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Title: Reduction of IMRT beam complexity through the use of beam modulation penalties in the objective function

Abstract

Inverse planned intensity modulated radiation therapy (IMRT) has become commonplace in treatment centers across the world. Due to the implications of beam complexity on treatment planning, delivery, and quality assurance, several methods have been proposed to reduce the complexity. These methods include beamlet intensity restrictions, smoothing procedures, and direct aperture optimization. Many of these methods typically sacrifice target coverage and/or normal tissue sparing in return for increased beam smoothness and delivery efficiency. In the present work, we penalize beam modulation in the inverse planning cost function to reduce beam complexity and increase delivery efficiency, while maintaining dosimetric quality. Three modulation penalties were tested: two that penalized deviation from Savitzky-Golay filtered versions of the optimized beams, and one that penalized the plan intensity map variation (a measure of overall beam modulation). The modulation penalties were applied at varying weights in a weighted sum objective (or cost) function to investigate their ability to reduce beam complexity while preserving IMRT plan quality. The behavior of the penalties was characterized on a CT phantom, and then clinical optimization comparisons were performed in the brain, prostate, and head/neck. Comparisons were made between (i) plans with a baseline cost function (ii) plans with a baseline costmore » function employing maximum beamlet intensity limits, and (iii) plans with each of the modulation penalties added to the baseline cost function. Plan analysis was based upon dose-volume histograms, relevant dose metrics, beam modulation, and monitor units required for step and shoot delivery. Each of the techniques yielded improvements over a baseline cost function in terms of MU reduction. In most cases, this was achieved with minimal change to the plan DVHs and metrics. In all cases, an acceptable plan was reached with each of the methods while reducing MU substantially. Each individual method has merit as a tool for reducing IMRT beam complexity and could be easily applied in the clinic to improve overall inverse plan quality. However, the penalty based upon the plan intensity map variation consistently produced the most delivery-efficient plans with the fewest computations.« less

Authors:
; ;  [1];  [2];  [2]
  1. Department of Radiation Oncology and Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, Michigan 48109 (United States)
  2. (United States)
Publication Date:
OSTI Identifier:
20951045
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 34; Journal Issue: 2; Other Information: DOI: 10.1118/1.2409749; (c) 2007 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
62 RADIOLOGY AND NUCLEAR MEDICINE; APERTURES; BEAMS; BRAIN; COMPUTERIZED TOMOGRAPHY; DOSIMETRY; METRICS; MODULATION; NECK; OPTIMIZATION; PHANTOMS; PLANNING; PROSTATE; QUALITY ASSURANCE; RADIATION DOSES; RADIOTHERAPY

Citation Formats

Matuszak, Martha M., Larsen, Edward W., Fraass, Benedick A., Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, Michigan 48109, and Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan 48109. Reduction of IMRT beam complexity through the use of beam modulation penalties in the objective function. United States: N. p., 2007. Web. doi:10.1118/1.2409749.
Matuszak, Martha M., Larsen, Edward W., Fraass, Benedick A., Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, Michigan 48109, & Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan 48109. Reduction of IMRT beam complexity through the use of beam modulation penalties in the objective function. United States. doi:10.1118/1.2409749.
Matuszak, Martha M., Larsen, Edward W., Fraass, Benedick A., Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, Michigan 48109, and Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan 48109. Thu . "Reduction of IMRT beam complexity through the use of beam modulation penalties in the objective function". United States. doi:10.1118/1.2409749.
@article{osti_20951045,
title = {Reduction of IMRT beam complexity through the use of beam modulation penalties in the objective function},
author = {Matuszak, Martha M. and Larsen, Edward W. and Fraass, Benedick A. and Department of Nuclear Engineering and Radiological Sciences, University of Michigan, Ann Arbor, Michigan 48109 and Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan 48109},
abstractNote = {Inverse planned intensity modulated radiation therapy (IMRT) has become commonplace in treatment centers across the world. Due to the implications of beam complexity on treatment planning, delivery, and quality assurance, several methods have been proposed to reduce the complexity. These methods include beamlet intensity restrictions, smoothing procedures, and direct aperture optimization. Many of these methods typically sacrifice target coverage and/or normal tissue sparing in return for increased beam smoothness and delivery efficiency. In the present work, we penalize beam modulation in the inverse planning cost function to reduce beam complexity and increase delivery efficiency, while maintaining dosimetric quality. Three modulation penalties were tested: two that penalized deviation from Savitzky-Golay filtered versions of the optimized beams, and one that penalized the plan intensity map variation (a measure of overall beam modulation). The modulation penalties were applied at varying weights in a weighted sum objective (or cost) function to investigate their ability to reduce beam complexity while preserving IMRT plan quality. The behavior of the penalties was characterized on a CT phantom, and then clinical optimization comparisons were performed in the brain, prostate, and head/neck. Comparisons were made between (i) plans with a baseline cost function (ii) plans with a baseline cost function employing maximum beamlet intensity limits, and (iii) plans with each of the modulation penalties added to the baseline cost function. Plan analysis was based upon dose-volume histograms, relevant dose metrics, beam modulation, and monitor units required for step and shoot delivery. Each of the techniques yielded improvements over a baseline cost function in terms of MU reduction. In most cases, this was achieved with minimal change to the plan DVHs and metrics. In all cases, an acceptable plan was reached with each of the methods while reducing MU substantially. Each individual method has merit as a tool for reducing IMRT beam complexity and could be easily applied in the clinic to improve overall inverse plan quality. However, the penalty based upon the plan intensity map variation consistently produced the most delivery-efficient plans with the fewest computations.},
doi = {10.1118/1.2409749},
journal = {Medical Physics},
number = 2,
volume = 34,
place = {United States},
year = {Thu Feb 15 00:00:00 EST 2007},
month = {Thu Feb 15 00:00:00 EST 2007}
}
  • Purpose: To estimate the dose distributions delivered to the patient in each treatment fraction using deformable image registration (DIR) and assess the radiobiological impact of the inter-fraction variations due to patient deformation and setup. Methods: The work is based on the cone beam CT (CBCT) images and treatment plans of two lung cancer patients. Both patients were treated with intensity modulated radiation therapy (IMRT) to 66Gy in 2Gy/fraction. The treatment plans were exported from the treatment planning system (TPS) to the Velocity AI where DIR was performed and the same deformation matrix was used for the deformation of the plannedmore » dose distribution and organ contours to each CBCT dataset. A radiobiological analysis was performed based on the radiobiological parameters of the involved organs at risk (OARs) and planning target volume (PTV). Using the complication free tumor control probability (P+) index, differences in P+ were observed between each CBCT as well as between CBCT and planning dose distributions. Results: The optimal CBCT P? values ranged from 91.6 % to 94.8 % for patient #1 and from 88.8 % to 90.6 % for patient #2. At the dose level of the clinical prescription, the CBCT P+ values ranged from 80.3% to 80.7% for patient #1 and from 80.7% to 81.0% for the patient #2. The planning CT P+ values were 81.0% and 80.7% for the two patients, respectively. These differences emphasize the significance of using the radiobiological analysis when assessing changes in the dose distribution due to the tumor motion and lung deformations. Conclusion: Daily setup variations yield to differences in the actual dose delivered versus the planned one. The observed differences were rather small when only looking at the dosimetric comparison of the dose distributions, however the radiobiology analysis was able to detect clinically relevant differences among the studied dose distributions.« less
  • Optimizing intensity-modulated radiotherapy (IMRT) plans involves tradeoffs that balance normal-tissue objectives against each other and against tumor objectives. Adjusting the parameters that determine the appropriate contributions of individual anatomic structures to the objective functions through trial and error is time consuming and may not produce the best achievable plans. We have developed a sensitivity-guided parameter optimization (SGPO) method to assist in the automatic determination of parameters to drive the IMRT optimization to better achieve, or even exceed, specified planning goals. The method is based on the trade-off relationships among multiple objectives: In a globally optimal plan (or within a convexmore » subspace of the plan objectives), any attempt to improve the achievement of goals for a structure will result in sacrificing the goals for at least one other structure. However, different objectives may have different sensitivities to the overall goal of an IMRT plan. For instance, changes in dose distribution, hence the subscore corresponding to an objective for a given normal structure, may minimally impact the target dose distribution. Stated differently, the target coverage is insensitive to the changes in dose distribution of the specific normal structure. A lung cancer treatment plan designed with the SGPO method was used to demonstrate that IMRT plans could be designed to favor a structure with the highest target sensitivity and spare the structures with the least target sensitivity without compromising the target coverage. Using one case each of prostate and paranasal sinus cancers, we also demonstrated that several alternative optimal solutions could be designed with the SGPO algorithm favoring different structures. Finally, we applied the method to eight oropharyngeal cancer cases to obtain objective function parameters that satisfied the Radiation Therapy Oncology Group RTOG-H-0022 protocol. The eight plans optimized using the computer-generated objective function parameters met the protocol's scoring criteria with no or only minor protocol violations. Our preliminary study indicates that the SGPO method may be an effective and practical way to improve IMRT planning.« less
  • Purpose: Intensity-modulated radiation therapy (IMRT) treatment planning typically combines multiple criteria into a single objective function by taking a weighted sum. The authors propose a statistical model that predicts objective function weights from patient anatomy for prostate IMRT treatment planning. This study provides a proof of concept for geometry-driven weight determination. Methods: A previously developed inverse optimization method (IOM) was used to generate optimal objective function weights for 24 patients using their historical treatment plans (i.e., dose distributions). These IOM weights were around 1% for each of the femoral heads, while bladder and rectum weights varied greatly between patients. Amore » regression model was developed to predict a patient's rectum weight using the ratio of the overlap volume of the rectum and bladder with the planning target volume at a 1 cm expansion as the independent variable. The femoral head weights were fixed to 1% each and the bladder weight was calculated as one minus the rectum and femoral head weights. The model was validated using leave-one-out cross validation. Objective values and dose distributions generated through inverse planning using the predicted weights were compared to those generated using the original IOM weights, as well as an average of the IOM weights across all patients. Results: The IOM weight vectors were on average six times closer to the predicted weight vectors than to the average weight vector, usingl{sub 2} distance. Likewise, the bladder and rectum objective values achieved by the predicted weights were more similar to the objective values achieved by the IOM weights. The difference in objective value performance between the predicted and average weights was statistically significant according to a one-sided sign test. For all patients, the difference in rectum V54.3 Gy, rectum V70.0 Gy, bladder V54.3 Gy, and bladder V70.0 Gy values between the dose distributions generated by the predicted weights and IOM weights was less than 5 percentage points. Similarly, the difference in femoral head V54.3 Gy values between the two dose distributions was less than 5 percentage points for all but one patient. Conclusions: This study demonstrates a proof of concept that patient anatomy can be used to predict appropriate objective function weights for treatment planning. In the long term, such geometry-driven weights may serve as a starting point for iterative treatment plan design or may provide information about the most clinically relevant region of the Pareto surface to explore.« less
  • Purpose: Intensity-modulated radiation therapy (IMRT) treatment planning typically combines multiple criteria into a single objective function by taking a weighted sum. The authors propose a statistical model that predicts objective function weights from patient anatomy for prostate IMRT treatment planning. This study provides a proof of concept for geometry-driven weight determination. Methods: A previously developed inverse optimization method (IOM) was used to generate optimal objective function weights for 24 patients using their historical treatment plans (i.e., dose distributions). These IOM weights were around 1% for each of the femoral heads, while bladder and rectum weights varied greatly between patients. Amore » regression model was developed to predict a patient's rectum weight using the ratio of the overlap volume of the rectum and bladder with the planning target volume at a 1 cm expansion as the independent variable. The femoral head weights were fixed to 1% each and the bladder weight was calculated as one minus the rectum and femoral head weights. The model was validated using leave-one-out cross validation. Objective values and dose distributions generated through inverse planning using the predicted weights were compared to those generated using the original IOM weights, as well as an average of the IOM weights across all patients. Results: The IOM weight vectors were on average six times closer to the predicted weight vectors than to the average weight vector, usingl{sub 2} distance. Likewise, the bladder and rectum objective values achieved by the predicted weights were more similar to the objective values achieved by the IOM weights. The difference in objective value performance between the predicted and average weights was statistically significant according to a one-sided sign test. For all patients, the difference in rectum V54.3 Gy, rectum V70.0 Gy, bladder V54.3 Gy, and bladder V70.0 Gy values between the dose distributions generated by the predicted weights and IOM weights was less than 5 percentage points. Similarly, the difference in femoral head V54.3 Gy values between the two dose distributions was less than 5 percentage points for all but one patient. Conclusions: This study demonstrates a proof of concept that patient anatomy can be used to predict appropriate objective function weights for treatment planning. In the long term, such geometry-driven weights may serve as a starting point for iterative treatment plan design or may provide information about the most clinically relevant region of the Pareto surface to explore.« less
  • Purpose: To develop a statistical model that predicts optimization objective function weights from patient geometry for intensity-modulation radiotherapy (IMRT) of prostate cancer. Methods: A previously developed inverse optimization method (IOM) is applied retrospectively to determine optimal weights for 51 treated patients. We use an overlap volume ratio (OVR) of bladder and rectum for different PTV expansions in order to quantify patient geometry in explanatory variables. Using the optimal weights as ground truth, we develop and train a logistic regression (LR) model to predict the rectum weight and thus the bladder weight. Post hoc, we fix the weights of the leftmore » femoral head, right femoral head, and an artificial structure that encourages conformity to the population average while normalizing the bladder and rectum weights accordingly. The population average of objective function weights is used for comparison. Results: The OVR at 0.7cm was found to be the most predictive of the rectum weights. The LR model performance is statistically significant when compared to the population average over a range of clinical metrics including bladder/rectum V53Gy, bladder/rectum V70Gy, and mean voxel dose to the bladder, rectum, CTV, and PTV. On average, the LR model predicted bladder and rectum weights that are both 63% closer to the optimal weights compared to the population average. The treatment plans resulting from the LR weights have, on average, a rectum V70Gy that is 35% closer to the clinical plan and a bladder V70Gy that is 43% closer. Similar results are seen for bladder V54Gy and rectum V54Gy. Conclusion: Statistical modelling from patient anatomy can be used to determine objective function weights in IMRT for prostate cancer. Our method allows the treatment planners to begin the personalization process from an informed starting point, which may lead to more consistent clinical plans and reduce overall planning time.« less