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At least 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
NASA NTRS2004

Verifying AI Plan Models: Even the Best Laid Plans Need to be Verified

This viewgraph presentation reviews work on model checking, and specifically the SPIN model checker. The goal of this work is to retire a significant class of risks associated with the use of Artificial Intelligence (Al) Planners on Missions. This effort must provide tangible testing results to a mission using Al technology. It is hoped that the work should be possible to leverage the technique and tools throughout NASA

artificial intelligence↗
DOE OSTI2015

Noncoplanar VMAT for nasopharyngeal tumors: Plan quality versus treatment time

Purpose: The authors investigated the potential of optimized noncoplanar irradiation trajectories for volumetric modulated arc therapy (VMAT) treatments of nasopharyngeal patients and studied the trade-off between treatment plan quality and delivery time in radiation therapy. Methods: For three nasopharyngeal patients, the authors generated treatment plans for nine different delivery scenarios using dedicated optimization methods. They compared these scenarios according to dose characteristics, number of beam directions, and estimated delivery times. In particular, the authors generated the following treatment plans: (1) a 4π plan, which is a not sequenced, fluence optimized plan that uses beam directions from approximately 1400 noncoplanar directions and marks a theoretical upper limit of the treatment plan quality, (2) a coplanar 2π plan with 72 coplanar beam directions as pendant to the noncoplanar 4π plan, (3) a coplanar VMAT plan, (4) a coplanar step and shoot (SnS) plan, (5) a beam angle optimized (BAO) coplanar SnS IMRT plan, (6) a noncoplanar BAO SnS plan, (7) a VMAT plan with rotated treatment couch, (8) a noncoplanar VMAT plan with an optimized great circle around the patient, and (9) a noncoplanar BAO VMAT plan with an arbitrary trajectory around the patient. Results: VMAT using optimized noncoplanar irradiation trajectories reduced the mean and maximum doses in organs at risk compared to coplanar VMAT plans by 19% on average while the target coverage remains constant. A coplanar BAO SnS plan was superior to coplanar SnS or VMAT; however, noncoplanar plans like a noncoplanar BAO SnS plan or noncoplanar VMAT yielded a better plan quality than the best coplanar 2π plan. The treatment plan quality of VMAT plans depended on the length of the trajectory. The delivery times of noncoplanar VMAT plans were estimated to be 6.5 min in average; 1.6 min longer than a coplanar plan but on average 2.8 min faster than a noncoplanar SnS plan with comparable treatment plan quality. Conclusions: The authors’ study reconfirms the dosimetric benefits of noncoplanar irradiation of nasopharyngeal tumors. Both SnS using optimized noncoplanar beam ensembles and VMAT using an optimized, arbitrary, noncoplanar trajectory enabled dose reductions in organs at risk compared to coplanar SnS and VMAT. Using great circles or simple couch rotations to implement noncoplanar VMAT, however, was not sufficient to yield meaningful improvements in treatment plan quality. The authors estimate that noncoplanar VMAT using arbitrary optimized irradiation trajectories comes at an increased delivery time compared to coplanar VMAT yet at a decreased delivery time compared to noncoplanar SnS IMRT.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Knowledge-based prediction of plan quality metrics in intracranial stereotactic radiosurgery

Purpose: The objective of this work was to develop a comprehensive knowledge-based methodology for predicting achievable dose–volume histograms (DVHs) and highly precise DVH-based quality metrics (QMs) in stereotactic radiosurgery/radiotherapy (SRS/SRT) plans. Accurate QM estimation can identify suboptimal treatment plans and provide target optimization objectives to standardize and improve treatment planning. Methods: Correlating observed dose as it relates to the geometric relationship of organs-at-risk (OARs) to planning target volumes (PTVs) yields mathematical models to predict achievable DVHs. In SRS, DVH-based QMs such as brain V{sub 10Gy} (volume receiving 10 Gy or more), gradient measure (GM), and conformity index (CI) are used to evaluate plan quality. This study encompasses 223 linear accelerator-based SRS/SRT treatment plans (SRS plans) using volumetric-modulated arc therapy (VMAT), representing 95% of the institution’s VMAT radiosurgery load from the past four and a half years. Unfiltered models that use all available plans for the model training were built for each category with a stratification scheme based on target and OAR characteristics determined emergently through initial modeling process. Model predictive accuracy is measured by the mean and standard deviation of the difference between clinical and predicted QMs, δQM = QM{sub clin} − QM{sub pred}, and a coefficient of determination, R{sup 2}. For categories with a large number of plans, refined models are constructed by automatic elimination of suspected suboptimal plans from the training set. Using the refined model as a presumed achievable standard, potentially suboptimal plans are identified. Predictions of QM improvement are validated via standardized replanning of 20 suspected suboptimal plans based on dosimetric predictions. The significance of the QM improvement is evaluated using the Wilcoxon signed rank test. Results: The most accurate predictions are obtained when plans are stratified based on proximity to OARs and their PTV volume sizes. Volumes are categorized into small (V{sub PTV} < 2 cm{sup 3}), medium (2 cm{sup 3} < V{sub PTV} < 25 cm{sup 3}), and large (25 cm{sup 3} < V{sub PTV}). The unfiltered models demonstrate the ability to predict GMs to ∼1 mm and fractional brain V{sub 10Gy} to ∼25% for plans with large V{sub PTV} and critical OAR involvements. Increased accuracy and precision of QM predictions are obtained when high quality plans are selected for the model training. For the small and medium V{sub PTV} plans without critical OAR involvement, predictive ability was evaluated using the refined model. For training plans, the model predicted GM to an accuracy of 0.2 ± 0.3 mm and fractional brain V{sub 10Gy} to 0.04 ± 0.12, suggesting highly accurate predictive ability. For excluded plans, the average δGM was 1.1 mm and fractional brain V{sub 10Gy} was 0.20. These δQM are significantly greater than those of the model training plans (p < 0.001). For CI, predictions are close to clinical values and no significant difference was observed between the training and excluded plans (p = 0.19). Twenty outliers with δGM > 1.35 mm were identified as potentially suboptimal, and replanning these cases using predicted target objectives demonstrates significant improvements on QMs: on average, 1.1 mm reduction in GM (p < 0.001) and 23% reduction in brain V{sub 10Gy} (p < 0.001). After replanning, the difference of δGM distribution between the 20 replans and the refined model training plans was marginal. Conclusions: The results demonstrate the ability to predict SRS QMs precisely and to identify suboptimal plans. Furthermore, the knowledge-based DVH predictions were directly used as target optimization objectives and allowed a standardized planning process that bettered the clinically approved plans. Full clinical application of this methodology can improve consistency of SRS plan quality in a wide range of PTV volume and proximity to OARs and facilitate automated treatment planning for this critical treatment site.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Preliminary evaluation of multifield and single-field optimization for the treatment planning of spot-scanning proton therapy of head and neck cancer

Purpose: Spot-scanning proton therapy (SSPT) using multifield optimization (MFO) can generate highly conformal dose distributions, but it is more sensitive to setup and range uncertainties than SSPT using single-field optimization (SFO). The authors compared the two optimization methods for the treatment of head and neck cancer with bilateral targets and determined the superior method on the basis of both the plan quality and the plan robustness in the face of setup and range uncertainties.Methods: Four patients with head and neck cancer with bilateral targets who received SSPT treatment in the authors' institution were studied. The patients had each been treated with a MFO plan using three fields. A three-field SFO plan (3F-SFO) and a two-field SFO plan (2F-SFO) with the use of a range shifter in the beam line were retrospectively generated for each patient. The authors compared the plan quality and robustness to uncertainties of the SFO plans with the MFO plans. Robustness analysis of each plan was performed to generate the two dose distributions consisting of the highest and the lowest possible doses (worst-case doses) from the spatial and range perturbations at every voxel. Dosimetric indices from the nominal and worst-case plans were compared.Results: The 3F-SFO plans generally yielded D95 and D5 values in the targets that were similar to those of the MFO plans. 3F-SFO resulted in a lower dose to the oral cavity than MFO in all four patients by an average of 9.9 Gy, but the dose to the two parotids was on average 6.7 Gy higher for 3F-SFO than for MFO. 3F-SFO plans reduced the variations of dosimetric indices under uncertainties in the targets by 22.8% compared to the MFO plans. Variations of dosimetric indices under uncertainties in the organs at risk (OARs) varied between organs and between patients, although they were on average 9.2% less for the 3F-SFO plans than for the MFO plans. Compared with the MFO plans, the 2F-SFO plans showed a reduced dose to the parotids for both the nominal dose and in the worst-case scenario, but the plan robustness in the target of the 2F-SFO plans was not notably greater than that of the MFO plans.Conclusions: Compared with MFO, 3F-SFO improves plan robustness in the targets but degrades dose sparing in the parotids in both the nominal and worst-case scenarios. Although 2F-SFO improves parotid sparing compared with MFO, it produces little improvement in plan robustness. Therefore, considering its tolerable target coverage and sparing of OARs in worst-case scenarios, the authors recommend MFO as the planning method for the treatment of head and neck cancer with bilateral targets.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2017

Evaluation of a Machine-Learning Algorithm for Treatment Planning in Prostate Low-Dose-Rate Brachytherapy

Purpose: This work presents the application of a machine learning (ML) algorithm to automatically generate high-quality, prostate low-dose-rate (LDR) brachytherapy treatment plans. The ML algorithm can mimic characteristics of preoperative treatment plans deemed clinically acceptable by brachytherapists. The planning efficiency, dosimetry, and quality (as assessed by experts) of preoperative plans generated with an ML planning approach was retrospectively evaluated in this study. Methods and Materials: Preimplantation and postimplantation treatment plans were extracted from 100 high-quality LDR treatments and stored within a training database. The ML training algorithm matches similar features from a new LDR case to those within the training database to rapidly obtain an initial seed distribution; plans were then further fine-tuned using stochastic optimization. Preimplantation treatment plans generated by the ML algorithm were compared with brachytherapist (BT) treatment plans in terms of planning time (Wilcoxon rank sum, α = 0.05) and dosimetry (1-way analysis of variance, α = 0.05). Qualitative preimplantation plan quality was evaluated by expert LDR radiation oncologists using a Likert scale questionnaire. Results: The average planning time for the ML approach was 0.84 ± 0.57 minutes, compared with 17.88 ± 8.76 minutes for the expert planner (P=.020). Preimplantation plans were dosimetrically equivalent to the BT plans; the average prostate V150% was 4% lower for ML plans (P=.002), although the difference was not clinically significant. Respondents ranked the ML-generated plans as equivalent to expert BT treatment plans in terms of target coverage, normal tissue avoidance, implant confidence, and the need for plan modifications. Respondents had difficulty differentiating between plans generated by a human or those generated by the ML algorithm. Conclusions: Prostate LDR preimplantation treatment plans that have equivalent quality to plans created by brachytherapists can be rapidly generated using ML. The adoption of ML in the brachytherapy workflow is expected to improve LDR treatment plan uniformity while reducing planning time and resources.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
NASA NTRS2000

ISS Payload Operations: The Need for and Benefit of Responsive Planning

International Space Station (ISS) payload operations are controlled through implementation of a payload operations plan. This plan, which represents the defined approach to payload operations in general, can vary in terms of level of definition. The detailed plan provides the specific sequence and timing of each component of a payload's operations. Such an approach to planning was implemented in the Spacelab program. The responsive plan provides a flexible approach to payload operations through generalization. A responsive approach to planning was implemented in the NASA/Mir Phase 1 program, and was identified as a need during the Skylab program. The current approach to ISS payload operations planning and control tends toward detailed planning, rather than responsive planning. The use of detailed plans provides for the efficient use of limited resources onboard the ISS. It restricts flexibility in payload operations, which is inconsistent with the dynamic nature of the ISS science program, and it restricts crew desires for flexibility and autonomy. Also, detailed planning is manpower intensive. The development and implementation of a responsive plan provides for a more dynamic, more accommodating, and less manpower intensive approach to planning. The science program becomes more dynamic and responsive as the plan provides flexibility to accommodate real-time science accomplishments. Communications limitations and the crew desire for flexibility and autonomy in plan implementation are readily accommodated with responsive planning. Manpower efficiencies are accomplished through a reduction in requirements collection and coordination, plan development, and maintenance. Through examples and assessments, this paper identifies the need to transition from detailed to responsive plans for ISS payload operations. Examples depict specific characteristics of the plans. Assessments identify the following: the means by which responsive plans accommodate the dynamic nature of science programs and the crew desire for flexibility; the means by which responsive plans readily accommodate ISS communications constraints; manpower efficiencies to be achieved through use of responsive plans; and the implications of responsive planning relative to resource utilization efficiency.

Nahay, Ed↗
DOE OSTI2014

A DVH-guided IMRT optimization algorithm for automatic treatment planning and adaptive radiotherapy replanning

Purpose: To develop a novel algorithm that incorporates prior treatment knowledge into intensity modulated radiation therapy optimization to facilitate automatic treatment planning and adaptive radiotherapy (ART) replanning. Methods: The algorithm automatically creates a treatment plan guided by the DVH curves of a reference plan that contains information on the clinician-approved dose-volume trade-offs among different targets/organs and among different portions of a DVH curve for an organ. In ART, the reference plan is the initial plan for the same patient, while for automatic treatment planning the reference plan is selected from a library of clinically approved and delivered plans of previously treated patients with similar medical conditions and geometry. The proposed algorithm employs a voxel-based optimization model and navigates the large voxel-based Pareto surface. The voxel weights are iteratively adjusted to approach a plan that is similar to the reference plan in terms of the DVHs. If the reference plan is feasible but not Pareto optimal, the algorithm generates a Pareto optimal plan with the DVHs better than the reference ones. If the reference plan is too restricting for the new geometry, the algorithm generates a Pareto plan with DVHs close to the reference ones. In both cases, the new plans have similar DVH trade-offs as the reference plans. Results: The algorithm was tested using three patient cases and found to be able to automatically adjust the voxel-weighting factors in order to generate a Pareto plan with similar DVH trade-offs as the reference plan. The algorithm has also been implemented on a GPU for high efficiency. Conclusions: A novel prior-knowledge-based optimization algorithm has been developed that automatically adjust the voxel weights and generate a clinical optimal plan at high efficiency. It is found that the new algorithm can significantly improve the plan quality and planning efficiency in ART replanning and automatic treatment planning.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

MO-B-BRB-00: Optimizing the Treatment Planning Process

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-01: Optimize Treatment Planning Process in Clinical Environment

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↗