Incorporation of delivery times in stereotactic radiosurgery treatment optimization

Ghaffari, Hamid R.; Aleman, Dionne M.; Jaffray, David A.; Ruschin, Mark

doi:10.1007/s10898-017-0527-8

Cited by 9 publications

(19 citation statements)

References 8 publications

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“…The irradiation times are converted into deliverable shots after a solution has been found. Despite its promises, methods using sector‐duration optimization has, so far, lacked an efficient way of controlling BOTs and have been too computationally costly for widespread clinical use . This is about to change.…”

Section: Introductionmentioning

confidence: 99%

A linear programming approach to inverse planning in Gamma Knife radiosurgery

et al. 2019

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PurposeLeksell Gamma Knife® is a stereotactic radiosurgery system that allows fine‐grained control of the delivered dose distribution. We describe a new inverse planning approach that both resolves shortcomings of earlier approaches and unlocks new capabilities.MethodsWe fix the isocenter positions and perform sector‐duration optimization using linear programming, and study the effect of beam‐on time penalization on the trade‐off between beam‐on time and plan quality. We also describe two techniques that reduce the problem size and thus further reduce the solution time: dualization and representative subsampling.ResultsThe beam‐on time penalization reduces the beam‐on time by a factor 2–3 compared with the naïve alternative. Dualization and representative subsampling each leads to optimization time‐savings by a factor 5–20. Overall, we find in a comparison with 75 clinical plans that we can always find plans with similar coverage and better selectivity and beam‐on time. In 44 of these, we can even find a plan that also has better gradient index. On a standard GammaPlan workstation, the optimization times ranged from 2.3 to 26 s with a median time of 5.7 s.ConclusionWe present a combination of techniques that enables sector‐duration optimization in a clinically feasible time frame.

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Section: Introductionmentioning

confidence: 99%

A linear programming approach to inverse planning in Gamma Knife radiosurgery

et al. 2019

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“…It is also important to note, since the treatment planning is done in two steps (isocenter selection and SDO), it is possible to remedy some of the errors made in the isocenter selection phase through SDO as indicated in previous studies. 9,16 Another limitation of the present work is the scarcity of the available data and its homogeneity. That is, the entire dataset comes from single institution and consists mainly of brain metastases cases.…”

Section: Discussionmentioning

confidence: 97%

“…It has been shown that such models for SDO yield plans with reasonable treatment times and are clinically acceptable provided that isocenters are appropriately chosen. 6,9,10 The existing automated isocenter selection methods can be categorized into heuristic and optimization-based approaches. The skeletonization method 3,5,11 is one of the existing heuristic isocenter selection methods that aims to extract the so called object's skeleton, a simplified representation that preserves morphology of the structure and then places isocenters on its joints and the endpoints.…”

Section: Introductionmentioning

confidence: 99%

“…Some other studies attempt to simultaneously optimize isocenter locations and sector durations. 9,16 The proposed methods involve selecting a subset of the isocenters from the candidate set by solving a mixed-integer linear program (MILP). Since MILPs are generally NP-hard, this approach is limited in terms of the size of the candidate set.…”

Section: Introductionmentioning

confidence: 99%

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Knowledge‐based isocenter selection in radiosurgery planning

et al. 2020

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We present a new method for knowledge-based isocenter selection for treatment planning in radiosurgery. Our objective is to develop a prediction model that can learn from past manually designed treatment plans. We leverage recent advances in deep learning to predict isocenter locations in treatment plans in order to provide a decision support tool. Methods: The proposed method adapts a geometric approach using orthogonal moment expansions as a feature vector for describing the shape of the tumor. Our approach accounts primarily for tumor shape and OAR proximity, the two factors that are known to greatly affect the isocenter placement. We solve the prediction problem by training a residual neural network with skip connections on the formed shape descriptors. Our network was trained on 533 patient cases and was validated on a set of out-of-sample cases. Results: Our method generates heatmap predictions for isocenter locations that are in most cases comparable to the experienced human planners, which shows that the method can be used in treatment planning to guide the users for determining the isocenters. Conclusions: Our numerical experiments indicate a positive predictive value on an independent validation set when compared against a test dataset that was not seen by the model during training.

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“…The literature on inverse planning for LGK is limited and only Ghaffari et al (2017) consider the SDIO problem. In their limited numerical analyses, Ghaffari et al (2017) show that low BOT values can be achieved while maintaining the quality of the plans by imposing limits on the minimum and maximum irradiation times of the selected isocenters for each sector and sector size. In our study, we directly incorporate the BOT function to the SDIO model, and we use big-M type constraints to ensure that the radiation delivery is only possible through selected isocenters.…”

Section: Discussionmentioning

confidence: 99%

Simultaneous optimization of isocenter locations and sector duration in radiosurgery

et al. 2019

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Stereotactic radiosurgery is an effective technique to treat brain tumors for which several inverse planning methods may be appropriate. We propose an integer programming model to simultaneous sector duration and isocenter optimization (SDIO) problem for Leksell Gamma Knife R Icon TM (Elekta, Stockholm, Sweden) to tractably incorporate treatment time. We devise a Benders decomposition scheme to solve the SDIO problem to optimality. The performances of our approaches are assessed using anonymized data from eight previously treated cases, and obtained treatment plans are compared against each other and against the clinical plans. The plans generated by our SDIO model all meet or exceed clinical guidelines while demonstrating high conformity.arXiv:1807.02607v1 [physics.med-ph] 7 Jul 2018 IntroductionStereotactic radiosurgery (SRS) is an effective method for treatment of brain tumors, vascular malformations, and other conditions such as tremors. During SRS, a large amount of radiation is delivered to tumors through the intact skull without damaging the surrounding normal brain tissue.SRS aims to obtain highly conformal dose distributions so that dose is delivered to target structures with high precision while sparing the surrounding healthy tissues, thus increasing the patient's survival chance and improving the quality of life after the treatment. One advanced SRS system is Leksell Gamma Knife R (LGK) (Elekta, Stockholm, Sweden) Icon TM . Icon TM has a single collimator with eight detached sectors that can either be robotically driven to three different collimator sizes (4, 8, and 16 mm) or be blocked. During SRS, the patient lies on a couch and radiation is emitted by 192 cobalt-60 sources to the targets where the intersection point of all the beams is called the isocenter. The radiation delivery follows a step-and-shoot manner, i.e., the couch is stationary during delivery of radiation at each isocenter location, and only moves when the beam is blocked. To fully leverage the robotic capability of the delivery system, an automated approach for planning is required. The clinical goals for such an automated approach are: (1) satisfy or exceed the dosimetric clinical objectives (e.g., coverage, conformality, healthy tissue dose); (2) achieve (1) with minimal irradiation time (i.e., beam-on time); and (3) achieve (1) and (2) with a plan as efficient as possible, including minimizing the number of isocenters and shots required.Most of the current literature has separated the task of isocenter placement from the task of sector duration optimization (SDO). In SDO, as in fluence map optimization in Intensity Modulated Radiation Therapy, the aim is to find the intensities (or irradiation times) of each beamlet in a fixed set of beams. Previous studies on SDO mainly focus on variants of convex quadratic penalty models (Oskoorouchi et al., 2011;Ghobadi et al., 2012;Ghaffari, 2012). In order to determine the isocenter locations, Ghobadi et al. (2012) propose an algorithm that combines the well-known

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Incorporation of delivery times in stereotactic radiosurgery treatment optimization

Cited by 9 publications

References 8 publications

A linear programming approach to inverse planning in Gamma Knife radiosurgery

A linear programming approach to inverse planning in Gamma Knife radiosurgery

Knowledge‐based isocenter selection in radiosurgery planning

Simultaneous optimization of isocenter locations and sector duration in radiosurgery

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