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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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At least 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

MO-F-CAMPUS-T-05: Correct Or Not to Correct for Rotational Patient Set-Up Errors in Stereotactic Radiosurgery

Purpose: To quantify the effect of patient rotation in stereotactic radiation therapy and establish a threshold where rotational patient set-up errors have a significant impact on target coverage. Methods: To simulate rotational patient set-up errors, a Matlab code was created to rotate the patient dose distribution around the treatment isocentre, located centrally in the lesion, while keeping the structure contours in the original locations on the CT and MRI. Rotations of 1°, 3°, and 5° for each of the pitch, roll, and yaw, as well as simultaneous rotations of 1°, 3°, and 5° around all three axes were applied to two types of brain lesions: brain metastasis and acoustic neuroma. In order to analyze multiple tumour shapes, these plans included small spherical (metastasis), elliptical (acoustic neuroma), and large irregular (metastasis) tumour structures. Dose-volume histograms and planning target volumes were compared between the planned patient positions and those with simulated rotational set-up errors. The RTOG conformity index for patient rotation was also investigated. Results: Examining the tumour volumes that received 80% of the prescription dose in the planned and rotated patient positions showed decreases in prescription dose coverage of up to 2.3%. Conformity indices for treatments with simulated rotational errors showed decreases of up to 3% compared to the original plan. For irregular lesions, degradation of 1% of the target coverage can be seen for rotations as low as 3°. Conclusions: This data shows that for elliptical or spherical targets, rotational patient set-up errors less than 3° around any or all axes do not have a significant impact on the dose delivered to the target volume or the conformity index of the plan. However the same rotational errors would have an impact on plans for irregular tumours.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

MO-F-CAMPUS-J-02: Automatic Recognition of Patient Treatment Site in Portal Images Using Machine Learning

Purpose: To investigate the method to automatically recognize the treatment site in the X-Ray portal images. It could be useful to detect potential treatment errors, and to provide guidance to sequential tasks, e.g. automatically verify the patient daily setup. Methods: The portal images were exported from MOSAIQ as DICOM files, and were 1) processed with a threshold based intensity transformation algorithm to enhance contrast, and 2) where then down-sampled (from 1024×768 to 128×96) by using bi-cubic interpolation algorithm. An appearance-based vector space model (VSM) was used to rearrange the images into vectors. A principal component analysis (PCA) method was used to reduce the vector dimensions. A multi-class support vector machine (SVM), with radial basis function kernel, was used to build the treatment site recognition models. These models were then used to recognize the treatment sites in the portal image. Portal images of 120 patients were included in the study. The images were selected to cover six treatment sites: brain, head and neck, breast, lung, abdomen and pelvis. Each site had images of the twenty patients. Cross-validation experiments were performed to evaluate the performance. Results: MATLAB image processing Toolbox and scikit-learn (a machine learning library in python) were used to implement the proposed method. The average accuracies using the AP and RT images separately were 95% and 94% respectively. The average accuracy using AP and RT images together was 98%. Computation time was ∼0.16 seconds per patient with AP or RT image, ∼0.33 seconds per patient with both of AP and RT images. Conclusion: The proposed method of treatment site recognition is efficient and accurate. It is not sensitive to the differences of image intensity, size and positions of patients in the portal images. It could be useful for the patient safety assurance. The work was partially supported by a research grant from Varian Medical System.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

MO-F-CAMPUS-I-01: EIT Imaging to Monitor Human Salivary Gland Functionality: A Feasibility Study

Purpose: Clinically, there exists a need to develop a non-invasive technique for monitoring salivary activity. In this study, we investigate the feasibility of a using the electrical conductivity information from Electrical Impedance Tomography (EIT) to monitor salivary flow activity. Methods: To acquire EIT data, eight Ag/AgCl ECG electrodes were placed around the mandible of the subject. An EIT scan was obtained by injecting current at 50 KHz, 0.4 mA through each pair of electrodes and recording voltage across other electrode pairs. The functional conductivity image was obtained through reconstruction of the voltage data, using Electrical Impedance Tomography and Diffuse Optical Tomography Reconstruction Software (EIDORS) in Matlab. In using EIDORS, forward solution was obtained using a user-defined finite element model shape and inverse solution was obtained using one-step Gaussian solver. EIT scans of volunteer research team members were acquired for three different physiological states: pre-stimulation, stimulation and post-stimulation. For pre-stimulation phase, data were collected in intervals of 5 minutes for 15 minutes. The salivary glands were then stimulated in the subject using lemon and the data were collected immediately. Post-stimulation data were collected at 4 different timings after stimulation. Results: Variations were observed in the electrical conductivity patterns near parotid regions between the pre- and post-stimulation stages. The three images acquired during the 15 minute pre-stimulation phase showed no major changes in the conductivity. Immediately after stimulation, electrical conductivity increased near parotid regions and 15 minutes later slowly returned to pre-stimulation level. Conclusion: In the present study involving human subjects, the change in electrical conductivity pattern shown in the EIT images, acquired at different times with and without stimulation of salivary glands, appeared to be consistent with the change in salivary gland activity. The conductivity changes imaged through EIT are potentially useful for the purpose of salivary monitoring.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

MO-F-CAMPUS-J-01: Acoustic Range Verification of Proton Beams: Simulation of Heterogeneity and Clinical Proton Pulses

Purpose: Through simulation, to assess acoustic-based range verification of proton beams (protoacoustics) under clinical conditions. Methods: Pressure waves generated by the energy deposition of a 150 MeV, 8 mm FWHM pulsed pencil proton beam were numerically simulated through two Methods: 1) For a homogeneous water medium, an analytical wave-equation solution was used to calculate the time-dependent pressure measured at detector points surrounding the proton Bragg peak. 2) For heterogeneity studies, a CT tissue image was used to calculate the proton dose deposition and define the acoustic properties of the voxels through which numerical pressure wave propagation was simulated with the k-Wave matlab toolbox. The simulations were used to assess the dependence of the acoustic amplitude and range-verification accuracy on proton pulse rise time and tissue heterogeneity. Results: As the proton pulse rise time is increased from 1 to 40 µs, the amplitude of the expected acoustic emission decreases (a 60% drop distal to the Bragg peak), the central frequency of the expected signal decreases (from 45 to 6 kHz), and the accuracy of the range-verification decreases (from <1 mm to 16 mm at 5 cm distal to the Bragg peak). For a 300 nA pulse, the expected pressure range is on the order of 0.1 Pa, which is observable with commercial detectors. For the heterogeneous medium, our test case shows that pressure waves emitted by an anterior pencil beam directed into the abdomen and detected posteriorly can determine the Bragg peak range to an accuracy of <2mm for a 1 µs proton pulse. Conclusion: For proton pulses with fast rise-times, protoacoustics is a promising potential method for monitoring penetration depth through heterogeneous tissue. The loss of range-verification accuracy with increasing rise-times, however, suggests the need for comparisons to modeling to improve accuracy for slower cyclotron proton sources.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2015

MO-F-CAMPUS-I-01: A System for Automatically Calculating Organ and Effective Dose for Fluoroscopically-Guided Procedures

Purpose: A system was developed that automatically calculates the organ and effective dose for individual fluoroscopically-guided procedures using a log of the clinical exposure parameters. Methods: We have previously developed a dose tracking system (DTS) to provide a real-time color-coded 3D- mapping of skin dose. This software produces a log file of all geometry and exposure parameters for every x-ray pulse during a procedure. The data in the log files is input into PCXMC, a Monte Carlo program that calculates organ and effective dose for projections and exposure parameters set by the user. We developed a MATLAB program to read data from the log files produced by the DTS and to automatically generate the definition files in the format used by PCXMC. The processing is done at the end of a procedure after all exposures are completed. Since there are thousands of exposure pulses with various parameters for fluoroscopy, DA and DSA and at various projections, the data for exposures with similar parameters is grouped prior to entry into PCXMC to reduce the number of Monte Carlo calculations that need to be performed. Results: The software developed automatically transfers data from the DTS log file to PCXMC and runs the program for each grouping of exposure pulses. When the dose from all exposure events are calculated, the doses for each organ and all effective doses are summed to obtain procedure totals. For a complicated interventional procedure, the calculations can be completed on a PC without manual intervention in less than 30 minutes depending on the level of data grouping. Conclusion: This system allows organ dose to be calculated for individual procedures for every patient without tedious calculations or data entry so that estimates of stochastic risk can be obtained in addition to the deterministic risk estimate provided by the DTS. Partial support from NIH grant R01EB002873 and Toshiba Medical Systems Corp.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

TU-AB-303-02: A Novel Surrogate to Identify Anatomical Changes During Radiotherapy of Head and Neck Cancer Patients

Purpose: To develop a novel method to monitor external anatomical changes in head and neck cancer patients in order to help guide adaptive radiotherapy decisions. Methods: The method, developed in MATLAB, reveals internal anatomical changes based on variations observed in external anatomy. Weekly kV-CBCT scans from 11 Head and neck patients were retrospectively analyzed. The pre-processing step first corrects each CBCT for artifacts and removes pixels from the immobilization mask to produce an accurate external contour of the patient’s skin. After registering the CBCTs to the initial planning CT, the external contours from each CBCT (CBCTn) are transferred to the first week — reference — CBCT{sub 1}. Contour radii, defined as the distances between an external contour and the central pixel of each CBCT slice, are calculated for each scan at angular increments of 1 degree. The changes in external anatomy are then quantified by the difference in radial distance between the external contours of CBCT1 and CBCTn. The radial difference is finally displayed on a 2D intensity map (angle vs radial distance difference) in order to highlight regions of interests with significant changes. Results: The 2D radial difference maps provided qualitative and quantitative information, such as the location and the magnitude of external contour divergences and the rate at which these deviations occur. With this method, anatomical changes due to tumor volume shrinkage and patient weight loss were clearly identified and could be correlated with the under-dosage of targets or over-dosage of OARs. Conclusion: This novel method provides an efficient tool to visualize 3D external anatomical modification on a single 2D map. It quickly pinpoints the location of differences in anatomy during the course of radiotherapy, which can help determine if a treatment plan needs to be adapted.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

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

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

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

WE-D-BRA-01: FEATURED PRESENTATION and BEST IN PHYSICS (THERAPY): Predicting Potentially Problematic VMAT Treatment Plans Before Patient Specific QA Measurements

Purpose: Several promising IMRT QA tools have been developed in recent years to combat problems found in the lack of sensitivity in planar dose measurements analyzed using consensus gamma analysis criteria. The increased complexity and added information with such devices adds not only increased time, but new challenges in determining endpoints for pass/fail criteria. Using a large cohort of previously measured planar IMRT QA data, it may be possible to correlate potentially problematic plans with calculated plan metrics that can be done a priori, such that these tools can be used only in clinically relevant situations. Methods: 90 previously measured, clinically delivered VMAT plans were exported in DICOM RT format. Using a Matlab program, plan metrics were computed based on a previously developed set of equations (Du et al. 2014). These metrics included MU-weighted beam irregularity, which quantifies an MLC shape’s deviation from that of a circle. Machine delivery parameters such as MU delivered per degree and leaf movement in millimeters per MU were also calculated. Based on a previous analysis of 394 IMRT QA measurements, a “failing” plan was defined as one with less than 85% gamma pass rate when computed at 1% dose difference and 2 mm distance to agreement; 16 of the 90 plans were identified as failing Results: A ROC curve was generated with an AUC of 0.9409. All 16 outliers were detected with a specificity of 85.1% when using a threshold value based on a linear combination of the MU-weighted plan irregularity and the leaf speed in mm/degree (average MU delivered per degree multiplied by average leaf movement in mm per MU). Conclusion: This data supports that potentially problematic VMAT plans can possibly be predicted before measurement by assessing the MU-weighted irregularity of the MLC shapes combined with the averaged leaf speed of the MLC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

WE-G-BRD-07: Automated MR Image Standardization and Auto-Contouring Strategy for MRI-Based Adaptive Brachytherapy for Cervix Cancer

Purpose: MRI-based adaptive brachytherapy (ABT) is an emerging treatment modality for patients with gynecological tumors. However, MR image intensity non-uniformities (IINU) can vary from fraction to fraction, complicating image interpretation and auto-contouring accuracy. We demonstrate here an automated MR image standardization and auto-contouring strategy for MRI-based ABT of cervix cancer. Methods: MR image standardization consisted of: 1) IINU correction using the MNI N3 algorithm, 2) noise filtering using anisotropic diffusion, and 3) signal intensity normalization using the volumetric median. This post-processing chain was implemented as a series of custom Matlab and Java extensions in MIM (v6.4.5, MIM Software) and was applied to 3D T2 SPACE images of six patients undergoing MRI-based ABT at 3T. Coefficients of variation (CV=σ/µ) were calculated for both original and standardized images and compared using Mann-Whitney tests. Patient-specific cumulative MR atlases of bladder, rectum, and sigmoid contours were constructed throughout ABT, using original and standardized MR images from all previous ABT fractions. Auto-contouring was performed in MIM two ways: 1) best-match of one atlas image to the daily MR image, 2) multi-match of all previous fraction atlas images to the daily MR image. Dice’s Similarity Coefficients (DSCs) were calculated for auto-generated contours relative to reference contours for both original and standardized MR images and compared using Mann-Whitney tests. Results: Significant improvements in CV were detected following MR image standardization (p=0.0043), demonstrating an improvement in MR image uniformity. DSCs consistently increased for auto-contoured bladder, rectum, and sigmoid following MR image standardization, with the highest DSCs detected when the combination of MR image standardization and multi-match cumulative atlas-based auto-contouring was utilized. Conclusion: MR image standardization significantly improves MR image uniformity. The combination of MR image standardization and multi-match cumulative atlas-based auto-contouring produced the highest DSCs and is a promising strategy for MRI-based ABT for cervix cancer.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Frame average optimization of cine-mode EPID images used for routine clinical in vivo patient dose verification of VMAT deliveries

Purpose: The in vivo 3D dose delivered to a patient during volumetric modulated arc therapy (VMAT) delivery can be calculated using electronic portal imaging device (EPID) images. These images must be acquired in cine-mode (i.e., “movie” mode) in order to capture the time-dependent delivery information. The angle subtended by each cine-mode EPID image during an arc can be changed via the frame averaging number selected within the image acquisition software. A large frame average number will decrease the EPID’s angular resolution and will result in a decrease in the accuracy of the dose information contained within each image. Alternatively, less EPID images acquired per delivery will decrease the overall 3D patient dose calculation time, which is appealing for large-scale clinical implementation. Therefore, the purpose of this study was to determine the optimal frame average value per EPID image, defined as the highest frame averaging that can be used without an appreciable loss in 3D dose reconstruction accuracy for VMAT treatments. Methods: Six different VMAT plans and six different SBRT-VMAT plans were delivered to an anthropomorphic phantom. Delivery was carried out on a Varian 2300ix model linear accelerator (Linac) equipped with an aS1000 EPID running at a frame acquisition rate of 7.5 Hz. An additional PC was set up at the Linac console area, equipped with specialized frame-grabber hardware and software packages allowing continuous acquisition of all EPID frames during delivery. Frames were averaged into “frame-averaged” EPID images using MATLAB. Each frame-averaged data set was used to calculate the in vivo dose to the patient and then compared to the single EPID frame in vivo dose calculation (the single frame calculation represents the highest possible angular resolution per EPID image). A mean percentage dose difference of low dose (<20% prescription dose) and high dose regions (>80% prescription dose) was calculated for each frame averaged scenario for each plan. The authors defined their unacceptable loss of accuracy as no more than a ±1% mean dose difference in the high dose region. Optimal frame average numbers were then determined as a function of the Linac’s average gantry speed and the dose per fraction. Results: The authors found that 9 and 11 frame averages were suitable for all VMAT and SBRT-VMAT treatments, respectively. This resulted in no more than a 1% loss to any of the dose region’s mean percentage difference when compared to the single frame reconstruction. The optimized number was dependent on the treatment’s dose per fraction and was determined to be as high as 14 for 12 Gy/fraction (fx), 15 for 8 Gy/fx, 11 for 6 Gy/fx, and 9 for 2 Gy/fx. Conclusions: The authors have determined an optimal EPID frame averaging number for multiple VMAT-type treatments. These are given as a function of the dose per fraction and average gantry speed. These optimized values are now used in the authors’ clinical, 3D, in vivo patient dosimetry program. This provides a reduction in calculation time while maintaining the authors’ required level of accuracy in the dose reconstruction.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

An online x-ray based position validation system for prostate hypofractionated radiotherapy

Purpose: Accurate positioning of the target volume during treatment is paramount for stereotactic body radiation therapy (SBRT). In this work, the authors present the development of an in-house software tool to verify target position with an Elekta-Synergy linear accelerator using kV planar images acquired during treatment delivery. Methods: In-house software, SeedTracker, was developed in MATLAB to perform the following three functions: 1. predict intended seed positions in a planar view perpendicular to any gantry angle, simulating a portal imaging device, from the 3D seed co-ordinates derived from the treatment planning system; 2. autosegment seed positions in kV planar images; and 3. report the position shift based on the seed positions in the projection images. The performance of SeedTracker was verified using a CIRS humanoid phantom (CIRS, VA, USA) implanted with three Civco gold seed markers (Civco, IA, USA) in the prostate. The true positive rate of autosegmentation (TPR{sub seg}) and the accuracy of the software in alerting the user when the isocenter position was outside the tolerance (TPR{sub trig}) were studied. Two-dimensional and 3D static position offsets introduced to the humanoid phantom and 3D dynamic offsets introduced to a gel phantom containing gold seeds were used for evaluation of the system. Results: SeedTracker showed a TPR{sub seg} of 100% in the humanoid phantom for projection images acquired at all angles except in the ranges of 80°–100° and 260°–280° where seeds are obscured by anatomy. This resulted in a TPR{sub trig} of 88% over the entire treatment range for considered 3D static offsets introduced to the phantom. For 2D static offsets where the position offsets were only introduced in the anterior–posterior and lateral directions, the TPR{sub trig} of SeedTracker was limited by both seed detectability and positional offset. SeedTracker showed a false positive trigger in the projection angle range between 130°–170° and 310°–350° (a maximum of 24% of treatment time) due to limited information that can be derived from monoscopic images. The system accurately determined the dynamic trajectory of the isocenter position in the superior and inferior direction for the studied dynamic offset scenarios based on the seed position in monoscopic images. Conclusions: The developed software has been shown to accurately autosegment the seed positions in kV planar images except for two 20° arcs where seeds are obscured by anatomical structures. The isocenter trajectories determined by the system, based on the monoscopic images, provide useful information for monitoring the prostate position. The developed system has potential application for monitoring prostate position during treatment delivery in linear accelerator based SBRT.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2016

Dynamic PET simulator via tomographic emission projection for kinetic modeling and parametric image studies

Purpose: To develop and evaluate a fast and simple tool called dPETSTEP (Dynamic PET Simulator of Tracers via Emission Projection), for dynamic PET simulations as an alternative to Monte Carlo (MC), useful for educational purposes and evaluation of the effects of the clinical environment, postprocessing choices, etc., on dynamic and parametric images. Methods: The tool was developed in MATLAB using both new and previously reported modules of PETSTEP (PET Simulator of Tracers via Emission Projection). Time activity curves are generated for each voxel of the input parametric image, whereby effects of imaging system blurring, counting noise, scatters, randoms, and attenuation are simulated for each frame. Each frame is then reconstructed into images according to the user specified method, settings, and corrections. Reconstructed images were compared to MC data, and simple Gaussian noised time activity curves (GAUSS). Results: dPETSTEP was 8000 times faster than MC. Dynamic images from dPETSTEP had a root mean square error that was within 4% on average of that of MC images, whereas the GAUSS images were within 11%. The average bias in dPETSTEP and MC images was the same, while GAUSS differed by 3% points. Noise profiles in dPETSTEP images conformed well to MC images, confirmed visually by scatter plot histograms, and statistically by tumor region of interest histogram comparisons that showed no significant differences (p < 0.01). Compared to GAUSS, dPETSTEP images and noise properties agreed better with MC. Conclusions: The authors have developed a fast and easy one-stop solution for simulations of dynamic PET and parametric images, and demonstrated that it generates both images and subsequent parametric images with very similar noise properties to those of MC images, in a fraction of the time. They believe dPETSTEP to be very useful for generating fast, simple, and realistic results, however since it uses simple scatter and random models it may not be suitable for studies investigating these phenomena. dPETSTEP can be downloaded free of cost from https://github.com/CRossSchmidtlein/dPETSTEP.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SIS epidemiological model for adaptive RT: Forecasting the parotid glands shrinkage during tomotherapy treatment

Purpose: A susceptible-infected-susceptible (SIS) epidemic model was applied to radiation therapy (RT) treatments to predict morphological variations in head and neck (H&N) anatomy. Methods: 360 daily MVCT images of 12 H&N patients treated by tomotherapy were analyzed in this retrospective study. Deformable image registration (DIR) algorithms, mesh grids, and structure recontouring, implemented in the RayStation treatment planning system (TPS), were applied to assess the daily organ warping. The parotid’s warping was evaluated using the epidemiological approach considering each vertex as a single subject and its deformed vector field (DVF) as an infection. Dedicated IronPython scripts were developed to export daily coordinates and displacements of the region of interest (ROI) from the TPS. MATLAB tools were implemented to simulate the SIS modeling. Finally, the fully trained model was applied to a new patient. Results: A QUASAR phantom was used to validate the model. The patients’ validation was obtained setting 0.4 cm of vertex displacement as threshold and splitting susceptible (S) and infectious (I) cases. The correlation between the epidemiological model and the parotids’ trend for further optimization of alpha and beta was carried out by Euclidean and dynamic time warping (DTW) distances. The best fit with experimental conditions across all patients (Euclidean distance of 4.09 ± 1.12 and DTW distance of 2.39 ± 0.66) was obtained setting the contact rate at 7.55 ± 0.69 and the recovery rate at 2.45 ± 0.26; birth rate was disregarded in this constant population. Conclusions: Combining an epidemiological model with adaptive RT (ART), the authors’ novel approach could support image-guided radiation therapy (IGRT) to validate daily setup and to forecast anatomical variations. The SIS-ART model developed could support clinical decisions in order to optimize timing of replanning achieving personalized treatments.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Technical Note: SPEKTR 3.0—A computational tool for x-ray spectrum modeling and analysis

Purpose: A computational toolkit (SPEKTR 3.0) has been developed to calculate x-ray spectra based on the tungsten anode spectral model using interpolating cubic splines (TASMICS) algorithm, updating previous work based on the tungsten anode spectral model using interpolating polynomials (TASMIP) spectral model. The toolkit includes a MATLAB (The Mathworks, Natick, MA) function library and improved user interface (UI) along with an optimization algorithm to match calculated beam quality with measurements. Methods: The SPEKTR code generates x-ray spectra (photons/mm{sup 2}/mAs at 100 cm from the source) using TASMICS as default (with TASMIP as an option) in 1 keV energy bins over beam energies 20–150 kV, extensible to 640 kV using the TASMICS spectra. An optimization tool was implemented to compute the added filtration (Al and W) that provides a best match between calculated and measured x-ray tube output (mGy/mAs or mR/mAs) for individual x-ray tubes that may differ from that assumed in TASMICS or TASMIP and to account for factors such as anode angle. Results: The median percent difference in photon counts for a TASMICS and TASMIP spectrum was 4.15% for tube potentials in the range 30–140 kV with the largest percentage difference arising in the low and high energy bins due to measurement errors in the empirically based TASMIP model and inaccurate polynomial fitting. The optimization tool reported a close agreement between measured and calculated spectra with a Pearson coefficient of 0.98. Conclusions: The computational toolkit, SPEKTR, has been updated to version 3.0, validated against measurements and existing models, and made available as open source code. Video tutorials for the SPEKTR function library, UI, and optimization tool are available.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-C-207B-03: A Geometrical Constrained Chan-Vese Based Tumor Segmentation Scheme for PET

Purpose: Accurate segmentation of tumor in PET is challenging when part of tumor is connected with normal organs/tissues with no difference in intensity. Conventional segmentation methods, such as thresholding or region growing, cannot generate satisfactory results in this case. We proposed a geometrical constrained Chan-Vese based scheme to segment tumor in PET for this special case by considering the similarity between two adjacent slices. Methods: The proposed scheme performs segmentation in a slice-by-slice fashion where an accurate segmentation of one slice is used as the guidance for segmentation of rest slices. For a slice that the tumor is not directly connected to organs/tissues with similar intensity values, a conventional clustering-based segmentation method under user’s guidance is used to obtain an exact tumor contour. This is set as the initial contour and the Chan-Vese algorithm is applied for segmenting the tumor in the next adjacent slice by adding constraints of tumor size, position and shape information. This procedure is repeated until the last slice of PET containing tumor. The proposed geometrical constrained Chan-Vese based algorithm was implemented in Matlab and its performance was tested on several cervical cancer patients where cervix and bladder are connected with similar activity values. The positive predictive values (PPV) are calculated to characterize the segmentation accuracy of the proposed scheme. Results: Tumors were accurately segmented by the proposed method even when they are connected with bladder in the image with no difference in intensity. The average PPVs were 0.9571±0.0355 and 0.9894±0.0271 for 17 slices and 11 slices of PET from two patients, respectively. Conclusion: We have developed a new scheme to segment tumor in PET images for the special case that the tumor is quite similar to or connected to normal organs/tissues in the image. The proposed scheme can provide a reliable way for segmenting tumors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-C-207B-05: Tissue Segmentation of Computed Tomography Images Using a Random Forest Algorithm: A Feasibility Study

Purpose: Develop an automated Random Forest algorithm for tissue segmentation of CT examinations. Methods: Seven materials were classified for segmentation: background, lung/internal gas, fat, muscle, solid organ parenchyma, blood/contrast, and bone using Matlab and the Trainable Weka Segmentation (TWS) plugin of FIJI. The following classifier feature filters of TWS were investigated: minimum, maximum, mean, and variance each evaluated over a pixel radius of 2n, (n = 0–4). Also noise reduction and edge preserving filters, Gaussian, bilateral, Kuwahara, and anisotropic diffusion, were evaluated. The algorithm used 200 trees with 2 features per node. A training data set was established using an anonymized patient’s (male, 20 yr, 72 kg) chest-abdomen-pelvis CT examination. To establish segmentation ground truth, the training data were manually segmented using Eclipse planning software, and an intra-observer reproducibility test was conducted. Six additional patient data sets were segmented based on classifier data generated from the training data. Accuracy of segmentation was determined by calculating the Dice similarity coefficient (DSC) between manual and auto segmented images. Results: The optimized autosegmentation algorithm resulted in 16 features calculated using maximum, mean, variance, and Gaussian blur filters with kernel radii of 1, 2, and 4 pixels, in addition to the original CT number, and Kuwahara filter (linear kernel of 19 pixels). Ground truth had a DSC of 0.94 (range: 0.90–0.99) for adult and 0.92 (range: 0.85–0.99) for pediatric data sets across all seven segmentation classes. The automated algorithm produced segmentation with an average DSC of 0.85 ± 0.04 (range: 0.81–1.00) for the adult patients, and 0.86 ± 0.03 (range: 0.80–0.99) for the pediatric patients. Conclusion: The TWS Random Forest auto-segmentation algorithm was optimized for CT environment, and able to segment seven material classes over a range of body habitus and CT protocol parameters with an average DSC of 0.86 ± 0.04 (range: 0.80–0.99).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-D-201-01: A Multi-Institutional Study Quantifying the Impact of Simulated Linear Accelerator VMAT Errors for Nasopharynx

Purpose: To quantify the impact of differing magnitudes of simulated linear accelerator errors on the dose to the target volume and organs at risk for nasopharynx VMAT. Methods: Ten nasopharynx cancer patients were retrospectively replanned twice with one full arc VMAT by two institutions. Treatment uncertainties (gantry angle and collimator in degrees, MLC field size and MLC shifts in mm) were introduced into these plans at increments of 5,2,1,−1,−2 and −5. This was completed using an in-house Python script within Pinnacle3 and analysed using 3DVH and MatLab. The mean and maximum dose were calculated for the Planning Target Volume (PTV1), parotids, brainstem, and spinal cord and then compared to the original baseline plan. The D1cc was also calculated for the spinal cord and brainstem. Patient average results were compared across institutions. Results: Introduced gantry angle errors had the smallest effect of dose, no tolerances were exceeded for one institution, and the second institutions VMAT plans were only exceeded for gantry angle of ±5° affecting different sided parotids by 14–18%. PTV1, brainstem and spinal cord tolerances were exceeded for collimator angles of ±5 degrees, MLC shifts and MLC field sizes of ±1 and beyond, at the first institution. At the second institution, sensitivity to errors was marginally higher for some errors including the collimator error producing doses exceeding tolerances above ±2 degrees, and marginally lower with tolerances exceeded above MLC shifts of ±2. The largest differences occur with MLC field sizes, with both institutions reporting exceeded tolerances, for all introduced errors (±1 and beyond). Conclusion: The plan robustness for VMAT nasopharynx plans has been demonstrated. Gantry errors have the least impact on patient doses, however MLC field sizes exceed tolerances even with relatively low introduced errors and also produce the largest errors. This was consistent across both departments. The authors acknowledge funding support from the NSW Cancer Council.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-R-13: Decoding 18F-FDG Uptake Heterogeneity for Primary and Lymphoma Tumors by Using Texture Analysis in PET Images

Purpose: To explore 18F-FDG uptake heterogeneity of primary tumor and lymphoma tumor by texture features of PET image and quantify the heterogeneity difference between primary tumor and lymphoma tumor. Methods: 18 patients with primary tumor and lymphoma tumor in lung cancer were enrolled. All patients underwent whole-body 18F-FDG PET/CT scans before treatment. Texture features, based on Gray-level Co-occurrence Matrix, second and high order matrices are extracted from code using MATLAB software to quantify 18F-FDG uptake heterogeneity. The relationships of volume between energy, entropy, correlation, homogeneity and contrast were analyzed. Results: For different cases, tumor heterogeneity was not the same. Texture parameters (contrast, entropy, and correlation) of lymphoma were lower than primary tumor. On the contrast, the texture parameters (energy, homogeneity and inverse different moment) of lymphoma were higher than primary tumor. Significantly, correlations were observed between volume and energy (primary, r=−0.194, p=0.441; lymphoma, r=−0.339, p=0.582), homogeneity (primary, r=−0.146, p=0.382; lymphoma, r=−0.193, p=0.44), inverse difference moment (primary, r=−0.14, p=0.374; lymphoma, r=−0.172, p=0.414) and a positive correlation between volume and entropy (primary, r=0.233, p=0.483; lymphoma, r=0.462, p=0.680), contrast (primary, r=0.159, p=0.399; lymphoma, r=0.341, p=0.584), correlation (primary, r=0.027, p=0.165; lymphoma, r=0.046, p=0.215). For the same patient, energy for primary and lymphoma tumor is equal. The volume of lymphoma is smaller than primary tumor, but the homogeneity were higher than primary tumor. Conclusion: This study showed that there were effective heterogeneity differences between primary and lymphoma tumor by FDG-PET image texture analysis.

60 APPLIED LIFE SCIENCES↗