The Geo-Privacy Bonus of Popular Photo Enhancements
Abstract not provided
97 MATHEMATICS AND COMPUTING↗
Engineering topics
Li, K
Publications and source records attributed to Li, K.
Toward Reliable Validation of HPC Network Simulation Models
Abstract not provided
97 MATHEMATICS AND COMPUTING↗
SU-F-P-34: Commission of Enhanced Dynamic Wedge of Varian Truebeam Linac System with Feature Study
Purpose: To evaluate an Enhanced Dynamic Wedge (EDW) as part of machine commission process with feature study. Methods: The EDW system in this study was from a Truebeam, which is the Linear accelerator manufactured by Varian Medical Systems. The EDW feature vectors includes selected elements. These elements were dosimetric output spots check, field size, wedge angles, dose rate, collimator orientation, and different energy settings. Point dose measurement was done by a PTW farmer chamber, and profiles were measured by Gafchromic EBT2 films positing at different depths of the Solidwater based on the study elements. The output spot measurements were done with PTW farmer chamber with Solidwater setting for all orientation and wedge angles in the EDW system. The profiles comparisons were done by IMRT measurement function in RIT software at version 6.3. And the films were scanned by Vidar scanner. Dosimetry calculation were done by using the same Solidwater scanned by GE LightSpeed CT in Eclipse Treatment Planning System (TPS). Then measurements were compared to simulation results in TPS. Results: The energy average percentage difference between chamber measurement and TPS was 0.16% with standard deviation (SD) at 0.93%. For selected features, the average percentage difference between film measurement and computation was 0.93% with SD at 1.55% in horizontal profiles, and 1.18% with SD at 0.98% at vertical profiles. The average gamma difference for film measurement and TPS computing results was at 0.924 with SD at 0.314. Conclusion: A feature vector was developed to describe the commission of EDW, and developing a complete set of features for sufficiency of commission of a LINAC function could provide optimal commission instance with acceptable confident level of clinical application of the machine. Given the institution specific vector pattern and big data process, it could provide wide range clinical outcome comparison information in application of EDW.
60 APPLIED LIFE SCIENCES↗
SU-F-P-51: Similarity Analysis of the Linear Accelerator Machines Based On Clinical Simulation
Purpose: To evaluate the clinical rationale for Truebeam and Trilogy Linac machines from Varian Medical System as exchangeable treatment modalities in the same radiation oncology department. Methods: Intensity Modulated Radiotherapy (IMRT) plans for different diseases were selected for this study. These disease sites included brain, head and neck, breast, lung, and prostate. The parameters selected for this study were the energy amount, Monitor Unit (MU); dose coverage of target reflected by prescription isodose volume(PIV); dose spillage described by the volume of 50% isodoseline of the prescription; and dose homogeneities represented by the maximum dose (MaxD) and the minimum dose (MinD) of target volume (TV) and critical structure (CS). Each percentage difference between the values of these parameters formed an element of a matrix, which was called Similarity Comparison Matrix(SCM). The elements of the SCM were then simplified by dimensional conversion algorithm, which was used to determine clinical similarity between two machines through a single value. Results: For the selected clinical cases in this study, the average percentage differences between Trilogy and Truebeam in MU was 0.28% with standard deviation(SD) 0.66%, PIV was 0.23% with SD 0.20%, Volume at 50% prescription dose was 0.31% with SD at 0.78%, MaxD at TV is 0.26% with SD 0.35%, MinD at TV is −0.04% with SD 0.51%, MaxD in CS is −0.53% with SD 0.92%, and MinD in CS 3.31%, with SD at 2.89%. The sum, product, geometric and harmonic mean for the matrix elements were 19.0%, 0.00%, 0.19%, and 0.00%. Conclusion: A method to compare two machines in clinical level was developed and some reference values were calculated for decision-making in clinical practice, and this strategy could be expanded to different clinical applications.
60 APPLIED LIFE SCIENCES↗
SU-F-T-348: The Impact of Model Library Population On RapidPlan Based Dose-Volume Histograms (DVHs) Prediction for Rectal Cancer Patients Treated with Volumetric-Modulated Radiotherapy (VMAT)
Purpose: RapidPlan uses a library consisting of expert plans from different patients to create a model that can predict achievable dose-volume histograms (DVHs) for new patients. The goal of this study is to investigate the impacts of model library population (plan numbers) on the DVH prediction for rectal cancer patients treated with volumetric-modulated radiotherapy (VMAT) Methods: Ninety clinically accepted rectal cancer patients’ VMAT plans were selected to establish 3 models, named as Model30, Model60 and Model90, with 30,60, and 90 plans in the model training. All plans had sufficient target coverage and bladder and femora sparings. Additional 10 patients were enrolled to test the DVH prediction differences with these 3 models. The predicted DVHs from these 3 models were compared and analyzed. Results: Predicted V40 (Vx, percent of volume that received x Gy for the organs at risk) and Dmean (mean dose, cGy) of the bladder were 39.84±13.38 and 2029.4±141.6 for the Model30,37.52±16.00 and 2012.5±152.2 for the Model60, and 36.33±18.35 and 2066.5±174.3 for the Model90. Predicted V30 and Dmean of the left femur were 23.33±9.96 and 1443.3±114.5 for the Model30, 21.83±5.75 and 1436.6±61.9 for the Model60, and 20.31±4.6 and 1415.0±52.4 for the Model90.There were no significant differences among the 3 models for the bladder and left femur predictions. Predicted V40 and Dmean of the right femur were 19.86±10.00 and 1403.6±115.6 (Model30),18.97±6.19 and 1401.9±68.78 (Model60), and 21.08±7.82 and 1424.0±85.3 (Model90). Although a slight lower DVH prediction of the right femur was found on the Model60, the mean differences for V30 and mean dose were less than 2% and 1%, respectively. Conclusion: There were no significant differences among Model30, Model60 and Model90 for predicting DVHs on rectal patients treated with VMAT. The impact of plan numbers for model library might be limited for cancers with similar target shape.
60 APPLIED LIFE SCIENCES↗
SU-F-T-358: Is Auto-Planning Useful for Volumetric-Modulated Arc Therapy Planning in Rectal Cancer Radiotherapy?
Purpose: To evaluate whether Auto-Planning based volumetric-modulated radiotherapy (auto-VMAT) can reduce manual interaction time during treatment planning and improve plan quality for rectal cancer radiotherapy. Methods: Ten rectal cancer patients (stage II and III) after radical resection using Dixon surgery were enrolled. All patients were treated with VMAT technique. The manual VMAT plans (man-VMAT) were designed in the Pinnacle treatment planning system (Version 9.10) following the standard treatment planning procedure developed in our department. Clinical plans were manually designed by our experienced dosimetrists. Additionally, an auto-VMAT plan was created for each patient using Auto-Planning module. However, manual interaction was still applied to meet the clinical requirements. The treatment planning time and plan quality surrogated by the DVH parameters were compared between manual and automated plans. Results: The total planning time and manual interaction time were 50.38 and 4.47 min for the auto-VMAT and 36.81 and 16.94 min for the man-VMAT (t=60.14,−23.86; p=0.000, 0.000). In terms of plan quality, both plans meet the clinical requirements. The PTV homogeneity index (HI) and conformity index (CI) were 0.054 and 0.822 for the auto-VMAT and 0.059 and 0.815 for the man-VMAT (t=−1.72, 0.36;p=0.119,0.730).Compared to the man-VMAT, the auto-VMAT showed reduction of 11.9% and 0.7% in V40 and V50 of the bladder, respectively.The V30 and D mean were reduced by 14.0% and 5.1Gy in the left femur and 12.2% and 3.8Gy in the right femur. Conclusion: The Auto-Planning based VMAT plans not only shows similar or superior plan quality to the manual ones in the rectal cancer radiotherapy, but also improve the planning efficiency significantly. However, manual interactions are still required to achieve a clinically acceptable plan based on our experiences.
60 APPLIED LIFE SCIENCES↗
SU-F-T-359: Incorporating Dose Volume Histogram Prediction Into Auto-Planning for Volumetric-Modulated Arc Therapy in Rectal Cancer
Purpose: To incorporate dose volume histogram (DVH) prediction into Auto-Planning for volumetric-modulated arc therapy (VMAT) treatment planning and investigate the benefit of this new technique for rectal cancer. Methods: Ninety clinically accepted VMAT plans for patients with rectal cancer were selected and trained in the RapidPlan for DVH prediction. Both internal and external validations were performed before implementing the prediction model. A new VMAT planning method (hybrid-VMAT) was created with combining the DVH prediction and Auto-Planning. For each new patient, the DVH will be predicted and individual DVH constrains will be obtained and were exported as the original optimization parameters to the Auto-Planning (Pinnacle3 treatment planning system, v9.10) for planning. A total of 20 rectal cancer patients previously treated with manual VMAT (manual-VMAT) plans were replanned using this new method. Dosimetric comparisons were performed between manual VMAT and new method plans. Results: Hybrid-VMAT shows similar PTV coverage to manual-VMAT in D2%, D98% and HI (p>0.05) and superior coverage in CI (p=0.000). For the bladder, the means of V40 and mean dose are 36.0% and 35.6Gy for hybrid-VMAT and 42% and 38.0Gy for the manual-VMAT. For the left (right) femur, the means of V30 and mean dose are 10.6% (11.6%) and 17.9Gy (19.2Gy) for the hybrid-VMAT and 25.6% (24.1%) and 27.3Gy (26.2Gy) for the manual-VMAT. The hybrid-VMAT has significantly improved the organs at risk sparing. Conclusion: The integration of DVH prediction and Auto-Planning significantly improve the VMAT plan quality in the rectal cancer radiotherapy. Our results show the benefit of the new method and will be further investigated in other tumor sites.
60 APPLIED LIFE SCIENCES↗
SU-F-T-447: The Impact of Treatment Planning Methods On RapidPlan Modeling for Rectum Cancer
Purpose: To investigate the dose volume histogram (DVH) prediction varieties based on intensity modulate radiotherapy (IMRT) plan or volume arc modulate radiotherapy (VMAT) plan models on the RapidPlan. Methods: Two DVH prediction models were generated in this study, including an IMRT model trained from 83 IMRT rectum plans and a VMAT model trained from 60 VMAT rectum plans. In the internal validation, 20 plans from each training database were selected to verify the clinical feasibility of the model. Then, 10 IMRT plans (PIMRT-by-IMRT-model) generated from IMRT model and 10 IMRT plans generated from VMAT model (PIMRT-by-VMAT-model) were compared on the dose to organs at risk (OAR), which included bladder, left and right femoral heads. The similar comparison was also performed on the VMAT plans generated from IMRT model (PVMAT-by-IMRT-model) and VMAT plans generated from VMAT (PVMAT-by-VMAT-model) model. Results: For the internal validation, all plans from IMRT or VMAT model shows significantly improvement on OAR sparing compared with the corresponded clinical ones. Compared to the PIMRT-by-VMAT-model, the PIMRT-by-IMRT-model has a reduction of 6.90±3.87%(p<0.001) on V40 6.63±3.62%(p<0.001) on V45 and 4.74±2.26%(p<0.001) on V50 in bladder; and a mean dose reduction of 2.12±1.75Gy(p=0.004) and 2.84±1.53Gy(p<0.001) in right and left femoral head, respectively. There was no significant difference on OAR sparing between PVMAT-by-IMRT-model and PVMAT-by-VMAT-model. Conclusion: The IMRT model for the rectal cancer in the RapidPlan can be applied to for VMAT planning. However, the VMAT model is not suggested to use in the IMRT planning. Cautions should be taken that the planning model based on some technique may not feasible to other planning techniques.
60 APPLIED LIFE SCIENCES↗
TU-D-201-07: Severity Indication in High Dose Rate Brachytherapy Emergency Response Procedure
Purpose: Understanding the corresponding dose to different staff during the High Dose Rate (HDR) Brachytherapy emergency response procedure could help to develop a strategy in efficiency and effective action. In this study, the variation and risk analysis methodology was developed to simulation the HDR emergency response procedure based on severity indicator. Methods: A GammaMedplus iX HDR unit from Varian Medical System was used for this simulation. The emergency response procedure was decomposed based on risk management methods. Severity indexes were used to identify the impact of a risk occurrence on the step including dose to patient and dose to operation staff by varying the time, HDR source activity, distance from the source to patient and staff and the actions. These actions in 7 steps were to press the interrupt button, press emergency shutoff switch, press emergency button on the afterloader keypad, turn emergency hand-crank, remove applicator from the patient, disconnect transfer tube and move afterloader from the patient, and execute emergency surgical recovery. Results: Given the accumulated time in second at the assumed 7 steps were 15, 5, 30, 15, 180, 120, 1800, and the dose rate of HDR source is 10 Ci, the accumulated dose in cGy to patient at 1cm distance were 188, 250, 625, 813, 3063, 4563 and 27063, and the accumulated exposure in rem to operator at outside the vault, 1m and 10cm distance were 0.0, 0.0, 0.1, 0.1, 22.6, 37.6 and 262.6. The variation was determined by the operators in action at different time and distance from the HDR source. Conclusion: The time and dose were estimated for a HDR unit emergency response procedure. It provided information in making optimal decision during the emergency procedure. Further investigation would be to optimize and standardize the responses for other emergency procedure by time-spatial-dose severity function.
60 APPLIED LIFE SCIENCES↗