- Email: [email protected]
Sequential annealing-gradient gamma knife radiosurgery optimization
Proceedings of the 43rd Annual ASTRO Meeting
planning system. Pencil beams are computed using the actual linac head and MLC physical geometry of an Elekta SL20 linac with the MLC shaped to the beam’s eye view of the target plus a generous margin in each beam orientation. The MC calculations are performed on 17 Pentium III CPUs running in parallel. In each iteration of the inverse planning process, the matrices of the intensity and the field-shape related factors change but the MC-generated pencil beam matrices remain the same. A simple convolution algorithm is used to evaluate dose and the associated cost function at each iteration. The technique is validated by measurement in two steps, first the MC method then the entire optimization process. For the verification of the MC, the dose distributions for unmodulated fields of variable size and shape representing individual field segments of an IMRT treatment are calculated using the MC and are compared with measurements. The validation of entire inverse planning process is carried out on an inhomogeneous Rando phantom, using the measurement as well as a final forward MC calculation based on the leaf positions generated by the inverse planning. Results: The MC calculations agree with measurements to within 2%, indicating the correctness of the MC modeling for the linac head and MLC. The plans generated for the Rando were found to agree well with measurement and with the final MC calculation. The two MC computations, first for initial pencil beams and then for final leaf positions, typically require 4-8 hours for CPU time. Since the computation is performed parallel, the computing time can be further reduced by using more CPUs to the network. Conclusion: By separating the pencil beam dose distributions and other factors in the dose calculation, we have shown that Monte Carlo technique is feasible for dose computation for pencil beam-based inverse planning algorithms. As compared with semi-empirical methods, accuracy is improved at a cost of increased computation time per pencil beam per CPU. This is not seen as a significant barrier to implementation of MC simulation for inverse planning as the cost/performance improvement of computers allows for large parallel computation networks to be used for inverse planning. Future work includes testing on patients for various sites, especially for head and neck and lung, and integrating the processes.
2344
Monte Carlo Dose Calculations for MIMiC-Based IMRT Treatments
1
M.C. Lee , S. Lam2, P. Xia3, C.M. Ma1 1 Radiation Oncology, Stanford University School of Medicine, Stanford, CA, 2NOMOS Corporation, Sewickley, PA, 3 Radiation Oncology, University of California at San Francisco, San Francisco, CA Purpose: At present, a commercial serial tomotherapy system (MIMiC, NOMOS Corp., Sewickley, PA) is the most commonly used intensity-modulated radiation therapy (IMRT) system. Treatment plans are generated by optimizing weights of beamlet dose distributions as computed by finite-sized pencil beam (FSPB) algorithms. It has been shown that in regions of electronic disequilibrium and interface regions, FSPB calculations may not correctly estimate the delivered dose, leading to dosimetric errors in dynamic MLC (DMLC) IMRT plans. The Monte Carlo method has been shown to accurately predict dose under these circumstances. In this work, we utilize Monte Carlo dose calculations to recalculate MIMiC plans to determine whether the FSPB dose errors seen with static gantry DMLC also exist with the MIMiC. Materials and Methods: The EGS4/MCDOSE dose calculation system has been modified to simulate the dose delivery from the continuously rotating fan beam binary collimator system. The continuous arcs were modeled as a series of several thousand static fields to remove potential dose artefacts that may occur by discretizing the delivery over a coarse angular grid. Leaf settings were modeled by applying weighting factors to the photon fluence distribution. Leaf transmission, scatter, and tongue-and-groove effects were incorporated in this intensity map. Source models were derived for two accelerators for use in dose calculations: a Varian Clinac 6/100, based on measured data, and a Siemens MXE, based on EGS4/BEAM simulations. The Monte Carlo implementation was validated by comparing dose distributions in water phantoms with FSPB calculations as implemented in the NOMOS CORVUS system. Forward dose calculations were then performed on treatment plans for head and neck targets using the Monte Carlo method and the CORVUS system. Included in the plans were oblique and orthogonal couch positions with intersecting arcs. The effect of performing Monte Carlo simulations with ICRU standard bone material or water with varying densities (as in the CORVUS model) was analyzed. Doses were reported as dose to water for consistency between the Monte Carlo and FSPB calculations. Results: In homogeneous phantoms, the CORVUS FSPB and Monte Carlo calculations agreed to within 4%, as consistent with previously published results. Discrepancies of 5% (relative to prescription) were found in target regions, leading to some loss of target dose homogeneity. Errors of up to 10% were found in regions surrounding sinus cavities, which in some cases included the target volume. Much larger discrepancies (⬍ 10%) were found in the air cavities, affecting DVHs when the target volume included the air volume though the clinical impact of this is negligible. Excellent agreement was found for critical structures located some distance away from the high dose region. Conclusion: The CORVUS FSPB algorithm accurately predicts dose both in homogeneous phantoms and in high dose regions (such as the target) in heterogeneous phantoms. Significant under- and overdosing may occur in regions of high electronic disequilibrium, such as regions directly adjacent to air cavities. This suggests that when target structures are located in close proximity to air cavities, accurate dose calculation algorithms such as the Monte Carlo method should be employed to properly assess the delivered dose. The results of this research also suggest the potential benefit of developing treatment planning system in which the optimization is based upon doses calculated by Monte Carlo or other highly accurate systems.
2345
Sequential Annealing-Gradient Gamma Knife Radiosurgery Optimization
R. Ove, R.A. Popple Radiation Oncology, UAB, Birmingham, AL Purpose: Inverse planning of complex treatment delivery schemes is generally performed computationally with either gradient methods or simulated annealing. Both of these techniques have proven effective, and have been packaged as part of commercially available planning systems. Simulated annealing is very effective in finding an approximation of a globally optimal solution. However, late convergence can be slow, and we have observed considerable dose inhomogeneity in plans
407
408
I. J. Radiation Oncology
● Biology ● Physics
Volume 51, Number 3, Supplement 1, 2001
optimized with this method. Conversely, gradient methods are well suited to convergence once near a potential solution, but there is no assurance that the solution will be globally optimal. We explored the possibility that employing simulated annealing followed by gradient optimization can improve on either method alone, within similar computation times. Methods: Two model inverse planning programs were developed for radiosurgical optimization. The first is a simple simulated annealing algorithm, and the second is a local optimizer utilizing a gradient approach. Both of these programs attempt to minimize the same objective function, which is critical to the success of the hybrid method. The objective function chosen is a least squares dose-matching function, attempting to match the prescription dose in the target volume and minimize dose in normal tissue, with differential weighting of tissues. The test target was chosen to be two adjacent and overlapping spheres of different radii. Solutions were restricted to have 4 shots: 18mm, 2x14mm, and 8mm. Dwell times and shot locations were then optimized. This problem has multiple minima, one of which is a global minimum which arises when the 8mm shot is located in the smaller sphere. In this model, dose homogeneity is represented by the minimum isodose covering the target. Results: 200 trials using the gradient method were done with random initial shot locations. The gradient method reached the region of the global optimum (minimum isodose coverage ⬎ 70%) 26 times, settled on a local minima 165 times, and failed to converge 9 times. Two trials with simulated annealing reached the region of the global optimum both times, but required very long computation times. A sequential annealing-gradient technique, in which the simulated annealing is terminated early and the solution refined with a gradient search, converged to the global minimum in 78 out of 80 trials and required substantially less computation than simulated annealing alone. The sequential technique improved dose homogeneity of simulated annealing solution, with the minimum isodose covering the target increasing from 73% ⫹/- 5% (mean ⫹/- standard deviation) to 76% ⫹/- 4%. Modification of the hybrid method by replacing simulated annealing with a random search using similar computation time was inferior. Repeating the gradient solver from random initial data can duplicate the results of the hybrid, but only with substantially greater computation time. Conclusions: Sequential implementation of simulated annealing and gradient optimization has the potential to improve on either method alone, allowing reliable approximation of the global solution followed by rapid refinement. The method is limited to applications where both techniques can be implemented, which eliminates problems with discrete variables.
2346
An Iterative “Step & Shoot” MLC-IMRT Segmentation Algorithm for Continuous Intensity Maps
S.X. Chang, L.D. Potter Dept. of Radiation Oncology, Univ. of North Carolina Medical School, Chapel Hill, NC Purpose: The conversion of the continuous intensity map generated by a dose optimization algorithm to a set of treatment delivery parameters can play a crucial role in the quality and efficiency of the IMRT treatment. The discrete intensity maps (1cm x 1cm) delivered by the “step & shoot” MLC-IMRT technique can lead to results that are of lower quality than those of continuous maps due to lack of resolution. Several hardware solutions have been developed, i. e. introducing 5-mm MLC leaves and treatment table movement. However, this can lead to the reduction of treatment efficiency. We present a software approach to improve the quality and efficiency of the “step & shoot” treatment delivered by the conventional MLC accelerators. Materials and Methods: The continuous intensity maps produced by the in-house TPS PlanUNC using the index-dose gradient optimization algorithm are used for the MLC segmentation. The MLC segments are generated iteratively based on the residual intensity map to be delivered. A base portion (slab) of the map with the optimal height is “sliced” from the map and the appropriate MLC segment field to deliver the intensity slab is calculated. The preferable collimator angle for the segment field is chosen based on two weighted criteria: 1) preservation of the steep gradient portion of the intensity map slice and 2) minimization of the difference between the shape of the slice and that of the MLC segment field. Areas of overdose and underdose can be weighted differently for separate considerations. Once the segment field is determined the intensity map it delivers is calculated using the PlanUNC TPS photon source model. This intensity map delivered by the MLC segment field is then subtracted from the residual intensity map. The above steps are repeated until the residual map is reduced to an acceptable level. Results: The iterative process of the residual intensity map minimization is demonstrated using clinical cases. We often found that for each slice of the intensity map the MLC leaf orientation (collimator angle) that best conforms to the shape of the intensity map slice is different from the collimator angle that best fits the steep gradient regions of the intensity map slice. A weighting method between the two considerations is presented. The dose optimization quality (based on how well the original optimization goals are met) and treatment efficiency of the “step & shoot” treatment generated by this algorithm are compared to those of the continuous maps for clinical cases. A similar comparison is also made with other segmentation methods using the intensity maps from the same dose optimization. Other important segmentation issues will also be discussed. Conclusion: We have developed a new MLC-IM segmentation algorithm for continuous intensity maps. It is able to focus on the regions of the map that are likely to play an important role in achieving the optimization objectives. This algorithm also makes use of the continuous ranges of the collimator angle and the MLC leaf positioning to minimize the impact of the MLC leaf width in IM resolution. We believe this algorithm has the potential to increase the quality of the “step & shoot” IMRT treatment and may also increase the treatment efficiency.
2347
Change in Quality of Life (QOL) and Function within the First Year in Patients Who Received Primary or Post-Operative Radiotherapy for Advanced Stage Head and Neck Cancer
C.K. Gwede, D. Johnson, B. Sauder, H. Divan, A. Trotti Radiation Oncology, University of South Florida, Moffitt Cancer Center, Tampa, FL Purpose: Patients with advanced stage head and neck cancer (HNC) experience significant morbidity and debilitating effects related to disease and treatment. The areas of dysfunction are well documented, but the temporal patterns of change during the first year after treatment are not well described. Understanding the trajectories of change in overall QOL and individual areas of function may facilitate the management of patients. Thus, we report the patterns of change in QOL in patients who received primary or post-operative radiotherapy.
planning system. Pencil beams are computed using the actual linac head and MLC physical geometry of an Elekta SL20 linac with the MLC shaped to the beam’s eye view of the target plus a generous margin in each beam orientation. The MC calculations are performed on 17 Pentium III CPUs running in parallel. In each iteration of the inverse planning process, the matrices of the intensity and the field-shape related factors change but the MC-generated pencil beam matrices remain the same. A simple convolution algorithm is used to evaluate dose and the associated cost function at each iteration. The technique is validated by measurement in two steps, first the MC method then the entire optimization process. For the verification of the MC, the dose distributions for unmodulated fields of variable size and shape representing individual field segments of an IMRT treatment are calculated using the MC and are compared with measurements. The validation of entire inverse planning process is carried out on an inhomogeneous Rando phantom, using the measurement as well as a final forward MC calculation based on the leaf positions generated by the inverse planning. Results: The MC calculations agree with measurements to within 2%, indicating the correctness of the MC modeling for the linac head and MLC. The plans generated for the Rando were found to agree well with measurement and with the final MC calculation. The two MC computations, first for initial pencil beams and then for final leaf positions, typically require 4-8 hours for CPU time. Since the computation is performed parallel, the computing time can be further reduced by using more CPUs to the network. Conclusion: By separating the pencil beam dose distributions and other factors in the dose calculation, we have shown that Monte Carlo technique is feasible for dose computation for pencil beam-based inverse planning algorithms. As compared with semi-empirical methods, accuracy is improved at a cost of increased computation time per pencil beam per CPU. This is not seen as a significant barrier to implementation of MC simulation for inverse planning as the cost/performance improvement of computers allows for large parallel computation networks to be used for inverse planning. Future work includes testing on patients for various sites, especially for head and neck and lung, and integrating the processes.
2344
Monte Carlo Dose Calculations for MIMiC-Based IMRT Treatments
1
M.C. Lee , S. Lam2, P. Xia3, C.M. Ma1 1 Radiation Oncology, Stanford University School of Medicine, Stanford, CA, 2NOMOS Corporation, Sewickley, PA, 3 Radiation Oncology, University of California at San Francisco, San Francisco, CA Purpose: At present, a commercial serial tomotherapy system (MIMiC, NOMOS Corp., Sewickley, PA) is the most commonly used intensity-modulated radiation therapy (IMRT) system. Treatment plans are generated by optimizing weights of beamlet dose distributions as computed by finite-sized pencil beam (FSPB) algorithms. It has been shown that in regions of electronic disequilibrium and interface regions, FSPB calculations may not correctly estimate the delivered dose, leading to dosimetric errors in dynamic MLC (DMLC) IMRT plans. The Monte Carlo method has been shown to accurately predict dose under these circumstances. In this work, we utilize Monte Carlo dose calculations to recalculate MIMiC plans to determine whether the FSPB dose errors seen with static gantry DMLC also exist with the MIMiC. Materials and Methods: The EGS4/MCDOSE dose calculation system has been modified to simulate the dose delivery from the continuously rotating fan beam binary collimator system. The continuous arcs were modeled as a series of several thousand static fields to remove potential dose artefacts that may occur by discretizing the delivery over a coarse angular grid. Leaf settings were modeled by applying weighting factors to the photon fluence distribution. Leaf transmission, scatter, and tongue-and-groove effects were incorporated in this intensity map. Source models were derived for two accelerators for use in dose calculations: a Varian Clinac 6/100, based on measured data, and a Siemens MXE, based on EGS4/BEAM simulations. The Monte Carlo implementation was validated by comparing dose distributions in water phantoms with FSPB calculations as implemented in the NOMOS CORVUS system. Forward dose calculations were then performed on treatment plans for head and neck targets using the Monte Carlo method and the CORVUS system. Included in the plans were oblique and orthogonal couch positions with intersecting arcs. The effect of performing Monte Carlo simulations with ICRU standard bone material or water with varying densities (as in the CORVUS model) was analyzed. Doses were reported as dose to water for consistency between the Monte Carlo and FSPB calculations. Results: In homogeneous phantoms, the CORVUS FSPB and Monte Carlo calculations agreed to within 4%, as consistent with previously published results. Discrepancies of 5% (relative to prescription) were found in target regions, leading to some loss of target dose homogeneity. Errors of up to 10% were found in regions surrounding sinus cavities, which in some cases included the target volume. Much larger discrepancies (⬍ 10%) were found in the air cavities, affecting DVHs when the target volume included the air volume though the clinical impact of this is negligible. Excellent agreement was found for critical structures located some distance away from the high dose region. Conclusion: The CORVUS FSPB algorithm accurately predicts dose both in homogeneous phantoms and in high dose regions (such as the target) in heterogeneous phantoms. Significant under- and overdosing may occur in regions of high electronic disequilibrium, such as regions directly adjacent to air cavities. This suggests that when target structures are located in close proximity to air cavities, accurate dose calculation algorithms such as the Monte Carlo method should be employed to properly assess the delivered dose. The results of this research also suggest the potential benefit of developing treatment planning system in which the optimization is based upon doses calculated by Monte Carlo or other highly accurate systems.
2345
Sequential Annealing-Gradient Gamma Knife Radiosurgery Optimization
R. Ove, R.A. Popple Radiation Oncology, UAB, Birmingham, AL Purpose: Inverse planning of complex treatment delivery schemes is generally performed computationally with either gradient methods or simulated annealing. Both of these techniques have proven effective, and have been packaged as part of commercially available planning systems. Simulated annealing is very effective in finding an approximation of a globally optimal solution. However, late convergence can be slow, and we have observed considerable dose inhomogeneity in plans
407
408
I. J. Radiation Oncology
● Biology ● Physics
Volume 51, Number 3, Supplement 1, 2001
optimized with this method. Conversely, gradient methods are well suited to convergence once near a potential solution, but there is no assurance that the solution will be globally optimal. We explored the possibility that employing simulated annealing followed by gradient optimization can improve on either method alone, within similar computation times. Methods: Two model inverse planning programs were developed for radiosurgical optimization. The first is a simple simulated annealing algorithm, and the second is a local optimizer utilizing a gradient approach. Both of these programs attempt to minimize the same objective function, which is critical to the success of the hybrid method. The objective function chosen is a least squares dose-matching function, attempting to match the prescription dose in the target volume and minimize dose in normal tissue, with differential weighting of tissues. The test target was chosen to be two adjacent and overlapping spheres of different radii. Solutions were restricted to have 4 shots: 18mm, 2x14mm, and 8mm. Dwell times and shot locations were then optimized. This problem has multiple minima, one of which is a global minimum which arises when the 8mm shot is located in the smaller sphere. In this model, dose homogeneity is represented by the minimum isodose covering the target. Results: 200 trials using the gradient method were done with random initial shot locations. The gradient method reached the region of the global optimum (minimum isodose coverage ⬎ 70%) 26 times, settled on a local minima 165 times, and failed to converge 9 times. Two trials with simulated annealing reached the region of the global optimum both times, but required very long computation times. A sequential annealing-gradient technique, in which the simulated annealing is terminated early and the solution refined with a gradient search, converged to the global minimum in 78 out of 80 trials and required substantially less computation than simulated annealing alone. The sequential technique improved dose homogeneity of simulated annealing solution, with the minimum isodose covering the target increasing from 73% ⫹/- 5% (mean ⫹/- standard deviation) to 76% ⫹/- 4%. Modification of the hybrid method by replacing simulated annealing with a random search using similar computation time was inferior. Repeating the gradient solver from random initial data can duplicate the results of the hybrid, but only with substantially greater computation time. Conclusions: Sequential implementation of simulated annealing and gradient optimization has the potential to improve on either method alone, allowing reliable approximation of the global solution followed by rapid refinement. The method is limited to applications where both techniques can be implemented, which eliminates problems with discrete variables.
2346
An Iterative “Step & Shoot” MLC-IMRT Segmentation Algorithm for Continuous Intensity Maps
S.X. Chang, L.D. Potter Dept. of Radiation Oncology, Univ. of North Carolina Medical School, Chapel Hill, NC Purpose: The conversion of the continuous intensity map generated by a dose optimization algorithm to a set of treatment delivery parameters can play a crucial role in the quality and efficiency of the IMRT treatment. The discrete intensity maps (1cm x 1cm) delivered by the “step & shoot” MLC-IMRT technique can lead to results that are of lower quality than those of continuous maps due to lack of resolution. Several hardware solutions have been developed, i. e. introducing 5-mm MLC leaves and treatment table movement. However, this can lead to the reduction of treatment efficiency. We present a software approach to improve the quality and efficiency of the “step & shoot” treatment delivered by the conventional MLC accelerators. Materials and Methods: The continuous intensity maps produced by the in-house TPS PlanUNC using the index-dose gradient optimization algorithm are used for the MLC segmentation. The MLC segments are generated iteratively based on the residual intensity map to be delivered. A base portion (slab) of the map with the optimal height is “sliced” from the map and the appropriate MLC segment field to deliver the intensity slab is calculated. The preferable collimator angle for the segment field is chosen based on two weighted criteria: 1) preservation of the steep gradient portion of the intensity map slice and 2) minimization of the difference between the shape of the slice and that of the MLC segment field. Areas of overdose and underdose can be weighted differently for separate considerations. Once the segment field is determined the intensity map it delivers is calculated using the PlanUNC TPS photon source model. This intensity map delivered by the MLC segment field is then subtracted from the residual intensity map. The above steps are repeated until the residual map is reduced to an acceptable level. Results: The iterative process of the residual intensity map minimization is demonstrated using clinical cases. We often found that for each slice of the intensity map the MLC leaf orientation (collimator angle) that best conforms to the shape of the intensity map slice is different from the collimator angle that best fits the steep gradient regions of the intensity map slice. A weighting method between the two considerations is presented. The dose optimization quality (based on how well the original optimization goals are met) and treatment efficiency of the “step & shoot” treatment generated by this algorithm are compared to those of the continuous maps for clinical cases. A similar comparison is also made with other segmentation methods using the intensity maps from the same dose optimization. Other important segmentation issues will also be discussed. Conclusion: We have developed a new MLC-IM segmentation algorithm for continuous intensity maps. It is able to focus on the regions of the map that are likely to play an important role in achieving the optimization objectives. This algorithm also makes use of the continuous ranges of the collimator angle and the MLC leaf positioning to minimize the impact of the MLC leaf width in IM resolution. We believe this algorithm has the potential to increase the quality of the “step & shoot” IMRT treatment and may also increase the treatment efficiency.
2347
Change in Quality of Life (QOL) and Function within the First Year in Patients Who Received Primary or Post-Operative Radiotherapy for Advanced Stage Head and Neck Cancer
C.K. Gwede, D. Johnson, B. Sauder, H. Divan, A. Trotti Radiation Oncology, University of South Florida, Moffitt Cancer Center, Tampa, FL Purpose: Patients with advanced stage head and neck cancer (HNC) experience significant morbidity and debilitating effects related to disease and treatment. The areas of dysfunction are well documented, but the temporal patterns of change during the first year after treatment are not well described. Understanding the trajectories of change in overall QOL and individual areas of function may facilitate the management of patients. Thus, we report the patterns of change in QOL in patients who received primary or post-operative radiotherapy.