Engineering topics

Souris, K

Publications and source records attributed to Souris, K.

3 records

DOE OSTI2016

SU-F-T-121: Abdominal Compression Effectively Reduces the Interplay Effect and Enables Pencil Beam Scanning Proton Therapy of Liver Tumors

Purpose: To study if abdominal compression can reduce breathing motion and mitigate interplay effect in pencil beam scanning proton therapy (PBSPT) treatment of liver tumors in order to better spare healthy liver volumes compared with photon therapy. Methods: Ten patients, six having large tumors initially treated with IMRT and four having small tumors treated with SBRT, were replanned for PBSPT. ITV and beam-specific PTVs based on 4D-CT were used to ensure target coverage in PBSPT. The use of an abdominal compression belt and volumetric repainting was investigated to mitigate the interplay effect between breathing motion and PBSPT dynamic delivery. An in-house Matlab script has been developed to simulate this interplay effect. The dose is computed on each phase individually by sorting all spots according to their simulated delivery timing. The final dose distribution is then obtained by accumulating all dose maps to a reference phase. Results: For equivalent target coverage PBSPT reduced average healthy liver dose by 9.5% of the prescription dose compared with IMRT/SBRT. Abdominal compression of 113.2±42.2 mmHg was effective for all 10 patients and reduced average motion by 2.25 mm. As a result, the average ITV volume decreased from 128.2% to 123.1% of CTV volume. Similarly, the average beam-specific PTV volume decreased from 193.2% to 183.3%. For 8 of the 10 patients, the average motion was reduced below 5 mm, and up to 3 repainting were sufficient to mitigate interplay. For the other two patients with larger residual motion, 4–5 repainting were needed. Conclusion: We recommend evaluation of the 4DCT motion histogram following simulation and the interplay effect following treatment planning in order to personalize the use of compression and volumetric repainting for each patient. Abdominal compression enables safe and more effective PBS treatment of liver tumors by reduction of motion and interplay effect. Kevin Souris is supported by IBA and Televie Grant from F.R.S.-FNRS. Liyong Lin is partially supported by Varian.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

MO-FG-CAMPUS-TeP3-01: A Model of Baseline Shift to Improve Robustness of Proton Therapy Treatments of Moving Tumors

Purpose: The shift in mean position of a moving tumor also known as “baseline shift”, has been modeled, in order to automatically generate uncertainty scenarios for the assessment and robust optimization of proton therapy treatments in lung cancer. Methods: An average CT scan and a Mid-Position CT scan (MidPCT) of the patient at the planning time are first generated from a 4D-CT data. The mean position of the tumor along the breathing cycle is represented by the GTV contour in the MidPCT. Several studies reported both systematic and random variations of the mean tumor position from fraction to fraction. Our model can simulate this baseline shift by generating a local deformation field that moves the tumor on all phases of the 4D-CT, without creating any non-physical artifact. The deformation field is comprised of normal and tangential components with respect to the lung wall in order to allow the tumor to slip within the lung instead of deforming the lung surface. The deformation field is eventually smoothed in order to enforce its continuity. Two 4D-CT series acquired at 1 week of interval were used to validate the model. Results: Based on the first 4D-CT set, the model was able to generate a third 4D-CT that reproduced the 5.8 mm baseline-shift measured in the second 4D-CT. Water equivalent thickness (WET) of the voxels have been computed for the 3 average CTs. The root mean square deviation of the WET in the GTV is 0.34 mm between week 1 and week 2, and 0.08 mm between the simulated data and week 2. Conclusion: Our model can be used to automatically generate uncertainty scenarios for robustness analysis of a proton therapy plan. The generated scenarios can also feed a TPS equipped with a robust optimizer. Kevin Souris, Ana Barragan, and Dario Di Perri are financially supported by Televie Grants from F.R.S.-FNRS.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

WE-AB-209-01: A Monte Carlo-Based Method to Include Random Errors in Robust Optimization

Purpose: To develop an efficient method to implement random set-up errors and organ motion in robust optimization for proton therapy treatment planning. Methods: The plans were created with an in-house robust optimizer, coupled with a super-fast Monte Carlo (MC) engine (less than 1 minute for final dose). MC simulates random errors by shifting randomly the starting point of each particle, according to their probability distribution. Such strategy assumes a sufficient number of treatment fractions. Two strategies are presented: 1) Full robust optimization with beamlets that already include the effect of random errors and 2) Mixed robust optimization, where the nominal beamlets are involved but a correction term C modifies the prescription. Starting from C=0, the method alternates optimization of the spot weights with the nominal beamlets and updates of C, with C=Drandom-Dnominal and where Drandom results from a regular MC computation (without pre-computed beamlets) that simulates random errors. Updates of C can be triggered as often as necessary by running the MC engine with the last corrected values for the spot weights as input. The method was applied to lung and prostate cases. For both patients the range error was set to 3%, systematic setup error to 5mm and standard deviation for random errors to 5 mm. Comparison between full robust optimization and the mixed strategy (with 3 updates of C) is presented. Results: Target coverage was far below the clinical constraints (D{sub 95} > 95% of the prescribed dose) for plans where random errors were not simulated, especially for lung case. However, by using the proposed strategies the plans achieved a target coverage above clinical constraints. Conclusion: Full robust optimization gives better results than the mixed strategy, but the latter can be useful in cases where a MC engine is not available or too computationally intensive for beamlets calculation.

60 APPLIED LIFE SCIENCES↗