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DOE OSTI2014

SU-F-BRE-01: A Rapid Method to Determine An Upper Limit On a Radiation Detector's Correction Factor During the QA of IMRT Plans

Purpose: Discrepancies in the verification of the absorbed dose to water from an IMRT plan using a radiation dosimeter can be wither caused by 1) detector specific nonstandard field correction factors as described by the formalism of Alfonso et al. 2) inaccurate delivery of the DQA plan. The aim of this work is to develop a simple/fast method to determine an upper limit on the contribution of composite field correction factors to these discrepancies. Methods: Indices that characterize the non-flatness of the symmetrised collapsed delivery (VSC) of IMRT fields over detector-specific regions of interest were shown to be correlated with IMRT field correction factors. The indices introduced are the uniformity index (UI) and the mean fluctuation index (MF). Each one of these correlation plots have 10 000 fields generated with a stochastic model. A total of eight radiation detectors were investigated in the radial orientation. An upper bound on the correction factors was evaluated by fitting values of high correction factors for a given index value. Results: These fitted curves can be used to compare the performance of radiation dosimeters in composite IMRT fields. Highly water-equivalent dosimeters like the scintillating detector (Exradin W1) and a generic alanine detector have been found to have corrections under 1% over a broad range of field modulations (0 – 0.12 for MF and 0 – 0.5 for UI). Other detectors have been shown to have corrections of a few percent over this range. Finally, a full Monte Carlo simulations of 18 clinical and nonclinical IMRT field showed good agreement with the fitted curve for the A12 ionization chamber. Conclusion: This work proposes a rapid method to evaluate an upper bound on the contribution of correction factors to discrepancies found in the verification of DQA plans.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2014

TU-C-17A-08: Improving IMRT Planning and Reducing Inter-Planner Variability Using the Stochastic Frontier Method: Validation Based On Clinical and Simulated Data

Purpose: Intensity modulated radiation therapy always requires compromises between PTV coverage and organs at risk (OAR) sparing. We previously developed metrics that correlate doses to OAR to specific patients’ morphology using stochastic frontier analysis (SFA). Here, we aim to examine the validity of this approach using a large set of realistically simulated dosimetric and geometric data. Methods: SFA describes a set of treatment plans as an asymmetric distribution with respect to a frontier defining optimal plans. Eighty head and neck IMRT plans were used to establish a metric predicting the mean dose to parotids as a function of simple geometric parameters. A database of 140 parotids was used as a basis distribution to simulate physically plausible data of geometry and dose. Distributions comprising between 20 and 5000 were simulated and the SFA was applied to obtain new frontiers, which were compared to the original frontier. Results: It was possible to simulate distributions consistent with the original dataset. Below 160 organs, the SFA could not always describe distributions as asymmetric: a few cases showed a Gaussian or half-Gaussian distribution. In order to converge to a stable solution, the number of organs in a distribution must ideally be above 100, but in many cases stable parameters could be achieved with as low as 60 samples of organ data. Mean RMS value of the error of new frontiers was significantly reduced when additional organs are used. Conclusion: The number of organs in a distribution showed to have an impact on the effectiveness of the model. It is always possible to obtain a frontier, but if the number of organs in the distribution is small (< 160), it may not represent de lowest dose achievable. These results will be used to determine number of cases necessary to adapt the model to other organs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2014

TH-A-9A-02: BEST IN PHYSICS (THERAPY) - 4D IMRT Planning Using Highly- Parallelizable Particle Swarm Optimization

Purpose: We present a particle swarm optimization (PSO)-based 4D IMRT planning technique designed for dynamic MLC tracking delivery to lung tumors. The key idea is to utilize the temporal dimension as an additional degree of freedom rather than a constraint in order to achieve improved sparing of organs at risk (OARs). Methods: The target and normal structures were manually contoured on each of the ten phases of a 4DCT scan acquired from a lung SBRT patient who exhibited 1.5cm tumor motion despite the use of abdominal compression. Corresponding ten IMRT plans were generated using the Eclipse treatment planning system. These plans served as initial guess solutions for the PSO algorithm. Fluence weights were optimized over the entire solution space i.e., 10 phases × 12 beams × 166 control points. The size of the solution space motivated our choice of PSO, which is a highly parallelizable stochastic global optimization technique that is well-suited for such large problems. A summed fluence map was created using an in-house B-spline deformable image registration. Each plan was compared with a corresponding, internal target volume (ITV)-based IMRT plan. Results: The PSO 4D IMRT plan yielded comparable PTV coverage and significantly higher dose—sparing for parallel and serial OARs compared to the ITV-based plan. The dose-sparing achieved via PSO-4DIMRT was: lung Dmean = 28%; lung V20 = 90%; spinal cord Dmax = 23%; esophagus Dmax = 31%; heart Dmax = 51%; heart Dmean = 64%. Conclusion: Truly 4D IMRT that uses the temporal dimension as an additional degree of freedom can achieve significant dose sparing of serial and parallel OARs. Given the large solution space, PSO represents an attractive, parallelizable tool to achieve globally optimal solutions for such problems. This work was supported through funding from the National Institutes of Health and Varian Medical Systems. Amit Sawant has research funding from Varian Medical Systems, VisionRT Ltd. and Elekta.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

TU-F-CAMPUS-I-02: Validation of a CT X-Ray Source Characterization Technique for Dose Computation Using An Anthropomorphic Thorax Phantom

Purpose: To experimentally validate a rotational kV x-ray source characterization technique by computing CT dose in an anthropomorphic thorax phantom using an in-house dose computation algorithm (kVDoseCalc). Methods: The lateral variation in incident energy spectra of a GE Optima big bore CT scanner was found by measuring the HVL along the internal, full bow-tie filter axis. The HVL and kVp were used to generate the x-ray spectra using Spektr software, while beam fluence was derived by dividing the integral product of the spectra and in-air mass-energy absorption coefficients by in-air dose measurements along the bow-tie filter axis. Beams produced by the GE Optima scanner were modeled at 80 and 140 kVp tube settings. kVDoseCalc calculates dose by solving the linear Boltzmann transport equation using a combination of deterministic and stochastic methods. Relative doses in an anthropomorphic thorax phantom (E2E SBRT Phantom) irradiated by the GE Optima scanner were measured using a (0.015 cc) PTW Freiburg ionization chamber, and compared to computations from kVDoseCalc. Results: The agreement in relative dose between dose computation and measurement for points of interest (POIs) within the primary path of the beam was within experimental uncertainty for both energies, however points outside the primary beam were not. The average absolute percent difference for POIs within the primary path of the beam was 1.37% and 5.16% for 80 and 140 kVp, respectively. The minimum and maximum absolute percent difference for both energies and all POIs within the primary path of the beam was 0.151% and 6.41%, respectively. Conclusion: The CT x-ray source characterization technique based on HVL measurements and kVp can be used to accurately compute CT dose in an anthropomorphic thorax phantom.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Concurrent Monte Carlo transport and fluence optimization with fluence adjusting scalable transport Monte Carlo

Purpose: The future of radiation therapy will require advanced inverse planning solutions to support single-arc, multiple-arc, and “4π” delivery modes, which present unique challenges in finding an optimal treatment plan over a vast search space, while still preserving dosimetric accuracy. The successful clinical implementation of such methods would benefit from Monte Carlo (MC) based dose calculation methods, which can offer improvements in dosimetric accuracy when compared to deterministic methods. The standard method for MC based treatment planning optimization leverages the accuracy of the MC dose calculation and efficiency of well-developed optimization methods, by precalculating the fluence to dose relationship within a patient with MC methods and subsequently optimizing the fluence weights. However, the sequential nature of this implementation is computationally time consuming and memory intensive. Methods to reduce the overhead of the MC precalculation have been explored in the past, demonstrating promising reductions of computational time overhead, but with limited impact on the memory overhead due to the sequential nature of the dose calculation and fluence optimization. The authors propose an entirely new form of “concurrent” Monte Carlo treat plan optimization: a platform which optimizes the fluence during the dose calculation, reduces wasted computation time being spent on beamlets that weakly contribute to the final dose distribution, and requires only a low memory footprint to function. In this initial investigation, the authors explore the key theoretical and practical considerations of optimizing fluence in such a manner. Methods: The authors present a novel derivation and implementation of a gradient descent algorithm that allows for optimization during MC particle transport, based on highly stochastic information generated through particle transport of very few histories. A gradient rescaling and renormalization algorithm, and the concept of momentum from stochastic gradient descent were used to address obstacles unique to performing gradient descent fluence optimization during MC particle transport. The authors have applied their method to two simple geometrical phantoms, and one clinical patient geometry to examine the capability of this platform to generate conformal plans as well as assess its computational scaling and efficiency, respectively. Results: The authors obtain a reduction of at least 50% in total histories transported in their investigation compared to a theoretical unweighted beamlet calculation and subsequent fluence optimization method, and observe a roughly fixed optimization time overhead consisting of ∼10% of the total computation time in all cases. Finally, the authors demonstrate a negligible increase in memory overhead of ∼7–8 MB to allow for optimization of a clinical patient geometry surrounded by 36 beams using their platform. Conclusions: This study demonstrates a fluence optimization approach, which could significantly improve the development of next generation radiation therapy solutions while incurring minimal additional computational overhead.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-J-196: A Prototype System for Portal Imaging for Intensity Modulated Neutron Therapy

Purpose: Fast neutron therapy is offered at the University of Washington Medical Center for treatment of selected cancers. The hardware and control systems of the UW Clinical Neutron Therapy System are undergoing upgrades to enable delivery of IMNT. To clinically implement IMNT, dose verification tools need to be developed. We propose a portal imaging system that relies on the creation of positron emitting isotopes ({sup 11}C and {sup 15}O) through (n, 2n) reactions with a PMMA plate placed below the patient. After field delivery, the plate is retrieved from the vault and imaged using a reader that detects the annihilation photons. The pattern of activity produced in the plate provides information to reconstruct the neutron fluence map that can be compared to fluence maps from Monte Carlo (MCNP) simulations to verify treatment delivery. We have previously performed Monte Carlo simulations of the portal imaging system (GATE simulations) and the beam line (MCNP simulations). In this work, initial measurements using a prototype system are presented. Methods: Custom electronics were developed for BGO detectors read out with photomultiplier tubes (previous generation PET detectors from a CTI ECAT 953 scanner). Two detectors were placed in coincidence, with a detector separation of 2 cm. Custom software was developed to create the crystal look up tables and perform a limited angle planar reconstruction with a stochastic normalization. To test the initial capabilities of the system, PMMA squares were irradiated with neutrons at a depth of 1.5 cm and read out using the prototype system. Doses ranging from 10–200 cGy were delivered. Results: Using the prototype system, dose differences in the therapeutic range could be determined. Conclusion: The prototype portal imaging system is capable of detecting neutron doses as low as 10–50 cGy and shows great promise as a patient QA tool for IMNT.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-139: Meeting the Challenges of Quality Control in the TOPAS Monte Carlo Simulation Toolkit for Proton Therapy

Purpose: Monte Carlo particle transport simulation (MC) codes have become important tools in proton therapy and biology, both for research and practice. TOPAS is an MC toolkit serving users worldwide (213 licensed users at 95 institutions in 21 countries). It provides unprecedented ease in 4D placement of geometry components, beam sources and scoring through its user-friendly and reproducible parameter file interface. Quality control (QC) of stochastic simulation software is inherently difficult, and the versatility of TOPAS introduces additional challenges. But QC is vital as the TOPAS development team implements new features, addresses user feedback and reacts to upgrades of underlying software (i.e. Geant4). Methods: Whenever code is committed to our repository, over 50 separate module tests are automatically triggered via a continuous integration service. They check that the various module options execute successfully and that their results are statistically consistent with previous reference values. Prior to each software release, longer end-to-end tests automatically validate TOPAS against experimental data and a TOPAS benchmark. These include simulating multiple properties of spread-out Bragg peaks, validating nuclear models, and searching for differences in patient simulations. Results: Continuous integration has proven effective in catching regressions at the time they are introduced, particularly when implementing new features that involve refactoring code (e.g. multithreading and ntuple output). Code coverage statistics highlight untested portions of code and guide development of new tests. The various end-to-end tests demonstrate that TOPAS accurately describes the physics of proton therapy within clinical tolerances. Conclusion: The TOPAS QC strategy of frequent short tests and pre-release long tests has led to a more reliable tool. However, the versatility of TOPAS makes it difficult to predict how users might combine different modules, and so QC ultimately remains a partnership between the developer and the user. This work was funded by National Cancer Institute grant R01 CA 140735.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-182: A Stochastic Approach to Daily QA Tolerances On Spot Properties for Proton Pencil Beam Scanning

Purpose: Proton pencil beam scanning is used clinically across the United States. There are no current guidelines on tolerances for daily QA specific to pencil beam scanning, specifically related to the individual spot properties (spot width). Using a stochastic method to determine tolerances has the potential to optimize tolerances on individual spots and decrease the number of false positive failures in daily QA. Individual and global spot tolerances were evaluated. Methods: As part of daily QA for proton pencil beam scanning, a field of 16 spots (corresponding to 8 energies) is measured using an array of ion chambers (Matrixx, IBA). Each individual spot is fit to two Gaussian functions (x,y). The spot width (σ) in × and y are recorded (32 parameters). Results from the daily QA were retrospectively analyzed for 100 days of data. The deviations of the spot widths were histogrammed and fit to a Gaussian function. The stochastic spot tolerance was taken to be the mean ± 3σ. Using these results, tolerances were developed and tested against known deviations in spot width. Results: The individual spot tolerances derived with the stochastic method decreased in 30/32 instances. Using the previous tolerances (± 20% width), the daily QA would have detected 0/20 days of the deviation. Using a tolerance of any 6 spots failing the stochastic tolerance, 18/20 days of the deviation would have been detected. Conclusion: Using a stochastic method we have been able to decrease daily tolerances on the spot widths for 30/32 spot widths measured. The stochastic tolerances can lead to detection of deviations that previously would have been picked up on monthly QA and missed by daily QA. This method could be easily extended for evaluation of other QA parameters in proton spot scanning.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-362: Quantification and Modelling of the Ionization Chamber Simulation Effective Points On Monaco Treatment Planning System

Purpose: Because of statistical noise in Monte Carlo dose calculations, effective point doses may not be accurate. Volume spheres are useful for evaluating dose in Monte Carlo plans, which have an inherent statistical uncertainty.We use a user-defined sphere volume instead of a point, take sphere sampling around effective point make the dose statistics to decrease the stochastic errors. Methods: Direct dose measurements were made using a 0.125cc Semiflex ion chamber (IC) 31010 isocentrically placed in the center of a homogeneous Cylindric sliced RW3 phantom (PTW, Germany).In the scanned CT phantom series the sensitive volume length of the IC (6.5mm) were delineated and defined the isocenter as the simulation effective points. All beams were simulated in Monaco in accordance to the measured model. In our simulation using 2mm voxels calculation grid spacing and choose calculate dose to medium and request the relative standard deviation ≤0.5%. Taking three different assigned IC over densities (air electron density(ED) as 0.01g/cm3 default CT scanned ED and Esophageal lumen ED 0.21g/cm3) were tested at different sampling sphere radius (2.5, 2, 1.5 and 1 mm) statistics dose were compared with the measured does. Results: The results show that in the Monaco TPS for the IC using Esophageal lumen ED 0.21g/cm3 and sampling sphere radius 1.5mm the statistical value is the best accordance with the measured value, the absolute average percentage deviation is 0.49%. And when the IC using air electron density(ED) as 0.01g/cm3 and default CT scanned EDthe recommented statistical sampling sphere radius is 2.5mm, the percentage deviation are 0.61% and 0.70%, respectivly. Conclusion: In Monaco treatment planning system for the ionization chamber 31010 recommend air cavity using ED 0.21g/cm3 and sampling 1.5mm sphere volume instead of a point dose to decrease the stochastic errors. Funding Support No.C201505006.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-681: Does the Biophysical Modeling for Immunological Aspects in Radiotherapy Precisely Predict Tumor and Normal Tissue Responses?

Purpose: Recent advances in immunotherapy make possible to combine with radiotherapy. The aim of this study was to assess the TCP/NTCP model with immunological aspects including stochastic distribution as intercellular uncertainties. Methods: In the clinical treatment planning system (Eclipse ver.11.0, Varian medical systems, US), biological parameters such as α/β, D50, γ, n, m, TD50 including repair parameters (bi-exponential repair) can be set as any given values to calculate the TCP/NTCP. Using a prostate cancer patient data with VMAT commissioned as a 6-MV photon beam of Novalis-Tx (BrainLab, US) in clinical use, the fraction schedule were hypothesized as 70–78Gy/35–39fr, 72–81Gy/40–45fr, 52.5–66Gy/16–22fr, 35–40Gy/5fr of 5–7 fractions in a week. By use of stochastic biological model applying for Gaussian distribution, the effects of the TCP/NTCP variation of repair parameters of the immune system as well as the intercellular uncertainty of tumor and normal tissues have been evaluated. Results: As respect to the difference of the α/β, the changes of the TCP/NTCP were increased in hypo-fraction regimens. The difference between the values of n and m affect the variation of the NTCP with the fraction schedules, independently. The elongation of repair half-time (long) increased the TCP/NTCP twice or much higher in the case of hypo-fraction scheme. For tumor, the repopulation parameters such as Tpot and Tstart, which is immunologically working to the tumor, improved TCP. Conclusion: Compared to default fixed value, which has affected by the probability of cell death and cure, hypo-fractionation schemes seemed to have advantages for the variations of the values of m. The possibility of an increase of the α/β or TD50 and repair parameters in tumor and normal tissue by immunological aspects were highly expected. For more precise prediction, treatment planning systems should be incorporated the complicated biological optimization in clinical practice combined with basic experiments data.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-G-201-09: Evaluation of a Novel Machine-Learning Algorithm for Permanent Prostate Brachytherapy Treatment Planning

Purpose: A novel, automated, algorithm for permanent prostate brachytherapy (PPB) treatment planning has been developed. The novel approach uses machine-learning (ML), a form of artificial intelligence, to substantially decrease planning time while simultaneously retaining the clinical intuition of plans created by radiation oncologists. This study seeks to compare the ML algorithm against expert-planned PPB plans to evaluate the equivalency of dosimetric and clinical plan quality. Methods: Plan features were computed from historical high-quality PPB treatments (N = 100) and stored in a relational database (RDB). The ML algorithm matched new PPB features to a highly similar case in the RDB; this initial plan configuration was then further optimized using a stochastic search algorithm. PPB pre-plans (N = 30) generated using the ML algorithm were compared to plan variants created by an expert dosimetrist (RT), and radiation oncologist (MD). Planning time and pre-plan dosimetry were evaluated using a one-way Student’s t-test and ANOVA, respectively (significance level = 0.05). Clinical implant quality was evaluated by expert PPB radiation oncologists as part of a qualitative study. Results: Average planning time was 0.44 ± 0.42 min compared to 17.88 ± 8.76 min for the ML algorithm and RT, respectively, a significant advantage [t(9), p = 0.01]. A post-hoc ANOVA [F(2,87) = 6.59, p = 0.002] using Tukey-Kramer criteria showed a significantly lower mean prostate V150% for the ML plans (52.9%) compared to the RT (57.3%), and MD (56.2%) plans. Preliminary qualitative study results indicate comparable clinical implant quality between RT and ML plans with a trend towards preference for ML plans. Conclusion: PPB pre-treatment plans highly comparable to those of an expert radiation oncologist can be created using a novel ML planning model. The use of an ML-based planning approach is expected to translate into improved PPB accessibility and plan uniformity.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-G-TeP1-02: Analytical Stopping Power and Range Parameterization for Therapeutic Energy Intervals

Purpose: To develop a simple, analytic parameterization of stopping power and range, which covers a wide energy interval and is applicable to many species of projectile ions and target materials, with less than 15% disagreement in linear stopping power and 1 mm in range. Methods: The new parameterization was required to be analytically integrable from stopping power to range, and continuous across the range interval of 1 µm to 50 cm. The model parameters were determined from stopping power and range data for hydrogen, carbon, iron, and uranium ions incident on water, carbon, aluminum, lead and copper. Stopping power and range data was taken from SRIM. A stochastic minimization algorithm was used to find model parameters, with 10 data points per energy decade. Additionally, fitting was performed with 2 and 26 data points per energy decade to test the model’s robustness to sparse Results: 6 free parameters were sufficient to cover the therapeutic energy range for each projectile ion species (e.g. 1 keV – 300 MeV for protons). The model agrees with stopping power and range data well, with less than 9% relative stopping power difference and 0.5 mm difference in range. As few as, 4 bins per decade were required to achieve comparable fitting results to the full data set. Conclusion: This study successfully demonstrated that a simple analytic function can be used to fit the entire energy interval of therapeutic ion beams of hydrogen and heavier elements. Advantages of this model were the small number (6) of free parameters, and that the model calculates both stopping power and range. Applications of this model include GPU-based dose calculation algorithms and Monte Carlo simulations, where available memory is limited. This work was supported in part by a research agreement between United States Naval Academy and Louisiana State University: Contract No N00189-13-P-0786. In addition, this work was accepted for presentation at the American Nuclear Society Annual Meeting 2016.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

TU-H-CAMPUS-JeP3-01: Towards Robust Adaptive Radiation Therapy Strategies

Purpose: To set up a framework combining robust treatment planning with adaptive reoptimization in order to maintain high treatment quality, to respond to interfractional variations and to identify those patients who will benefit the most from an adaptive fractionation schedule. Methods: We propose adaptive strategies based on stochastic minimax optimization for a series of simulated treatments on a one-dimensional patient phantom. The plan should be able to handle anticipated systematic and random errors and is applied during the first fractions. Information on the individual geometric variations is gathered at each fraction. At scheduled fractions, the impact of the measured errors on the delivered dose distribution is evaluated. For a patient that receives a dose that does not satisfy specified plan quality criteria, the plan is reoptimized based on these individual measurements using one of three different adaptive strategies. The reoptimized plan is then applied during future fractions until a new scheduled adaptation becomes necessary. In the first adaptive strategy the measured systematic and random error scenarios and their assigned probabilities are updated to guide the robust reoptimization. The focus of the second strategy lies on variation of the fraction of the worst scenarios taken into account during robust reoptimization. In the third strategy the uncertainty margins around the target are recalculated with the measured errors. Results: By studying the effect of the three adaptive strategies combined with various adaptation schedules on the same patient population, the group which benefits from adaptation is identified together with the most suitable strategy and schedule. Preliminary computational results indicate when and how best to adapt for the three different strategies. Conclusion: A workflow is presented that provides robust adaptation of the treatment plan throughout the course of treatment and useful measures to identify patients in need for an adaptive treatment strategy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-AB-209-09: Optimization of Rotational Arc Station Parameter Optimized Radiation Therapy

Purpose: To develop a fast optimization method for station parameter optimized radiation therapy (SPORT) and show that SPORT is capable of improving VMAT in both plan quality and delivery efficiency. Methods: The angular space from 0° to 360° was divided into 180 station points (SPs). A candidate aperture was assigned to each of the SPs based on the calculation results using a column generation algorithm. The weights of the apertures were then obtained by optimizing the objective function using a state-of-the-art GPU based Proximal Operator Graph Solver (POGS) within seconds. Apertures with zero or low weight were thrown out. To avoid being trapped in a local minimum, a stochastic gradient descent method was employed which also greatly increased the convergence rate of the objective function. The above procedure repeated until the plan could not be improved any further. A weighting factor associated with the total plan MU also indirectly controlled the complexities of aperture shapes. The number of apertures for VMAT and SPORT was confined to 180. The SPORT allowed the coexistence of multiple apertures in a single SP. The optimization technique was assessed by using three clinical cases (prostate, H&N and brain). Results: Marked dosimetric quality improvement was demonstrated in the SPORT plans for all three studied cases. Prostate case: the volume of the 50% prescription dose was decreased by 22% for the rectum. H&N case: SPORT improved the mean dose for the left and right parotids by 15% each. Brain case: the doses to the eyes, chiasm and inner ears were all improved. SPORT shortened the treatment time by ∼1 min for the prostate case, ∼0.5 min for brain case, and ∼0.2 min for the H&N case. Conclusion: The superior dosimetric quality and delivery efficiency presented here indicates that SPORT is an intriguing alternative treatment modality.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

WE-H-207A-09: Theoretical Limits to Molecular Biomarker Detection Using Magnetic Nanoparticles

Purpose: Estimate the limits of molecular biomarker detection using magnetic nanoparticle methods like in vivo ELISA. Methods: Magnetic nanoparticles in an alternating magnetic field produce a magnetization that can be detected at exceedingly low levels because the signal at the harmonic frequencies is uniquely produced by the nanoparticles. Because the magnetization can also be used to characterize the nanoparticle rotational freedom, the bound state can be found. If the nanoparticles are coated with molecules that bind the desired biomarker, the rotational freedom reflects the biomarker concentration. The irreducible noise limit is the thermal noise or Johnson noise of the tissue and the contrast that can be measured must be larger than that limit. The contrast produced is a function of the applied field and depends strongly on nanoparticle volume. We have estimated the contrast using a Langevin function of a single composite variable to approximate the full stochastic Langevin equation for nanoparticle dynamics. Results: The thermal noise for a bandwidth reasonable for spectroscopy suggests mid zeptomolar (10–21) to low attomolar (10–18) concentrations can be measured in a volume that is 10cm in scale. The suggested sensitivity is far below the physiologically concentrations of almost all critical biomarkers including cytokines (picomolar), hormones (nanomolar) and heat shock proteins. Conclusion: The sensitivity of in vivo ELISA concentration measurements should be sufficient to measure physiological concentrations of critical biomarkers like cytokines in vivo. Further the sensitivity should be sufficient to measure concentrations of other biomarkers that are six to eight orders of magnitude lower in concentration than immune signaling molecules like cytokines. NIH - 1U54CA151662-01 Department of Radiology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TH-CD-202-07: A Methodology for Generating Numerical Phantoms for Radiation Therapy Using Geometric Attribute Distribution Models

Purpose: A persistent challenge for the quality assessment of radiation therapy treatments (e.g. contouring accuracy) is the absence of the known, ground truth for patient data. Moreover, assessment results are often patient-dependent. Computer simulation studies utilizing numerical phantoms can be performed for quality assessment with a known ground truth. However, previously reported numerical phantoms do not include the statistical properties of inter-patient variations, as their models are based on only one patient. In addition, these models do not incorporate tumor data. In this study, a methodology was developed for generating numerical phantoms which encapsulate the statistical variations of patients within radiation therapy, including tumors. Methods: Based on previous work in contouring assessment, geometric attribute distribution (GAD) models were employed to model both the deterministic and stochastic properties of individual organs via principle component analysis. Using pre-existing radiation therapy contour data, the GAD models are trained to model the shape and centroid distributions of each organ. Then, organs with different shapes and positions can be generated by assigning statistically sound weights to the GAD model parameters. Organ contour data from 20 retrospective prostate patient cases were manually extracted and utilized to train the GAD models. As a demonstration, computer-simulated CT images of generated numerical phantoms were calculated and assessed subjectively and objectively for realism. Results: A cohort of numerical phantoms of the male human pelvis was generated. CT images were deemed realistic both subjectively and objectively in terms of image noise power spectrum. Conclusion: A methodology has been developed to generate realistic numerical anthropomorphic phantoms using pre-existing radiation therapy data. The GAD models guarantee that generated organs span the statistical distribution of observed radiation therapy patients, according to the training dataset. The methodology enables radiation therapy treatment assessment with multi-modality imaging and a known ground truth, and without patient-dependent bias.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TH-CD-207B-06: Swank Factor of Segmented Scintillators in Multi-Slice CT Detectors: Pulse Height Spectra and Light Escape

Purpose: Pulse height spectra (PHS) have been used to determine the Swank factor of a scintillator by measuring fluctuations in its light output per x-ray interaction. The Swank factor and x-ray quantum efficiency of a scintillator define the upper limit to its imaging performance, i.e. DQE(0). The Swank factor below the K-edge is dominated by optical properties, i.e. variations in light escape efficiency from different depths of interaction, denoted e(z). These variations can be optimized to improve tradeoffs in x-ray absorption, light yield, and spatial resolution. This work develops a quantitative model for interpreting measured PHS, and estimating e(z) on an absolute scale. The method is used to investigate segmented ceramic GOS scintillators used in multi-slice CT detectors. Methods: PHS of a ceramic GOS plate (1 mm thickness) and segmented GOS array (1.4 mm thick) were measured at 46 keV. Signal and noise propagation through x-ray conversion gain, light escape, detection by a photomultiplier tube and dynode amplification were modeled using a cascade of stochastic gain stages. PHS were calculated with these expressions and compared to measurements. Light escape parameters were varied until modeled PHS agreed with measurements. The resulting estimates of e(z) were used to calculate PHS without measurement noise to determine the inherent Swank factor. Results: The variation in e(z) was 67.2–89.7% in the plate and 40.2–70.8% in the segmented sample, corresponding to conversion gains of 28.6–38.1 keV{sup −1} and 17.1–30.1 keV{sup −1}, respectively. The inherent Swank factors of the plate and segmented sample were 0.99 and 0.95, respectively. Conclusion: The high light escape efficiency in the ceramic GOS samples yields high Swank factors and DQE(0) in CT applications. The PHS model allows the intrinsic optical properties of scintillators to be deduced from PHS measurements, thus it provides new insights for evaluating the imaging performance of segmented ceramic GOS scintillators.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Optimization of rotational arc station parameter optimized radiation therapy

Purpose: To develop a fast optimization method for station parameter optimized radiation therapy (SPORT) and show that SPORT is capable of matching VMAT in both plan quality and delivery efficiency by using three clinical cases of different disease sites. Methods: The angular space from 0° to 360° was divided into 180 station points (SPs). A candidate aperture was assigned to each of the SPs based on the calculation results using a column generation algorithm. The weights of the apertures were then obtained by optimizing the objective function using a state-of-the-art GPU based proximal operator graph solver. To avoid being trapped in a local minimum in beamlet-based aperture selection using the gradient descent algorithm, a stochastic gradient descent was employed here. Apertures with zero or low weight were thrown out. To find out whether there was room to further improve the plan by adding more apertures or SPs, the authors repeated the above procedure with consideration of the existing dose distribution from the last iteration. At the end of the second iteration, the weights of all the apertures were reoptimized, including those of the first iteration. The above procedure was repeated until the plan could not be improved any further. The optimization technique was assessed by using three clinical cases (prostate, head and neck, and brain) with the results compared to that obtained using conventional VMAT in terms of dosimetric properties, treatment time, and total MU. Results: Marked dosimetric quality improvement was demonstrated in the SPORT plans for all three studied cases. For the prostate case, the volume of the 50% prescription dose was decreased by 22% for the rectum and 6% for the bladder. For the head and neck case, SPORT improved the mean dose for the left and right parotids by 15% each. The maximum dose was lowered from 72.7 to 71.7 Gy for the mandible, and from 30.7 to 27.3 Gy for the spinal cord. The mean dose for the pharynx and larynx was reduced by 8% and 6%, respectively. For the brain case, the doses to the eyes, chiasm, and inner ears were all improved. SPORT shortened the treatment time by ∼1 min for the prostate case, ∼0.5 min for brain case, and ∼0.2 min for the head and neck case. Conclusions: The dosimetric quality and delivery efficiency presented here indicate that SPORT is an intriguing alternative treatment modality. With the widespread adoption of digital linac, SPORT should lead to improved patient care in the future.

60 APPLIED LIFE SCIENCES↗