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DOE OSTI2015

MO-B-BRB-02: Maintain the Quality of Treatment Planning for Time-Constraint Cases

The radiotherapy treatment planning process has evolved over the years with innovations in treatment planning, treatment delivery and imaging systems. Treatment modality and simulation technologies are also rapidly improving and affecting the planning process. For example, Image-guided-radiation-therapy has been widely adopted for patient setup, leading to margin reduction and isocenter repositioning after simulation. Stereotactic Body radiation therapy (SBRT) and Radiosurgery (SRS) have gradually become the standard of care for many treatment sites, which demand a higher throughput for the treatment plans even if the number of treatments per day remains the same. Finally, simulation, planning and treatment are traditionally sequential events. However, with emerging adaptive radiotherapy, they are becoming more tightly intertwined, leading to iterative processes. Enhanced efficiency of planning is therefore becoming more critical and poses serious challenge to the treatment planning process; Lean Six Sigma approaches are being utilized increasingly to balance the competing needs for speed and quality. In this symposium we will discuss the treatment planning process and illustrate effective techniques for managing workflow. Topics will include: Planning techniques: (a) beam placement, (b) dose optimization, (c) plan evaluation (d) export to RVS. Planning workflow: (a) import images, (b) Image fusion, (c) contouring, (d) plan approval (e) plan check (f) chart check, (g) sequential and iterative process Influence of upstream and downstream operations: (a) simulation, (b) immobilization, (c) motion management, (d) QA, (e) IGRT, (f) Treatment delivery, (g) SBRT/SRS (h) adaptive planning Reduction of delay between planning steps with Lean systems due to (a) communication, (b) limited resource, (b) contour, (c) plan approval, (d) treatment. Optimizing planning processes: (a) contour validation (b) consistent planning protocol, (c) protocol/template sharing, (d) semi-automatic plan evaluation, (e) quality checklist for error prevention, (f) iterative process, (g) balance of speed and quality Learning Objectives: Gain familiarity with the workflow of modern treatment planning process. Understand the scope and challenges of managing modern treatment planning processes. Gain familiarity with Lean Six Sigma approaches and their implementation in the treatment planning workflow.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

MO-B-BRB-03: Systems Engineering Tools for Treatment Planning Process Optimization in Radiation Medicine

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

TU-AB-303-10: KVCBCT, MVCBCT and MVCT On-Board Imaging Suitability for An Urgent Radiotherapy Treatment Workflow

Purpose: A comparison of image quality and dose calculation accuracy to study the suitability of available on-board imaging systems for a new treatment workflow in emergency radiotherapy situations. Methods: Water and anthropomorphic phantom images were acquired on four different Linac on-board imaging systems, including kVCBCT (Varian TrueBeam and Elekta VersaHD), MVCBCT (Siemens Artiste) and MVCT (Accuray Tomotherapy). Simple treatments of single or opposed beams were planned on the respective kVCT images and copied to all CT images. Image suitability for dose planning was based on the overall mean dose differences and 3D gamma index with 3%/3mm criteria for a prescription of 100 monitor units (MU) and differences in calculated MUs per plan for dose prescriptions to mid-plane. Results: TrueBeam kVCBCT and Tomotherapy MVCT images produced most accurate dose calculation for all tested cases (γ-index >95%). MVCBCT and VersaHD kVCBCT images resulted in minimum γ-passing rates of 94% and 87%, respectively. MUs calculated from treatment plans prescribed to mid-plane were within differences of 5% relative to kVCT-based plans in all cases. However, VersaHD images showed considerable local image artifacts in the pelvis water and anthropomorphic neck phantom that complicated accurate Hounsfield unit (HU) to electron density conversion, thus causing local dose differences of more than 10% relative to kVCT-based dose distributions. Conclusion: TrueBeam kVCBCT, MVCBCT and MVCT systems provide image quality that allows for accurate simple treatment plan calculation. Prescription points should be placed away from areas found to cause local dose discrepancies, such as air cavities. Improved image filter settings and HU-to-electron density calibration adjustments may be required for the VersaHD system to obtain an overall accurate dose distribution. To evaluate a system’s overall suitability, its clinical features also require consideration, such as imaging field of view, acquisition time and the ability to instantly export images for treatment planning.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

TU-AB-303-11: Predict Parotids Deformation Applying SIS Epidemiological Model in H&N Adaptive RT

Purpose: The aim is to investigate the use of epidemiological models to predict morphological variations in patients undergoing radiation therapy (RT). The susceptible-infected-susceptible (SIS) deterministic model was applied to simulate warping within a focused region of interest (ROI). Hypothesis is to consider each voxel like a single subject of the whole sample and to treat displacement vector fields like an infection. Methods: Using Raystation hybrid deformation algorithms and automatic re-contouring based on mesh grid, we post-processed 360 MVCT images of 12 H&N patients treated with Tomotherapy. Study focused on parotid glands, identified by literature and previous analysis, as ROI more susceptible to warping in H&N region. Susceptible (S) and infectious (I) cases were identified in voxels with inter-fraction movement respectively under and over a set threshold. IronPython scripting allowed to export positions and displacement data of surface voxels for every fraction. A MATLAB homemade toolbox was developed to model the SIS. Results: SIS model was validated simulating organ motion on QUASAR phantom. Applying model in patients, within a [0–1cm] range, a single voxel movement of 0.4cm was selected as displacement threshold. SIS indexes were evaluated by MATLAB simulations. Dynamic time warping algorithm was used to assess matching between model and parotids behavior days of treatments. The best fit of the model was obtained with contact rate of 7.89±0.94 and recovery rate of 2.36±0.21. Conclusion: SIS model can follow daily structures evolutions, making possible to compare warping conditions and highlighting challenges due to abnormal variation and set-up errors. By epidemiology approach, organ motion could be assessed and predicted not in terms of average of the whole ROI, but in a voxel-by-voxel deterministic trend. Identifying anatomical region subjected to variations, would be possible to focus clinic controls within a cohort of pre-selected patients eligible for adaptive RT. The research is partially co-funded by the Italian Research Grant: Dose warping methods for IGRT and Adaptive RT: dose accumulation based on organ motion and anatomical variations of the patients during radiation therapy treatments,MoH (GR-2010-2318757) and Tecnologie Avanzate S.r.l.(Italy)

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

TU-F-CAMPUS-J-04: Evaluation of Metal Artifact Reduction Technique for the Radiation Therapy Planning

Purpose: To evaluate the feasibility of using a metal artifact reduction technique in depleting metal artifact and its application in improving dose calculation in External Radiation Therapy Planning. Methods: CIRS electron density phantom was scanned with and without steel drill bits placed in some plug holes. Meta artifact reduction software with Metal Deletion Technique (MDT) was used to remove metal artifacts for scanned image with metal. Hounsfield units of electron density plugs from artifact free reference image and MDT processed images were compared. To test the dose calculation improvement after the MDT processed images, clinically approved head and neck plan with manual dental artifact correction was tested. Patient images were exported and processed with MDT and plan was recalculated with new MDT image without manual correction. Dose profiles near the metal artifacts were compared. Results: The MDT used in this study effectively reduced the metal artifact caused by beam hardening and scatter. The windmill around the metal drill was greatly improved with smooth rounded view. Difference of the mean HU in each density plug between reference and MDT images were less than 10 HU in most of the plugs. Dose difference between original plan and MDT images were minimal. Conclusion: Most metal artifact reduction methods were developed for diagnostic improvement purpose. Hence Hounsfield unit accuracy was not rigorously tested before. In our test, MDT effectively eliminated metal artifacts with good HU reproduciblity. However, it can introduce new mild artifacts so the MDT images should be checked with original images.

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

Development of a video image-based QA system for the positional accuracy of dynamic tumor tracking irradiation in the Vero4DRT system

Purpose: To develop and evaluate a new video image-based QA system, including in-house software, that can display a tracking state visually and quantify the positional accuracy of dynamic tumor tracking irradiation in the Vero4DRT system. Methods: Sixteen trajectories in six patients with pulmonary cancer were obtained with the ExacTrac in the Vero4DRT system. Motion data in the cranio–caudal direction (Y direction) were used as the input for a programmable motion table (Quasar). A target phantom was placed on the motion table, which was placed on the 2D ionization chamber array (MatriXX). Then, the 4D modeling procedure was performed on the target phantom during a reproduction of the patient’s tumor motion. A substitute target with the patient’s tumor motion was irradiated with 6-MV x-rays under the surrogate infrared system. The 2D dose images obtained from the MatriXX (33 frames/s; 40 s) were exported to in-house video-image analyzing software. The absolute differences in the Y direction between the center of the exposed target and the center of the exposed field were calculated. Positional errors were observed. The authors’ QA results were compared to 4D modeling function errors and gimbal motion errors obtained from log analyses in the ExacTrac to verify the accuracy of their QA system. The patients’ tumor motions were evaluated in the wave forms, and the peak-to-peak distances were also measured to verify their reproducibility. Results: Thirteen of sixteen trajectories (81.3%) were successfully reproduced with Quasar. The peak-to-peak distances ranged from 2.7 to 29.0 mm. Three trajectories (18.7%) were not successfully reproduced due to the limited motions of the Quasar. Thus, 13 of 16 trajectories were summarized. The mean number of video images used for analysis was 1156. The positional errors (absolute mean difference + 2 standard deviation) ranged from 0.54 to 1.55 mm. The error values differed by less than 1 mm from 4D modeling function errors and gimbal motion errors in the ExacTrac log analyses (n = 13). Conclusions: The newly developed video image-based QA system, including in-house software, can analyze more than a thousand images (33 frames/s). Positional errors are approximately equivalent to those in ExacTrac log analyses. This system is useful for the visual illustration of the progress of the tracking state and for the quantification of positional accuracy during dynamic tumor tracking irradiation in the Vero4DRT system.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

Collision prediction software for radiotherapy treatments

Purpose: This work presents a method of collision predictions for external beam radiotherapy using surface imaging. The present methodology focuses on collision prediction during treatment simulation to evaluate the clearance of a patient’s treatment position and allow for its modification if necessary. Methods: A Kinect camera (Microsoft, Redmond, WA) is used to scan the patient and immobilization devices in the treatment position at the simulator. The surface is reconstructed using the SKANECT software (Occipital, Inc., San Francisco, CA). The treatment isocenter is marked using simulated orthogonal lasers projected on the surface scan. The point cloud of this surface is then shifted to isocenter and converted from Cartesian to cylindrical coordinates. A slab models the treatment couch. A cylinder with a radius equal to the normal distance from isocenter to the collimator plate, and a height defined by the collimator diameter is used to estimate collisions. Points within the cylinder clear through a full gantry rotation with the treatment couch at 0° , while points outside of it collide. The angles of collision are reported. This methodology was experimentally verified using a mannequin positioned in an alpha cradle with both arms up. A planning CT scan of the mannequin was performed, two isocenters were marked in PINNACLE, and this information was exported to AlignRT (VisionRT, London, UK)—a surface imaging system for patient positioning. This was used to ensure accurate positioning of the mannequin in the treatment room, when available. Collision calculations were performed for the two treatment isocenters and the results compared to the collisions detected the room. The accuracy of the Kinect-Skanect surface was evaluated by comparing it to the external surface of the planning CT scan. Results: Experimental verification results showed that the predicted angles of collision matched those recorded in the room within 0.5°, in most cases (largest deviation −1.2°). The accuracy study for the Kinect-Skanect surface showed an average discrepancy between the CT external contour and the surface scan of 2.2 mm. Conclusions: This methodology provides fast and reliable collision predictions using surface imaging. The use of the Kinect-Skanect system allows for a comprehensive modeling of the patient topography including all the relevant anatomy and immobilization devices that may lead to collisions. The use of this tool at the treatment simulation stage may allow therapists to evaluate the clearance of a patient’s treatment position and optimize it before the planning CT scan is performed. This can allow for safer treatments for the patients due to better collision predictions and improved clinical workflow by minimizing replanning and resimulations due to unforeseen clearance issues.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Minimizing treatment planning errors in proton therapy using failure mode and effects analysis

Purpose: Failure mode and effects analysis (FMEA) is a widely used tool to evaluate safety or reliability in conventional photon radiation therapy. However, reports about FMEA application in proton therapy are scarce. The purpose of this study is to apply FMEA in safety improvement of proton treatment planning at their center. Methods: The authors performed an FMEA analysis of their proton therapy treatment planning process using uniform scanning proton beams. The authors identified possible failure modes in various planning processes, including image fusion, contouring, beam arrangement, dose calculation, plan export, documents, billing, and so on. For each error, the authors estimated the frequency of occurrence, the likelihood of being undetected, and the severity of the error if it went undetected and calculated the risk priority number (RPN). The FMEA results were used to design their quality management program. In addition, the authors created a database to track the identified dosimetric errors. Periodically, the authors reevaluated the risk of errors by reviewing the internal error database and improved their quality assurance program as needed. Results: In total, the authors identified over 36 possible treatment planning related failure modes and estimated the associated occurrence, detectability, and severity to calculate the overall risk priority number. Based on the FMEA, the authors implemented various safety improvement procedures into their practice, such as education, peer review, and automatic check tools. The ongoing error tracking database provided realistic data on the frequency of occurrence with which to reevaluate the RPNs for various failure modes. Conclusions: The FMEA technique provides a systematic method for identifying and evaluating potential errors in proton treatment planning before they result in an error in patient dose delivery. The application of FMEA framework and the implementation of an ongoing error tracking system at their clinic have proven to be useful in error reduction in proton treatment planning, thus improving the effectiveness and safety of proton therapy.

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

SU-C-202-03: A Tool for Automatic Calculation of Delivered Dose Variation for Off-Line Adaptive Therapy Using Cone Beam CT

Purpose: Monitoring the delivered dose is an important task for the adaptive radiotherapy (ART) and for determining time to re-plan. A software tool which enables automatic delivered dose calculation using cone-beam CT (CBCT) has been developed and tested. Methods: The tool consists of four components: a CBCT Colleting Module (CCM), a Plan Registration Moduel (PRM), a Dose Calculation Module (DCM), and an Evaluation and Action Module (EAM). The CCM is triggered periodically (e.g. every 1:00 AM) to search for newly acquired CBCTs of patients of interest and then export the DICOM files of the images and related registrations defined in ARIA followed by triggering the PRM. The PRM imports the DICOM images and registrations, links the CBCTs to the related treatment plan of the patient in the planning system (RayStation V4.5, RaySearch, Stockholm, Sweden). A pre-determined CT-to-density table is automatically generated for dose calculation. Current version of the DCM uses a rigid registration which regards the treatment isocenter of the CBCT to be the isocenter of the treatment plan. Then it starts the dose calculation automatically. The AEM evaluates the plan using pre-determined plan evaluation parameters: PTV dose-volume metrics and critical organ doses. The tool has been tested for 10 patients. Results: Automatic plans are generated and saved in the order of the treatment dates of the Adaptive Planning module of the RayStation planning system, without any manual intervention. Once the CTV dose deviates more than 3%, both email and page alerts are sent to the physician and the physicist of the patient so that one can look the case closely. Conclusion: The tool is capable to perform automatic dose tracking and to alert clinicians when an action is needed. It is clinically useful for off-line adaptive therapy to catch any gross error. Practical way of determining alarming level for OAR is under development.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-C-202-05: Pilot Study of Online Treatment Evaluation and Adaptive Re-Planning for Laryngeal SBRT

Purpose: We have instigated a phase I trial of 5-fraction stereotactic body radiotherapy (SBRT) for advanced-stage laryngeal cancer. We conducted this pilot dosimetric study to confirm the potential utility of online adaptive re-planning to preserve treatment quality. Methods: Ten cases of larynx cancer were evaluated. Baseline and daily SBRT treatment plans were generated per trial protocol. Daily volumetric images were acquired prior to every fraction of treatment. Reference simulation CT images were deformably registered to daily volumetric images using Eclipse. Planning contours were then deformably propagated to daily images. Reference SBRT plans were directly copied to calculate delivered dose distributions on deformed reference CT images. In-house software platform has been developed to calculate cumulative dose over a course of treatment in four steps: 1) deforming delivered dose grid to reference CT images using deformation information exported from Eclipse; 2) generating tetrahedrons using deformed dose grid as vertices; 3) resampling dose to a high resolution within every tetrahedron; 4) calculating dose-volume histograms. Our inhouse software was benchmarked with a commercial software, Mirada. Results: In all ten cases including 49 fractions of treatments, delivered daily doses were completely evaluated and treatment could be re-planned within 10 minutes. Prescription dose coverage of PTV was less than intended in 53% of fractions of treatment (mean: 94%, range: 84%–98%) while minimum coverage of CTV and GTV was 94% and 97%, respectively. Maximum bystander point dose limits to arytenoids, parotids, and spinal cord remained respected in all cases, although variances in carotid artery doses were observed in a minority of cases. Conclusion: Although GTV and CTV coverage is preserved by in-room 3D image guidance of larynx SBRT, PTV coverage can vary significantly from intended plans. Online adaptive treatment evaluation and re-planning is potentially necessary and our procedure is clinically applicable to fully preserve treatment quality. This project is supported by CPRIT Individual Investigator Research Award RP150386.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-F-SPS-09: Parallel MC Kernel Calculations for VMAT Plan Improvement

Purpose: Adding kernels (small perturbations in leaf positions) to the existing apertures of VMAT control points may improve plan quality. We investigate the calculation of kernel doses using a parallelized Monte Carlo (MC) method. Methods: A clinical prostate VMAT DICOM plan was exported from Eclipse. An arbitrary control point and leaf were chosen, and a modified MLC file was created, corresponding to the leaf position offset by 0.5cm. The additional dose produced by this 0.5 cm × 0.5 cm kernel was calculated using the DOSXYZnrc component module of BEAMnrc. A range of particle history counts were run (varying from 3 × 10{sup 6} to 3 × 10{sup 7}); each job was split among 1, 10, or 100 parallel processes. A particle count of 3 × 10{sup 6} was established as the lower range because it provided the minimal accuracy level. Results: As expected, an increase in particle counts linearly increases run time. For the lowest particle count, the time varied from 30 hours for the single-processor run, to 0.30 hours for the 100-processor run. Conclusion: Parallel processing of MC calculations in the EGS framework significantly decreases time necessary for each kernel dose calculation. Particle counts lower than 1 × 10{sup 6} have too large of an error to output accurate dose for a Monte Carlo kernel calculation. Future work will investigate increasing the number of parallel processes and optimizing run times for multiple kernel calculations.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-F-R-20: Image Texture Features Correlate with Time to Local Failure in Lung SBRT Patients

Purpose: To explore possible correlation between CT image-based texture and histogram features and time-to-local-failure in early stage non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiotherapy (SBRT).Methods and Materials: From an IRB-approved lung SBRT registry for patients treated between 2009–2013 we selected 48 (20 male, 28 female) patients with local failure. Median patient age was 72.3±10.3 years. Mean time to local failure was 15 ± 7.1 months. Physician-contoured gross tumor volumes (GTV) on the planning CT images were processed and 3D gray-level co-occurrence matrix (GLCM) based texture and histogram features were calculated in Matlab. Data were exported to R and a multiple linear regression model was used to examine the relationship between texture features and time-to-local-failure. Results: Multiple linear regression revealed that entropy (p=0.0233, multiple R2=0.60) from GLCM-based texture analysis and the standard deviation (p=0.0194, multiple R2=0.60) from the histogram-based features were statistically significantly correlated with the time-to-local-failure. Conclusion: Image-based texture analysis can be used to predict certain aspects of treatment outcomes of NSCLC patients treated with SBRT. We found entropy and standard deviation calculated for the GTV on the CT images displayed a statistically significant correlation with and time-to-local-failure in lung SBRT patients.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-F-I-49: Vendor-Independent, Model-Based Iterative Reconstruction On a Rotating Grid with Coordinate-Descent Optimization for CT Imaging Investigations

Purpose: Iterative reconstruction methods show promise for improving image quality and lowering the dose in helical CT. We aim to develop a novel model-based reconstruction method that offers potential for dose reduction with reasonable computation speed and storage requirements for vendor-independent reconstruction from clinical data on a normal desktop computer. Methods: In 2012, Xu proposed reconstructing on rotating slices to exploit helical symmetry and reduce the storage requirements for the CT system matrix. Inspired by this concept, we have developed a novel reconstruction method incorporating the stored-system-matrix approach together with iterative coordinate-descent (ICD) optimization. A penalized-least-squares objective function with a quadratic penalty term is solved analytically voxel-by-voxel, sequentially iterating along the axial direction first, followed by the transaxial direction. 8 in-plane (transaxial) neighbors are used for the ICD algorithm. The forward problem is modeled via a unique approach that combines the principle of Joseph’s method with trilinear B-spline interpolation to enable accurate reconstruction with low storage requirements. Iterations are accelerated with multi-CPU OpenMP libraries. For preliminary evaluations, we reconstructed (1) a simulated 3D ellipse phantom and (2) an ACR accreditation phantom dataset exported from a clinical scanner (Definition AS, Siemens Healthcare). Image quality was evaluated in the resolution module. Results: Image quality was excellent for the ellipse phantom. For the ACR phantom, image quality was comparable to clinical reconstructions and reconstructions using open-source FreeCT-wFBP software. Also, we did not observe any deleterious impact associated with the utilization of rotating slices. The system matrix storage requirement was only 4.5GB, and reconstruction time was 50 seconds per iteration. Conclusion: Our reconstruction method shows potential for furthering research in low-dose helical CT, in particular as part of our ongoing development of an acquisition/reconstruction pipeline for generating images under a wide range of conditions. Our algorithm will be made available open-source as “FreeCT-ICD”. NIH U01 CA181156; Disclosures (McNitt-Gray): Institutional research agreement, Siemens Healthcare; Past recipient, research grant support, Siemens Healthcare; Consultant, Toshiba America Medical Systems; Consultant, Samsung Electronics.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-F-J-22: Lung VolumeVariability Assessed by Bh-CBCT in 3D Surface Image Guided Deep InspirationBreath Hold (DIBH) Radiotherapy for Left-Sided Breast Cancer

Purpose: With the increasing use of DIBH techniques for left-sided breast cancer, 3D surface-image guided DIBH techniques have improved patient setup and facilitated DIBH radiation delivery. However, quantification of the daily separation between the heart and left breast still presents a challenge. One method of assuring separation is to ensure consistent left lung filling. With this in mind, the aim of this study is to retrospectively quantify left lung volume from weekly breath hold-CBCTs (bh-CBCT) of left-sided breast patients treated using a 3D surface imaging system. Methods: Ten patients (n=10) previously treated to the left breast using the C-Rad CatalystHD system (C-RAD AG, Uppsala Sweden) were evaluated. Patients were positioned with CatalystHD and with bh-CBCT. bh-CBCTs were acquired at the validation date, first day of treatment and at subsequent weekly intervals. Total treatment courses spanned from 3 to 5 weeks. bh-CBCT images were exported to VelocityAI and the left lung volume was segmented. Volumes were recorded and analyzed. Results: A total of 41 bh-CBCTs were contoured in VelocityAI for the 10 patients. The mean left lung volume for all patients was 1657±295cc based on validation bh-CBCT. With the subsequent lung volumes normalized to the validation lung volume, the mean relative ratios for all patients were 1.02±0.11, 0.97±0.14, 0.98±0.11, 1.02±0.01, and 0.96±0.02 for week 1, 2, 3, 4, and 5, respectively. Overall, the mean left lung volume change was ≤4.0% over a 5-week course; however left lung volume variations of up to 28% were noted in a select patient. Conclusion: With the use of the C-RAD CatalystHD system, the mean lung volume variability over a 5-week course of DIBH treatments was ≤4.0%. By minimizing left lung volume variability, heart to left breast separation maybe more consistently maintained. AN Gutierrez has a research grant from C-RAD AG.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

SU-F-J-113: Multi-Atlas Based Automatic Organ Segmentation for Lung Radiotherapy Planning

Purpose: Normal organ segmentation is one time-consuming and labor-intensive step for lung radiotherapy treatment planning. The aim of this study is to evaluate the performance of a multi-atlas based segmentation approach for automatic organs at risk (OAR) delineation. Methods: Fifteen Lung stereotactic body radiation therapy patients were randomly selected. Planning CT images and OAR contours of the heart - HT, aorta - AO, vena cava - VC, pulmonary trunk - PT, and esophagus – ES were exported and used as reference and atlas sets. For automatic organ delineation for a given target CT, 1) all atlas sets were deformably warped to the target CT, 2) the deformed sets were accumulated and normalized to produce organ probability density (OPD) maps, and 3) the OPD maps were converted to contours via image thresholding. Optimal threshold for each organ was empirically determined by comparing the auto-segmented contours against their respective reference contours. The delineated results were evaluated by measuring contour similarity metrics: DICE, mean distance (MD), and true detection rate (TD), where DICE=(intersection volume/sum of two volumes) and TD = {1.0 - (false positive + false negative)/2.0}. Diffeomorphic Demons algorithm was employed for CT-CT deformable image registrations. Results: Optimal thresholds were determined to be 0.53 for HT, 0.38 for AO, 0.28 for PT, 0.43 for VC, and 0.31 for ES. The mean similarity metrics (DICE[%], MD[mm], TD[%]) were (88, 3.2, 89) for HT, (79, 3.2, 82) for AO, (75, 2.7, 77) for PT, (68, 3.4, 73) for VC, and (51,2.7, 60) for ES. Conclusion: The investigated multi-atlas based approach produced reliable segmentations for the organs with large and relatively clear boundaries (HT and AO). However, the detection of small and narrow organs with diffused boundaries (ES) were challenging. Sophisticated atlas selection and multi-atlas fusion algorithms may further improve the quality of segmentations.

60 APPLIED LIFE SCIENCES↗