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DOE OSTI2024

Conversion of Magnetic Energy to Plasma Kinetic Energy During Guide Field Magnetic Reconnection in the Laboratory

Here, we present laboratory measurements showing the two-dimensional (2D) structure of energy conversion during magnetic reconnection with a guide field over the electron and ion diffusion regions, resolving the separate energy deposition on electrons and ions. We find that the electrons are energized by the parallel electric field at two locations, at the X line and around the separatrices. On the other hand, the ions are energized ballistically by the perpendicular electric field in the vicinity of the high-density separatrices. An energy balance calculation by evaluating the terms of the Poynting theorem shows that 40% of the magnetic energy is converted to particle energy, 2/3 of which is transferred to ions and 1/3 to electrons. Further analysis suggests that the energy deposited on particles manifests mostly in the form of thermal kinetic energy in the diffusion regions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Guided Mode Evolution and Ionization Injection in Meter-Scale Multi-GeV Laser Wakefield Accelerators

Here, we show that multi-GeV laser wakefield electron accelerators in meter-scale, low density hydrodynamic plasma waveguides operate in a new nonlinear propagation regime dominated by sustained beating of lowest order modes of the ponderomotively modified channel; this occurs whether or not the injected pulse is linearly matched to the guide. For a continuously doped gas jet, this emergent mode beating effect leads to axially modulated enhancement of ionization injection and a multi-GeV energy spectrum of multiple quasimonoenergetic peaks; the same process in a locally doped jet produces single multi-GeV peaks with <10% energy spread. A three-stage model of drive laser pulse evolution and ionization injection characterizes the beating effect and explains our experimental results.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Cyclic superconducting refrigerators using guided fluxon propagation

We propose cyclic refrigeration in solid state, employing a gas of magnetic field vortices in a type-II superconductor—also known as fluxons—as the cooling agent. Refrigeration cycles are realized by envisioning a racetrack geometry consisting of both adiabatic and isothermal arms, etched into a type-II superconductor. The guided propagation of fluxons in the racetrack is achieved by applying an external electrical current, in a Corbino geometry, through the sample. A gradient of magnetic field is set across the racetrack allowing one to adiabatically cool down and heat up the fluxons, which subsequently exchange heat with the cold and hot reservoirs, respectively. We characterize the steady state of refrigeration cycles thermodynamically for both s -wave and d -wave pairing symmetries, and present their figures of merit such as the cooling power delivered, and the coefficient of performance. Our cooling principle can offer significant cooling for on-chip microrefrigeration purposes, by locally cooling below the base temperatures achievable in a conventional dilution refrigerator. We estimate n W / m m 2 of cooling power per unit area assuming a tunnel coupling with ∼ M Ω µ m 2 specific resistance. Integrating the fluxon fridge to quantum circuits can enhance their coherence time by locally suppressing thermal fluctuations, and improve the efficiency of single photon detectors and charge sensors. Published by the American Physical Society 2024

Karmakar, Tathagata (ORCID:0000000187812017)↗

DOE OSTI2025

Interpretable machine learning-guided design of Fe-based soft magnetic alloys

Here, we present a machine learning (ML) guided approach to predict saturation magnetization (𝑀 S ) and coercivity (𝐻 C ) in Fe-rich soft magnetic alloys, particularly Fe-Si-B systems. ML models trained on experimental data reveal that increasing Si and B content reduces 𝑀 S from 1.81 T (DFT ≈ 2.04 T) to ≈1.54 T (DFT ≈ 1.56T) in Fe-Si-B, which is attributed to decreased magnetic density and structural modifications. Experimental validation of ML predicted magnetic saturation on Fe-1Si-1B (2.09 T), Fe-5Si-5B (2.01 T), and Fe-10Si-10B (1.54 T) alloy compositions further supports our findings. These trends are consistent with density functional theory predictions, which link increased electronic disorder and band broadening to lower 𝑀 S values. Experimental validation on selected alloys confirms the predictive accuracy of the ML model, with good agreement across compositions. Beyond predictive accuracy, detailed uncertainty quantification and model interpretability including through feature importance and partial dependence analysis reveal that 𝑀 S is governed by a nonlinear interplay between Fe content and early transition metal ratios, while 𝐻 C is more sensitive to processing conditions such as ribbon thickness and thermal treatment windows. The ML framework was further applied to Fe-Si-B/Cr/Cu/Zr/Nb alloys in a pseudoquaternary compositional space, which shows comparable magnetic properties to NANOMET (Fe 84.8 ⁢Si 0.5 ⁢B 9.4 ⁢Cu 0.8⁢ P 3.5 ⁢C 1 ), FINEMET (Fe 73.5 ⁢Si 13.5 ⁢B 9 Cu 1 ⁢Nb 3 ), NANOPERM (Fe 88 ⁢Zr 7⁢ B 4 ⁢Cu 1 ), and HITPERM (Fe 44 ⁢Co 44 ⁢Zr 7⁢ B 4 ⁢Cu 1 . Our findings demonstrate the potential of the ML framework for accelerated search of high-performance soft magnetic materials.

density functional theory↗

DOE OSTI2025

TRIM: AI Guided Random Number Generation for Resource-Constrained IoT Systems

Random numbers often serve as the backbone for many security solutions in diverse domains such as cryptography, side channel leakage prevention, and moving target defense. However, generating true random numbers requires a physical source of entropy (e.g. hardware, quantum, environmental phenomenon) making it difficult to realize at a large scale and at a low cost. On the flip side, pseudorandom number generators (easy to implement) following a specific distribution (e.g. Gaussian) can be easily compromised given a sufficient amount of traces. In this work, we have developed a machine learning-guided generative approach that can be used to create portable, resource-efficient, and cost-effective random number generators with high throughput and true randomness characteristics. We implement the proposed approach as a highly parameterized framework and perform extensive evaluation for different settings. The framework was able to learn from true random sources such as irrational numbers and environmental audio noise and imitate those sources towards generating new good quality random numbers on demand. We have generated more than 1 billion bits and observed robust performance in terms of true randomness metrics obtained from NIST SP 800-22 and FIPS 140-1 randomness test suites achieving a throughput of up to 142.85 Mbps. Compared to the state-of-the-art (SOTA) technique, the iso-cost setup of our framework can achieve more than 500 Mbps in a distributed setting. We have evaluated the efficacy of running the true randomness imitation AI models on target edge devices such as Raspberry Pi 4 (Model B), Nvidia Jetson Nano, Nvidia Jetson Orin Nano and Nvidia Jetson Xavier. We have also looked at the security of the TRIM framework itself against different adversarial threat models.

Cybersecurity↗

DOE OSTI2018

Super Subwavelength Guiding and Rejecting of Terahertz Spoof SPPs Enabled by Planar Plasmonic Waveguides and Notch Filters Based on Spiral-Shaped Units

In this study, we numerically simulate novel planar plasmonic waveguides and notch filters with excellent guiding and rejection of terahertz (THz) waves with super subwavelength confinement. Our design is based on spoof surface plasmon polaritons-surface plasmon polaritons with a frequency that has been tuned using patterned conductive surfaces. We find that by using patterns of periodically arranged spiral-shaped units, the dispersion characteristics can be engineered at will by tuning the parameters of the spirals. We find that the resulting plasmonic waveguides have much lower asymptotic frequencies and much tighter terahertz field confinement when compared with conventional rectangular-grooved plasmonic waveguides. We show it is possible to design a structure with lateral dimensions that are only 25% the size of the conventional spoof surface plasmon polariton waveguides but with the same asymptotic frequency. Finally, we combined this architecture with broadband couplers to design an ultrawideband low-pass filter with sharp roll-off (cut-off frequency at 1.29 THz) and low insertion loss (<;3 dB). Furthermore, by introducing double ring resonators based on spiral-shaped units, a planar plasmonic notch filter with rejection of more than 17 dB between 0.97 and 0.99 THz is demonstrated. Finally, the proposed waveguides and notch filters may have great potential applications in the promising terahertz integrated plasmonic circuits and systems.

42 ENGINEERING↗

DOE OSTI2021

A Confidence-Guided Technique for Tracking Time-Varying Features

Application scientists often employ feature tracking algorithms to capture the temporal evolution of various features in their simulation data. However, as the complexity of the scientific features is increasing with the advanced simulation modeling techniques, quantification of reliability of the feature tracking algorithms is becoming important. One of the desired requirements for any robust feature tracking algorithm is to estimate its confidence during each tracking step so that the results obtained can be interpreted without any ambiguity. To address this, we develop a confidence-guided feature tracking algorithm that allows reliable tracking of user-selected features and presents the tracking dynamics using a graph-based visualization along with the spatial visualization of the tracked feature. Here, the efficacy of the proposed method is demonstrated by applying it to two scientific datasets containing different types of time-varying features.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

ARETE: Accurate Error Assessment via Machine Learning-Guided Dynamic-Timing Analysis

Nanometer circuits are increasingly prone to timing errors, escalating the need for fault injection frameworks to accurately evaluate their impact on applications. Here in this paper, we propose ARETE, a novel cross-layer, fault-injection framework that combines dynamic-binary instrumentation with machine learning-guided dynamic-timing analysis. ARETE enables accurate fault-injection into any application by estimating the location of the injecting errors via dynamic-timing analysis. To accelerate fault-injection, we develop a novel, data-aware, machine learning-based mechanism that dynamically pre-selects the error-prone instructions and limits the application of the costly dynamic-timing analysis only to them. To evaluate ARETE's accuracy, our fully automated toolflow is configured to support fault-injection based on detailed post-layout gate-level simulations as well as via existing workload-agnostic error models. Our results for various workloads, including an autonomous-driving library, show that the location and time of injected errors performed by ARETE, is 89.9% consistent with fault-injection based on full gate-level simulation. On average, ARETE executes 84.6x faster than gate-level simulation and at a cost of 3.4% loss in the program output quality estimation. When compared to the existing statistical fault-injection tools that are based on workload-agnostic error models, ARETE improves the accuracy of fault-injection rate and output quality estimation by 143.9% and 40.4% on average, respectively.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Modifying Network Guided Vehicle for Repeatable EcoCAR ACC Energy Testing

The newest iteration of the Department of Energy (DOE) Advanced Vehicle Technology Competitions is the EcoCAR Mobility Challenge. In this paper, ANL and HORIBA MIRA collaborate to propose and pilot a test method that enables controlled, repeatable track-based energy consumption testing of semi-autonomous features. The testing was performed at MIRA’s proving ground facility in Nuneaton, UK, using their Network Guided Vehicle (NGV) connectivity and automation test platform. The NGV served as a repeatable lead vehicle following prescribed drive traces around two different track circuits while the host vehicle under test, a Honda Civic with full speed range Adaptive Cruise Control, followed behind. Over the course of a two day test, multiple iterations of certification and custom EPA drive traces were performed on track, demonstrating the viability of a two vehicle energy test. Results were analyzed to assess NGV lead vehicle performance in following the prescribed cycle using SAE J2951 standard calculations in an effort to quantify how repeatably and accurately the lead vehicle followed the trace. Host vehicle energy consumption was assessed using both vehicle CAN bus monitoring and an on-board fuel flow meter.

ACC↗

DOE OSTI2022

Analysis of implant loss risk factors after simultaneous guided bone regeneration: A retrospective study of 5404 dental implants

Abstract Purpose The purpose was to analyze the risk factors for implant loss after simultaneous guided bone regeneration (GBR). Materials and Methods Patients who underwent implant placement with simultaneous GBR between January 2011 and December 2018 were screened for this study. The cumulative survival rate (CSR) was calculated using the life table method. Log‐rank test and Kaplan–Meier survival estimates were used to identify potential risk factors for implant loss. The association between the investigated variables and implant loss was determined using hazard ratios (HRs) obtained from a multivariate Cox regression analysis. Results A total of 3973 patients with 5404 implants were included in this study. The CSRs of the implants at 1, 5, and 10 years were 99.6%, 98.9%, and 98.7%, respectively. Male patient (HR = 2.94, 95% CI: 1.41–6.14), periodontitis (HR = 4.26, 95% CI: 2.05–9.86), tissue‐level implants (HR = 3.02, 95% CI: 1.30–6.98), narrow implants (HR = 2.71, 95% CI: 1.12–6.57), and implant length ≤10 mm (HR = 2.91, 95% CI: 1.41–6.02) significantly increased the risk of implant loss ( p < 0.05). The risk of implant loss was significantly higher in the maxillary posterior region (HR = 2.26, 95% CI: 1.04–4.90) than in the maxillary anterior region ( p < 0.05). Compared to Straumann, Nobel (HR = 4.07, 95% CI: 1.75–9.44) and other implant systems (HR = 14.23, 95% CI: 4.32–46.85) showed a significantly higher risk of implant loss ( p < 0.05). Conclusion Male patient, periodontitis, maxillary posterior region, Nobel implant system, other implant systems, tissue‐level implants, narrow implants, and implant length ≤10 mm were considered risk factors for implant loss after simultaneous GBR.

Shen, Xiaoting↗

DOE OSTI2016

Using models to guide field experiments: a priori predictions for the CO 2 response of a nutrient- and water-limited native Eucalypt woodland

One major uncertainty in Earth System models is the response of terrestrial ecosystems to rising atmospheric CO 2 concentration (Ca), particularly under nutrient-lim- ited conditions. The Eucalyptus Free-Air CO 2 Enrichment (EucFACE) experiment, recently established in a nutrient- and water-limited woodlands, presents a unique opportunity to address this uncertainty, but can best do so if key model uncertainties have been identified in advance. Moreover, we applied seven vegetation models, which have previously been comprehensively assessed against earlier forest FACE experi- ments, to simulate a priori possible outcomes from EucFACE. Our goals were to provide quantitative projections against which to evaluate data as they are collected, and to identify key measurements that should be made in the experiment to allow discrimination among alternative model assumptions in a postexperiment model intercompari- son. Simulated responses of annual net primary productivity (NPP) to elevated Ca ranged from 0.5 to 25% across models. The simulated reduction of NPP during a low-rainfall year also varied widely, from 24 to 70%. Key processes where assumptions caused disagreement among models included nutrient limitations to growth; feedbacks to nutri- ent uptake; autotrophic respiration; and the impact of low soil moisture availability on plant processes. Finally, knowledge of the causes of variation among models is now guiding data collection in the experiment, with the expectation that the experimental data can optimally inform future model improvements.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Experimental Investigation of Innovative Cooling Schemes on an Additively Manufactured Engine Scale Turbine Nozzle Guide Vane

This study includes the design, validation, and fabrication via direct metal laser sintering (DMLS) of a gas turbine nozzle guide vanes (NGV) that incorporates three innovative cooling schemes specifically enabled by additive manufacturing. The novel NGV design is the culmination of an extensive research and development effort over a period of 4 years that included low- and high-speed cascade testing coupled with unsteady computational fluid dynamics for numerous candidate innovative cooling architectures. The final vane design (SJ-vane) consists of sweeping jet (SJ) film cooling holes on the suction surface (SS), sweeping jet impingement holes at the leading edge and double-wall partial length triangular pin-fin with impinging jet at the trailing edge. For comparison purposes, a second DMLS enabled vane (777-vane) was designed and fabricated with prototypical cooling circuits to serve as a baseline. This vane consists of a shaped film cooling holes on the suction surface, circular impingement holes at the leading edge, and full-length cylindrical pin-fins at the trailing edge. Experiments with the two DMLS enabled vanes were performed at the Ohio State University Turbine Reacting Flow Rig (TuRFR) at engine-relevant temperature (1375 K) and Mach number conditions. Infrared (IR) thermography was utilized to measure the wall temperature of the pressure and suction surface at several coolant mass flowrates to estimate the overall cooling effectiveness (&straightphi;). Results showed improved cooling performance for the advanced cooling schemes (sweeping jet film cooling, impingement cooling, and triangular pin-fin cooling) compared with the baseline cooling schemes.

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 OSTI2020

Introductory guide to the application of XPS to epitaxial films and heterostructures

XPS is an important characterization method for epitaxial films and heterostructures. Although standard approaches to XPS data collection and analysis provide useful information such as average composition and the presence of contaminants, more in-depth analysis provides information about film structure, surface termination, built-in electric potentials and band offsets. The high degree of structural order in these materials enables such information to be extracted from spectral data but also adds complications to the analysis. This guide highlights three topics of importance in this field: i) the impacts of crystallinity on XPS signals and quantification, ii) the unexpected spectral line shapes that can occur in unusual or novel materials, and, iii) the ability of XPS to yield information about built-in potentials and band offsets. Concepts are demonstrated using complex oxide heterostructures. Although the topics are highly relevant to epitaxial films and heterostructures, they also apply to signal crystals of complex materials

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Considerations in upconversion: A practical guide to sum-frequency generation spectrometer design and implementation

Here, in this tutorial review, we discuss how the choice of upconversion pulse shape in broadband vibrational sum-frequency generation (SFG) spectrometer design impacts the chemical or physical insights one can obtain from a set of measurements. A time-domain picture of a vibrational coherence being mapped by a second optical field is described and the implications of how this mapping, or upconversion process, takes place are given in the context of several popular and emerging approaches found in the literature. Emphasis is placed on broadband frequency-domain measurements, where the choice of upconversion pulse enhances or limits the information contained in the SFG spectrum. We conclude with an outline for a flexible approach to SFG upconversion using pulse-shaping methods and a simple guide to design and optimize the associated instrumentation.

47 OTHER INSTRUMENTATION↗

DOE OSTI2011

Evaluations of an adaptive planning technique incorporating dose feedback in image-guided radiotherapy of prostate cancer

Purpose: Online image guidance (IG) has been used to effectively correct the setup error and inter-fraction rigid organ motion for prostate cancer. However, planning margins are still necessary to account for uncertainties such as deformation and intra-fraction motion. The purpose of this study is to investigate the effectiveness of an adaptive planning technique incorporating offline dose feedback to manage inter-fraction motion and residuals from online correction. Methods: Repeated helical CT scans from 28 patients were included in the study. The contours of prostate and organs-at-risk (OARs) were delineated on each CT, and online IG was simulated by matching center-of-mass of prostate between treatment CTs and planning CT. A seven beam intensity modulated radiation therapy (IMRT) plan was designed for each patient on planning CT for a total of 15 fractions. Dose distribution at each fraction was evaluated based on actual contours of the target and OARs from that fraction. Cumulative dose up to each fraction was calculated by tracking each voxel based on a deformable registration algorithm. The cumulative dose was compared with the dose from initial plan. If the deviation exceeded the pre-defined threshold, such as 2% of the D{sub 99} to the prostate, an adaptive planning technique called dose compensation was invoked, in which the cumulative dose distribution was fed back to the treatment planning system and the dose deficit was made up through boost radiation in future treatment fractions. The dose compensation was achieved by IMRT inverse planning. Two weekly compensation delivery strategies were simulated: one intended to deliver the boost dose in all future fractions (schedule A) and the other in the following week only (schedule B). The D{sub 99} to prostate and generalized equivalent uniform dose (gEUD) to rectal wall and bladder were computed and compared with those without the dose compensation. Results: If only 2% underdose is allowed at the end of the treatment course, then 11 patients fail. If the same criteria is assessed at the end of each week (every five fractions), then 14 patients fail, with three patients failing the 1st or 2nd week but passing at the end. The average dose deficit from these 14 patients was 4.4%. They improved to 2% after the weekly compensation. Out of these 14 patients who needed dose compensation, ten passed the dose criterion after weekly dose compensation, three patients failed marginally, and one patient still failed the criterion significantly (10% deficit), representing 3.6% of the patient population. A more aggressive compensation frequency (every three fractions) could successfully reduce the dose deficit to the acceptable level for this patient. The average number of required dose compensation re-planning per patient was 0.82 (0.79) per patient for schedule A (B) delivery strategy. The doses to OARs were not significantly different from the online IG only plans without dose compensation. Conclusions: We have demonstrated the effectiveness of offline dose compensation technique in image-guided radiotherapy for prostate cancer. It can effectively account for residual uncertainties which cannot be corrected through online IG. Dose compensation allows further margin reduction and critical organs sparing.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2012

Quality assurance for image-guided radiation therapy utilizing CT-based technologies: A report of the AAPM TG-179

Purpose: Commercial CT-based image-guided radiotherapy (IGRT) systems allow widespread management of geometric variations in patient setup and internal organ motion. This document provides consensus recommendations for quality assurance protocols that ensure patient safety and patient treatment fidelity for such systems. Methods: The AAPM TG-179 reviews clinical implementation and quality assurance aspects for commercially available CT-based IGRT, each with their unique capabilities and underlying physics. The systems described are kilovolt and megavolt cone-beam CT, fan-beam MVCT, and CT-on-rails. A summary of the literature describing current clinical usage is also provided. Results: This report proposes a generic quality assurance program for CT-based IGRT systems in an effort to provide a vendor-independent program for clinical users. Published data from long-term, repeated quality control tests form the basis of the proposed test frequencies and tolerances.Conclusion: A program for quality control of CT-based image-guidance systems has been produced, with focus on geometry, image quality, image dose, system operation, and safety. Agreement and clarification with respect to reports from the AAPM TG-101, TG-104, TG-142, and TG-148 has been addressed.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2012

Dosimetric and deformation effects of image-guided interventions during stereotactic body radiation therapy of the prostate using an endorectal balloon

Purpose: During stereotactic body radiation therapy (SBRT) for the treatment of prostate cancer, an inflatable endorectal balloon (ERB) may be used to reduce motion of the target and reduce the dose to the posterior rectal wall. This work assessed the dosimetric impact of manual interventions on ERB position in patients receiving prostate SBRT and investigated the impact of ERB interventions on prostate shape. Methods: The data of seven consecutive patients receiving SBRT for the treatment of clinical stage T1cN0M0 prostate cancer enrolled in a multi-institutional, IRB-approved trial were analyzed. The SBRT dose was 50 Gy in five fractions to a planning target volume (PTV) that included the prostate (implanted with three fiducial markers) with a 3-5 mm margin. All plans were based on simulation images that included an ERB inflated with 60 cm{sup 3} of air. Daily kilovoltage cone-beam computed tomography (CBCT) imaging was performed to localize the PTV, and an automated fusion with the planning images yielded displacements required for PTV relocalization. When the ERB volume and/or position were judged to yield inaccurate repositioning, manual adjustment (ERB reinflation and/or repositioning) was performed. Based on all 59 CBCT image sets acquired, a deformable registration algorithm was used to determine the dose received by, displacement of, and deformation of the prostate, bladder (BLA), and anterior rectal wall (ARW). This dose tracking methodology was applied to images taken before and after manual adjustment of the ERB (intervention), and the delivered dose was compared to that which would have been delivered in the absence of intervention. Results: Interventions occurred in 24 out of 35 (69%) of the treated fractions. The direct effect of these interventions was an increase in the prostate radiation dose that included 95% of the PTV (D95) from 9.6 {+-} 1.0 to 10.0 {+-} 0.2 Gy (p = 0.06) and an increase in prostate coverage from 94.0% {+-} 8.5% to 97.8% {+-} 1.9% (p = 0.03). Additionally, ERB interventions reduced prostate deformation in the anterior-posterior (AP) direction, reduced errors in the sagittal rotation of the prostate, and increased the similarity in shape of the prostate to the radiotherapy plan (increased Dice coefficient from 0.76 {+-} 0.06 to 0.80 {+-} 0.04, p = 0.01). Postintervention decreases in prostate volume receiving less than the prescribed dose and decreases in the voxel-wise displacement of the prostate, bladder, and anterior rectal wall were observed, which resulted in improved dose-volume histogram (DVH) characteristics. Conclusions: Image-guided interventions in ERB volume and/or position during prostate SBRT were necessary to ensure the delivery of the dose distribution as planned. ERB interventions resulted in reductions in prostate deformations that would have prevented accurate localization of patient anatomy.

60 APPLIED LIFE SCIENCES↗