and regulatory approval. K. Sheng, NIH U19AI067769, NIH R43CA183390, NIH R01CA188300, Varian Medical Systems V. Yu, Varian Medical Systems, AAPM Summer Undergraduate Fellowship, NSF graduate fellowship S. Nill, Elekta AB. Cancer Research UK under Programme C33589/A19727, NIHR Biomedical Research Centre at The Royal Marsden and The Institute of Cancer Research.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

TH-AB-BRB-02: ET-Reloaded

Current state-of-the art digital C-arm medical linear accelerators are capable of delivering radiation treatments with high level of automation, which affords coordinated motions of gantry, couch, and multileaf collimator (MLC) with dose rate modulations. The new machine capacity has shown the potential to bring substantially improved radiation dosimetry and/or delivery efficiency to many challenging diseases. Combining an integrated beam orientation optimization algorithm with automated machine navigation, markedly improved dose conformity has been achieved using 4ρ therapy. Trajectory modulated radiation therapy (TMAT) can be used to deliver highly conformal dose to partial breast or to carve complex dose distribution for therapy involving extended volumes such as total marrow and total lymph node treatment. Dynamic electron arc radiotherapy (DEAR) not only overcomes the deficiencies of conventional electron therapy in dose conformity and homogeneity but also achieves so without patient-specific shields. The combination of MLC and couch tracking provides improved motion management of thoracic and abdominal tumors. A substantial body of work has been done in these technological advances for clinical translation. The proposed symposium will provide a timely review of these exciting opportunities. Learning Objectives: Recognize the potential of using digitally controlled linacs for clinically significant improvements in delivered dose distributions for various treatment sites. Identify existing approaches to treatment planning, optimization and delivery for treatment techniques utilizing the advanced functions of digital linacs and venues for further development and improvement. Understand methods for testing and validating delivery system performance. Identify tools available on current delivery systems for implementation and control for such treatments. Obtain the update in clinical applications, trials and regulatory approval. K. Sheng, NIH U19AI067769, NIH R43CA183390, NIH R01CA188300, Varian Medical Systems V. Yu, Varian Medical Systems, AAPM Summer Undergraduate Fellowship, NSF graduate fellowship S. Nill, Elekta AB. Cancer Research UK under Programme C33589/A19727, NIHR Biomedical Research Centre at The Royal Marsden and The Institute of Cancer Research.

60 APPLIED LIFE SCIENCES↗