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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

TQEA: Temporal Quantum Error Analysis

For the last few years quantum computers have captured researchers’ interest in solving complicated problems in finance, chemistry, machine learning, and many other NP problems. Quantum computing benefits from parallel computation, entanglement, and calculation speedup compared to classical computing. Even though the operations and number of qubits in quantum computers are limited, previous research showed that using quantum computers can be beneficial. These benefits gained researchers’ interest to work on the improvement of quantum computers and overcome the limitations by building more advanced quantum computers with fault tolerance capability, more physical qubits, and better operational gates. In practice, Quantum applications would be translated into Quantum Circuits and each circuit can contain one or many entanglements (s), single qubits operations, and readouts. The importance of running Quantum applications with controlled errors or without errors is critical to the desired result from Quantum readout. In this paper, we study the characteristics of superconducting quantum machines along with the previously proposed methods of Quantum error detection/correction and in order to build a better error model we propose a temporal method for analyzing the the behavior of qubits errors (T1 and T2), frequency of operation, entanglement errors, and readout errors. In order to detect and predict the behavior of each machine, we use simple exponential smoothing analysis on heuristic calibration data. Finally, based on observation, we show that based on frequency, qubits, readout and single operation errors we can build a temporal based model to study and predict the outcome of runs on each Quantum machine.

Baheri, Betis↗
DOE OSTI2013

Effects of error covariance structure on estimation of model averaging weights and predictive performance

When conducting model averaging for assessing groundwater conceptual model uncertainty, the averaging weights are often evaluated using model selection criteria such as AIC, AICc, BIC, and KIC (Akaike Information Criterion, Corrected Akaike Information Criterion, Bayesian Information Criterion, and Kashyap Information Criterion, respectively). However, this method often leads to an unrealistic situation in which the best model receives overwhelmingly large averaging weight (close to 100%), which cannot be justified by available data and knowledge. It was found in this study that this problem was caused by using the covariance matrix, CE, of measurement errors for estimating the negative log likelihood function common to all the model selection criteria. This problem can be resolved by using the covariance matrix, Cek, of total errors (including model errors and measurement errors) to account for the correlation between the total errors. An iterative two-stage method was developed in the context of maximum likelihood inverse modeling to iteratively infer the unknown Cek from the residuals during model calibration. The inferred Cek was then used in the evaluation of model selection criteria and model averaging weights. While this method was limited to serial data using time series techniques in this study, it can be extended to spatial data using geostatistical techniques. The method was first evaluated in a synthetic study and then applied to an experimental study, in which alternative surface complexation models were developed to simulate column experiments of uranium reactive transport. It was found that the total errors of the alternative models were temporally correlated due to the model errors. The iterative two-stage method using Cek resolved the problem that the best model receives 100% model averaging weight, and the resulting model averaging weights were supported by the calibration results and physical understanding of the alternative models. Using Cek obtained from the iterative two-stage method also improved predictive performance of the individual models and model averaging in both synthetic and experimental studies.

error correlation↗
NASA NTRS1994

A New Class of Single Event Hard Errors

This paper reports on hard errors induced by single ions in dynamic memories. For ions with atomic number below 80, hard errors in DRAMs appear to be similar to the hard errors reported in previous work on SRAMs. One feature of these hard errors is that they tend to recover gradually with time, because of annealing, and are thus partially recoverable. However, for gold ions, a second type of hard error was discovered which is not recoverable, and appears to be due to catastrophic internal shorting rather than small changes in leakage current. Thus, nonrecoverable errors will likely occur even in devices which eliminate the extreme sensitivity to leakage current that is inherent in 4-T SRAMs and DRAMs. It is important to understand the mechanism that is responsible for nonrecoverable errors, and investigate the effect of device scaling.

single event hard errors DRAM SRAM ions gold ions ↗
DOE OSTI2020

Time-series machine-learning error models for approximate solutions to parameterized dynamical systems

This work proposes a machine-learning framework for modeling the error incurred by approximate solutions to parameterized dynamical systems. In particular, we extend the machine-learning error models (MLEM) framework proposed in Ref. Freno and Carlberg (2019) to dynamical systems. The proposed Time-Series Machine-Learning Error Modeling (T-MLEM) method constructs a regression model that maps features – which comprise error indicators that are derived from standard a posteriori error-quantification techniques – to a random variable for the approximate-solution error at each time instance. The proposed framework considers a wide range of candidate features, regression methods, and additive noise models. We consider primarily recursive regression techniques developed for time-series modeling, including both classical time-series models (e.g., autoregressive models) and recurrent neural networks (RNNs), but also analyze standard non-recursive regression techniques (e.g., feed-forward neural networks) for comparative purposes. Finally, numerical experiments conducted on multiple benchmark problems illustrate that the long short-term memory (LSTM) neural network, which is a type of RNN, outperforms other methods and yields substantial improvements in error predictions over traditional approaches.

42 ENGINEERING↗
DOE OSTI2012

Modern Palliative Radiation Treatment: Do Complexity and Workload Contribute to Medical Errors?

Purpose: To examine whether treatment workload and complexity associated with palliative radiation therapy contribute to medical errors. Methods and Materials: In the setting of a large academic health sciences center, patient scheduling and record and verification systems were used to identify patients starting radiation therapy. All records of radiation treatment courses delivered during a 3-month period were retrieved and divided into radical and palliative intent. 'Same day consultation, planning and treatment' was used as a proxy for workload and 'previous treatment' and 'multiple sites' as surrogates for complexity. In addition, all planning and treatment discrepancies (errors and 'near-misses') recorded during the same time frame were reviewed and analyzed. Results: There were 365 new patients treated with 485 courses of palliative radiation therapy. Of those patients, 128 (35%) were same-day consultation, simulation, and treatment patients; 166 (45%) patients had previous treatment; and 94 (26%) patients had treatment to multiple sites. Four near-misses and 4 errors occurred during the audit period, giving an error per course rate of 0.82%. In comparison, there were 10 near-misses and 5 errors associated with 1100 courses of radical treatment during the audit period. This translated into an error rate of 0.45% per course. An association was found between workload and complexity and increased palliative therapy error rates. Conclusions: Increased complexity and workload may have an impact on palliative radiation treatment discrepancies. This information may help guide the necessary recommendations for process improvement for patients who require palliative radiation therapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Treatment Planning System Calculation Errors Are Present in Most Imaging and Radiation Oncology Core-Houston Phantom Failures

Purpose: The anthropomorphic phantom program at the Houston branch of the Imaging and Radiation Oncology Core (IROC-Houston) is an end-to-end test that can be used to determine whether an institution can accurately model, calculate, and deliver an intensity modulated radiation therapy dose distribution. Currently, institutions that do not meet IROC-Houston's criteria have no specific information with which to identify and correct problems. In the present study, an independent recalculation system was developed to identify treatment planning system (TPS) calculation errors. Methods and Materials: A recalculation system was commissioned and customized using IROC-Houston measurement reference dosimetry data for common linear accelerator classes. Using this system, 259 head and neck phantom irradiations were recalculated. Both the recalculation and the institution's TPS calculation were compared with the delivered dose that was measured. In cases in which the recalculation was statistically more accurate by 2% on average or 3% at a single measurement location than was the institution's TPS, the irradiation was flagged as having a “considerable” institutional calculation error. The error rates were also examined according to the linear accelerator vendor and delivery technique. Results: Surprisingly, on average, the reference recalculation system had better accuracy than the institution's TPS. Considerable TPS errors were found in 17% (n=45) of the head and neck irradiations. Also, 68% (n=13) of the irradiations that failed to meet the IROC-Houston criteria were found to have calculation errors. Conclusions: Nearly 1 in 5 institutions were found to have TPS errors in their intensity modulated radiation therapy calculations, highlighting the need for careful beam modeling and calculation in the TPS. An independent recalculation system can help identify the presence of TPS errors and pass on the knowledge to the institution.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2017

Stochastic goal-oriented error estimation with memory

We propose a stochastic dual-weighted error estimator for the viscous shallow-water equation with boundaries. For this purpose, previous work on memory-less stochastic dual-weighted error estimation is extended by incorporating memory effects. The memory is introduced by describing the local truncation error as a sum of time-correlated random variables. The random variables itself represent the temporal fluctuations in local truncation errors and are estimated from high-resolution information at near-initial times. The resulting error estimator is evaluated experimentally in two classical ocean-type experiments, the Munk gyre and the flow around an island. In these experiments, the stochastic process is adapted locally to the respective dynamical flow regime. Our stochastic dual-weighted error estimator is shown to provide meaningful error bounds for a range of physically relevant goals. We prove, as well as show numerically, that our approach can be interpreted as a linearized stochastic-physics ensemble.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Residual-based error correction for neural operator accelerated infinite-dimensional Bayesian inverse problems

We explore using neural operators, or neural network representations of nonlinear maps between function spaces, to accelerate infinite-dimensional Bayesian inverse problems (BIPs) with models governed by nonlinear parametric partial differential equations (PDEs). Neural operators have gained significant attention in recent years for their ability to approximate the parameter-to-solution maps defined by PDEs using as training data solutions of PDEs at a limited number of parameter samples. The computational cost of BIPs can be drastically reduced if the large number of PDE solves required for posterior characterization are replaced with evaluations of trained neural operators. However, reducing error in the resulting BIP solutions via reducing the approximation error of the neural operators in training can be challenging and unreliable. We provide an a priori error bound result that implies certain BIPs can be ill-conditioned to the approximation error of neural operators, thus leading to inaccessible accuracy requirements in training. To reliably deploy neural operators in BIPs, we consider a strategy for enhancing the performance of neural operators: correcting the prediction of a trained neural operator by solving a linear variational problem based on the PDE residual. We show that a trained neural operator with error correction can achieve a quadratic reduction of its approximation error, all while retaining substantial computational speedups of posterior sampling when models are governed by highly nonlinear PDEs. The strategy is applied to two numerical examples of BIPs based on a nonlinear reaction–diffusion problem and deformation of hyperelastic materials. We demonstrate that posterior representations of the two BIPs produced using trained neural operators are greatly and consistently enhanced by error correction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Motoneuron axon pathfinding errors in zebrafish: Differential effects related to concentration and timing of nicotine exposure

Nicotine exposure during embryonic stages of development can affect many neurodevelopmental processes. In the developing zebrafish, exposure to nicotine was reported to cause axonal pathfinding errors in the later born secondary motoneurons (SMNs). These alterations in SMN axon morphology coincided with muscle degeneration at high nicotine concentrations (15–30 μM). Previous work showed that the paralytic mutant zebrafish known as sofa potato exhibited nicotine-induced effects onto SMN axons at these high concentrations but in the absence of any muscle deficits, indicating that pathfinding errors could occur independent of muscle effects. In this study, we used varying concentrations of nicotine at different developmental windows of exposure to specifically isolate its effects onto subpopulations of motoneuron axons. We found that nicotine exposure can affect SMN axon morphology in a dose-dependent manner. At low concentrations of nicotine, SMN axons exhibited pathfinding errors, in the absence of any nicotine-induced muscle abnormalities. Moreover, the nicotine exposure paradigms used affected the 3 subpopulations of SMN axons differently, but the dorsal projecting SMN axons were primarily affected. We then identified morphologically distinct pathfinding errors that best described the nicotine-induced effects on dorsal projecting SMN axons. To test whether SMN pathfinding was potentially influenced by alterations in the early born primary motoneuron (PMN), we performed dual labeling studies, where both PMN and SMN axons were simultaneously labeled with antibodies. We show that only a subset of the SMN axon pathfinding errors coincided with abnormal PMN axonal targeting in nicotine-exposed zebrafish. We conclude that nicotine exposure can exert differential effects depending on the levels of nicotine and developmental exposure window. - Highlights: • Embryonic nicotine exposure can specifically affect secondary motoneuron axons in a dose-dependent manner. • The nicotine-induced secondary motoneuron axonal pathfinding errors can occur independent of any muscle fiber alterations. • Nicotine exposure primarily affects dorsal projecting secondary motoneurons axons. • Nicotine-induced primary motoneuron axon pathfinding errors can influence secondary motoneuron axon morphology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Quantifying UAS Observation Error Variance Used in Data Assimilation Systems and Its Impact on Predictive Skill

Observation error determines the weights of the observations and background state used in data assimilation to generate analyses. Quantifying observation error is critical for the optimal assimilation of observational data sets. Uncrewed Aircraft System (UAS) observations have shown potential benefits in filling observational gaps in the lower atmosphere; however, characterization of their error characteristics has been limited. To optimize the use of UAS observations in numerical weather prediction, UAS observation error is estimated based on the 3‐cornered hat diagnostic approach which uses three independent estimates of the atmospheric state. This approach is applied to data from the 2018 Lower Atmospheric Profiling Studies at Elevation‐a Remotely‐piloted Aircraft Team Experiment field campaign using collocated UAS and rawinsonde observations along with output from a set of convection‐permitting model simulations. The estimated observation error values for UAS temperature, wind, and relative humidity measurements were found to be only weakly dependent on height AGL with mean values equal to 0.5°C, 0.8 m s −1 , and 3%, respectively. Only the newly estimated observation error for temperature differed from that previously used to assimilate commercial aircraft observations into global models (1.0°C). However, using this reduced temperature observation error produced more accurate mesoscale analyses and forecasts of both terrain‐driven flows and convection initiation generated by colliding outflow boundaries within the San Luis Valley of Colorado.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

SYSTEMATIC CONTINUUM ERRORS IN THE Ly{alpha} FOREST AND THE MEASURED TEMPERATURE-DENSITY RELATION

Continuum fitting uncertainties are a major source of error in estimates of the temperature-density relation (usually parameterized as a power-law, T {proportional_to} {Delta}{sup {gamma}-1}) of the intergalactic medium through the flux probability distribution function (PDF) of the Ly{alpha} forest. Using a simple order-of-magnitude calculation, we show that few percent-level systematic errors in the placement of the quasar continuum due to, e.g., a uniform low-absorption Gunn-Peterson component could lead to errors in {gamma} of the order of unity. This is quantified further using a simple semi-analytic model of the Ly{alpha} forest flux PDF. We find that under(over)estimates in the continuum level can lead to a lower (higher) measured value of {gamma}. By fitting models to mock data realizations generated with current observational errors, we find that continuum errors can cause a systematic bias in the estimated temperature-density relation of ({delta}({gamma})) Almost-Equal-To -0.1, while the error is increased to {sigma}{sub {gamma}} Almost-Equal-To 0.2 compared to {sigma}{sub {gamma}} Almost-Equal-To 0.1 in the absence of continuum errors.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2014

SU-E-J-94: Positioning Errors Resulting From Using Bony Anatomy Alignment for Treating SBRT Lung Tumor

Purpose: To quantify patient setups errors based on bony anatomy registration rather than 3D tumor alignment for SBRT lung treatments. Method: A retrospective study was performed for patients treated with lung SBRT and imaged with kV cone beam computed tomography (kV-CBCT) image-guidance. Daily CBCT images were registered to treatment planning CTs based on bony anatomy alignment and then inter-fraction tumor movement was evaluated by comparing shift in the tumor center in the medial-lateral, anterior-posterior, and superior-inferior directions. The PTV V100% was evaluated for each patient based on the average daily tumor displacement to assess the impact of the positioning error on the target coverage when the registrations were based on bony anatomy. Of the 35 patients studied, 15 were free-breathing treatments, 10 used abdominal compression with a stereotactic body frame, and the remaining 10 were performed with BodyFIX vacuum bags. Results: For free-breathing treatments, the range of tumor displacement error is between 1–6 mm in the medial-lateral, 1–13 mm in the anterior-posterior, and 1–7 mm in the superior-inferior directions. These positioning errors lead to 6–22% underdose coverage for PTV - V100% . Patients treated with abdominal compression immobilization showed positional errors of 0–4mm mediallaterally, 0–3mm anterior-posteriorly, and 0–2 mm inferior-superiorly with PTV - V100% underdose ranging between 6–17%. For patients immobilized with the vacuum bags, the positional errors were found to be 0–1 mm medial-laterally, 0–1mm anterior-posteriorly, and 0–2 mm inferior-superiorly with PTV - V100% under dose ranging between 5–6% only. Conclusion: It is necessary to align the tumor target by using 3D image guidance to ensure adequate tumor coverage before performing SBRT lung treatments. The BodyFIX vacuum bag immobilization method has the least positioning errors among the three methods studied when bony anatomy is used for registration.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

SU-E-T-132: Dosimetric Impact of Positioning Errors in Hypo-Fractionated Cranial Radiation Therapy Using Frameless Stereotactic BrainLAB System

Purpose: To determine dosimetric impact of positioning errors in the stereotactic hypo-fractionated treatment of intracranial lesions using 3Dtransaltional and 3D-rotational corrections (6D) frameless BrainLAB ExacTrac X-Ray system. Methods: 20 cranial lesions, treated in 3 or 5 fractions, were selected. An infrared (IR) optical positioning system was employed for initial patient setup followed by stereoscopic kV X-ray radiographs for position verification. 6D-translational and rotational shifts were determined to correct patient position. If these shifts were above tolerance (0.7 mm translational and 1° rotational), corrections were applied and another set of X-rays was taken to verify patient position. Dosimetric impact (D95, Dmin, Dmax, and Dmean of planning target volume (PTV) compared to original plans) of positioning errors for initial IR setup (XC: Xray Correction) and post-correction (XV: X-ray Verification) was determined in a treatment planning system using a method proposed by Yue et al. (Med. Phys. 33, 21-31 (2006)) with 3D-translational errors only and 6D-translational and rotational errors. Results: Absolute mean translational errors (±standard deviation) for total 92 fractions (XC/XV) were 0.79±0.88/0.19±0.15 mm (lateral), 1.66±1.71/0.18 ±0.16 mm (longitudinal), 1.95±1.18/0.15±0.14 mm (vertical) and rotational errors were 0.61±0.47/0.17±0.15° (pitch), 0.55±0.49/0.16±0.24° (roll), and 0.68±0.73/0.16±0.15° (yaw). The average changes (loss of coverage) in D95, Dmin, Dmax, and Dmean were 4.5±7.3/0.1±0.2%, 17.8±22.5/1.1±2.5%, 0.4±1.4/0.1±0.3%, and 0.9±1.7/0.0±0.1% using 6Dshifts and 3.1±5.5/0.0±0.1%, 14.2±20.3/0.8±1.7%, 0.0±1.2/0.1±0.3%, and 0.7±1.4/0.0±0.1% using 3D-translational shifts only. The setup corrections (XC-XV) improved the PTV coverage by 4.4±7.3% (D95) and 16.7±23.5% (Dmin) using 6D adjustment. Strong correlations were observed between translation errors and deviations in dose coverage for XC. Conclusion: The initial BrainLAB IR system based on rigidity of the mask-frame setup is not sufficient for accurate stereotactic positioning; however, with X-ray imageguidance sub-millimeter accuracy is achieved with negligible deviations in dose coverage. The angular corrections (mean angle summation=1.84°) are important and cause considerable deviations in dose coverage.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

TH-E-BRE-07: Development of Dose Calculation Error Predictors for a Widely Implemented Clinical Algorithm

Purpose: Several shortcomings of the current implementation of the analytic anisotropic algorithm (AAA) may lead to dose calculation errors in highly modulated treatments delivered to highly heterogeneous geometries. Here we introduce a set of dosimetric error predictors that can be applied to a clinical treatment plan and patient geometry in order to identify high risk plans. Once a problematic plan is identified, the treatment can be recalculated with more accurate algorithm in order to better assess its viability. Methods: Here we focus on three distinct sources dosimetric error in the AAA algorithm. First, due to a combination of discrepancies in smallfield beam modeling as well as volume averaging effects, dose calculated through small MLC apertures can be underestimated, while that behind small MLC blocks can overestimated. Second, due the rectilinear scaling of the Monte Carlo generated pencil beam kernel, energy is not properly transported through heterogeneities near, but not impeding, the central axis of the beamlet. And third, AAA overestimates dose in regions very low density (< 0.2 g/cm{sup 3}). We have developed an algorithm to detect the location and magnitude of each scenario within the patient geometry, namely the field-size index (FSI), the heterogeneous scatter index (HSI), and the lowdensity index (LDI) respectively. Results: Error indices successfully identify deviations between AAA and Monte Carlo dose distributions in simple phantom geometries. Algorithms are currently implemented in the MATLAB computing environment and are able to run on a typical RapidArc head and neck geometry in less than an hour. Conclusion: Because these error indices successfully identify each type of error in contrived cases, with sufficient benchmarking, this method can be developed into a clinical tool that may be able to help estimate AAA dose calculation errors and when it might be advisable to use Monte Carlo calculations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-I-83: Error Analysis of Multi-Modality Image-Based Volumes of Rodent Solid Tumors Using a Preclinical Multi-Modality QA Phantom

Purpose: In our previous study a preclinical multi-modality quality assurance (QA) phantom that contains five tumor-simulating test objects with 2, 4, 7, 10 and 14 mm diameters was developed for accurate tumor size measurement by researchers during cancer drug development and testing. This study analyzed the errors during tumor volume measurement from preclinical magnetic resonance (MR), micro-computed tomography (micro- CT) and ultrasound (US) images acquired in a rodent tumor model using the preclinical multi-modality QA phantom. Methods: Using preclinical 7-Tesla MR, US and micro-CT scanners, images were acquired of subcutaneous SCC4 tumor xenografts in nude rats (3–4 rats per group; 5 groups) along with the QA phantom using the same imaging protocols. After tumors were excised, in-air micro-CT imaging was performed to determine reference tumor volume. Volumes measured for the rat tumors and phantom test objects were calculated using formula V = (π/6)*a*b*c where a, b and c are the maximum diameters in three perpendicular dimensions determined by the three imaging modalities. Then linear regression analysis was performed to compare image-based tumor volumes with the reference tumor volume and known test object volume for the rats and the phantom respectively. Results: The slopes of regression lines for in-vivo tumor volumes measured by three imaging modalities were 1.021, 1.101 and 0.862 for MRI, micro-CT and US respectively. For phantom, the slopes were 0.9485, 0.9971 and 0.9734 for MRI, micro-CT and US respectively. Conclusion: For both animal and phantom studies, random and systematic errors were observed. Random errors were observer-dependent and systematic errors were mainly due to selected imaging protocols and/or measurement method. In the animal study, there were additional systematic errors attributed to ellipsoidal assumption for tumor shape. The systematic errors measured using the QA phantom need to be taken into account to reduce measurement errors during the animal study.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-334: Dosimetric Impacts of Organ at Risk (OAR) Contour Errors in Prostate Volumetric Modulated Arc Therapy (VMAT) Planning: A Sensitivity Study

Purpose: To investigate the dosimetric impacts of OAR contour errors in prostate VMAT planning with a sensitivity analysis method. Methods: Ten randomly selected prostate VMAT patients were used to simulate OAR contour error in rectum and bladder. For each OAR 12 even spaced contour control points were selected in every three slices. For each simulation, only one of the control points was simulated to move inwards or outwards by up to 6 voxels and coordinates of adjacent voxels to that moving control point were moved accordingly with a 3D-spline smooth function. An in-house software was used to predict OARs dosimetric endpoints based on geometric relationship between PTV and OAR. In this study, the V75, V70, V65, V60 of rectum and V80, V75, V70, V65 of bladder with and without perturbed contours were calculated and compared. Results: The percentage of OAR dose volume difference around the reference OAR contours were plotted as iso-error lines overlaid on CT images. The significant difference was shown in OAR contours adjacent to the PTV where high dose gradient exists. When one of the 12 points adjacent to the PTV moved up to 6 voxels, maximum errors of 1.99%, 2.46%, 2.89%, and 3.19% were found in V75, V70, V65, and V60 of the rectum, and 1.59%, 1.67%, 1.87%, and 1.90% were found in V80, V75, V70, and V65 of the bladder, respectively. Conclusion: We can quantify the dosimetric impact of OAR contouring error by evaluating the percentage of OAR dose volume changes in prostate VMAT planning. For the adaptive radiation therapy, the iso-error lines can provide the planner a guideline that which portion of contour needed to be checked carefully because dosimetric sensitivity of contour errors are different for different part of contours.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-310: Does a Head-Mounted Ionization Chamber Detect IMRT Errors?

Purpose: The conventional plan verification strategy is delivering a plan to a QA-phantom before the first treatment. Monitoring each fraction of the patient treatment in real-time would improve patient safety. We evaluated how well a new detector, the IQM (iRT Systems, Germany), is capable of detecting errors we induced into IMRT plans of three different treatment regions. Results were compared to an established phantom. Methods: Clinical plans of a brain, prostate and head-and-neck patient were modified in the Pinnacle planning system, such that they resulted in either several percent lower prescribed doses to the target volume or several percent higher doses to relevant organs at risk. Unaltered plans were measured on three days, modified plans once, each with the IQM at an Elekta Synergy with an Agility MLC. All plans were also measured with the ArcCHECK with the cavity plug and a PTW semiflex 31010 ionization chamber inserted. Measurements were evaluated with SNC patient software. Results: Repeated IQM measurements of the original plans were reproducible, such that a 1% deviation from the mean as warning and 3% as action level as suggested by the manufacturer seemed reasonable. The IQM detected most of the simulated errors including wrong energy, a faulty leaf, wrong trial exported and a 2 mm shift of one leaf bank. Detection limits were reached for two plans - a 2 mm field position error and a leaf bank offset combined with an MU change. ArcCHECK evaluation according to our current standards also left undetected errors. Ionization chamber evaluation alone would leave most errors undetected. Conclusion: The IQM detected most errors and performed as well as currently established phantoms with the advantage that it can be used throughout the whole treatment. Drawback is that it does not indicate the source of the error.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-381: Fast Calculation of Three-Dimensional Dose Considering MLC Leaf Positional Errors for VMAT Plans

Purpose: In this study, we developed a system to calculate three dimensional (3D) dose that reflects dosimetric error caused by leaf miscalibration for head and neck and prostate volumetric modulated arc therapy (VMAT) without additional treatment planning system calculation on real time. Methods: An original system called clarkson dose calculation based dosimetric error calculation to calculate dosimetric error caused by leaf miscalibration was developed by MATLAB (Math Works, Natick, MA). Our program, first, calculates point doses at isocenter for baseline and modified VMAT plan, which generated by inducing MLC errors that enlarged aperture size of 1.0 mm with clarkson dose calculation. Second, error incuced 3D dose was generated with transforming TPS baseline 3D dose using calculated point doses. Results: Mean computing time was less than 5 seconds. For seven head and neck and prostate plans, between our method and TPS calculated error incuced 3D dose, the 3D gamma passing rates (0.5%/2 mm, global) are 97.6±0.6% and 98.0±0.4%. The dose percentage change with dose volume histogram parameter of mean dose on target volume were 0.1±0.5% and 0.4±0.3%, and with generalized equivalent uniform dose on target volume were −0.2±0.5% and 0.2±0.3%. Conclusion: The erroneous 3D dose calculated by our method is useful to check dosimetric error caused by leaf miscalibration before pre treatment patient QA dosimetry checks.

60 APPLIED LIFE SCIENCES↗