Target motion tracking with a scanned particle beam

Bert, Christoph; Saito, N.; Schmidt, Alexander; Chaudhri, Naved; Schardt, D.; Rietzel, Eike

doi:10.1118/1.2815934

Cited by 117 publications

(84 citation statements)

References 15 publications

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“…1,2 The movement of lung tumors during free breathing has become a paradigmatic example of this problem. Compensation strategies currently being employed or under development include temporal gating of the beam, 3,4 repositioning of the linear accelerator, 5,6 adjustment of the collimating aperture, 7,8 electromagnetic steering of a charged particle beam, 9 and compensatory shift of the treatment couch. 10,11 Because no system response can occur instantaneously, all methods must make a future prediction of the tumor position to cover for the lag time between the detection of the tumor position and the compensatory system response.…”

Section: Introductionmentioning

confidence: 99%

Optimization of an adaptive neural network to predict breathing

Murphy

Pokhrel

2008

Medical Physics

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Purpose: To determine the optimal configuration and performance of an adaptive feed forward neural network filter to predict breathing in respiratory motion compensation systems for external beam radiation therapy. Method and Materials: A two-layer feed forward neural network was trained to predict future breathing amplitudes for 27 recorded breathing histories. The prediction intervals ranged from 100 to 500 ms. The optimal sampling frequency, number of input samples, training rate, and number of training epochs were determined for each breathing history and prediction interval. The overall optimal filter configuration was determined from this parameter survey, and its accuracy for each breathing example was compared to the individually optimal filter setups. Prediction accuracy was also compared to breathing stability as measured by the autocorrelation of the breathing signal. Results: The survey of filter configurations converged on a standard setup for all examples of breathing. For 24 of the 27 breathing histories the accuracy of the standard filter for a 300 ms prediction interval was within a few percent of the individually optimized filter setups; for the remaining three histories the standard filter was 5%-15% less accurate. Conclusions: A standard adaptive neural network filter setup can provide approximately optimal breathing prediction for a wide range of breathing patterns. The filter accuracy has a clear correlation with the stability of breathing.

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Section: Introductionmentioning

confidence: 99%

Optimization of an adaptive neural network to predict breathing

Murphy

Pokhrel

2008

Medical Physics

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“…Gating [10][11][12] and tracking techniques [13,14] using external surrogates have also been reported in particle therapy. However, internal organ motions and the external surrogate signals of abdominal motion are not necessarily correlated during treatment [15].…”

Section: Introductionmentioning

confidence: 99%

Optimization and evaluation of multiple gating beam delivery in a synchrotron-based proton beam scanning system using a real-time imaging technique

et al. 2016

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(Naoki Miyamoto) Purpose:To find the optimum parameter of a new beam control function installed in a synchrotron-based proton therapy system. Methods:A function enabling multiple gated irradiation in the flat top phase has been installed in a real-time-image gated proton beam therapy (RGPT) system. This function is realized by a waiting timer that monitors the elapsed time from the last gate-off signal in the flat top phase. The gated irradiation efficiency depends on the timer value, T w . To find the optimum T w value, gated irradiation efficiency was evaluated for each configurable T w value. 271 gate signal data sets from 58 patients were used for the simulation. Results:The highest mean efficiency 0.52 was obtained in T W = 0.2 s. The irradiation efficiency was approximately 21% higher than at T W = 0 s, which corresponds to ordinary synchrotron operation. The irradiation efficiency was improved in 154 (57%) of the 271 cases. The irradiation efficiency was reduced in 117 cases because the T W value was insufficient or the function introduced an unutilized wait time for the next gate-on signal in the flat top phase. In the actual treatment of a patient with a hepatic tumor at T w = 0.2 s, 4.48 GyE irradiation was completed within 250 s. In contrast, the treatment time of ordinary synchrotron operation was estimated to be 420 s. Conclusions:The results suggest that the multiple gated-irradiation function has potential to improve the gated irradiation efficiency and to reduce the treatment time.

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“…Owing to the need to compensate for both CTV motion and the change in radiological path length in proton therapy, the German national heavy-ion physics laboratory, the Gesellschaft fü r Schwerionen, in Darmstadt, has established a method of using a motordriven compensation system for changes in radiological path length [46,47] and raster scanning [45,48].…”

Section: Beam Trackingmentioning

confidence: 99%

Treatment planning optimisation in proton therapy

McGowan¹,

Burnet²,

Lomax³

2013

BJR

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ABSTRACT. The goal of radiotherapy is to achieve uniform target coverage while sparing normal tissue. In proton therapy, the same sources of geometric uncertainty are present as in conventional radiotherapy. However, an important and fundamental difference in proton therapy is that protons have a finite range, highly dependent on the electron density of the material they are traversing, resulting in a steep dose gradient at the distal edge of the Bragg peak. Therefore, an accurate knowledge of the sources and magnitudes of the uncertainties affecting the proton range is essential for producing plans which are robust to these uncertainties. This review describes the current knowledge of the geometric uncertainties and discusses their impact on proton dose plans. The need for patient-specific validation is essential and in cases of complex intensity-modulated proton therapy plans the use of a planning target volume (PTV) may fail to ensure coverage of the target. In cases where a PTV cannot be used, other methods of quantifying plan quality have been investigated. A promising option is to incorporate uncertainties directly into the optimisation algorithm. A further development is the inclusion of robustness into a multicriteria optimisation framework, allowing a multi-objective Pareto optimisation function to balance robustness and conformity. The question remains as to whether adaptive therapy can become an integral part of a proton therapy, to allow re-optimisation during the course of a patient's treatment. The challenge of ensuring that plans are robust to range uncertainties in proton therapy remains, although these methods can provide practical solutions. The ability to create and deliver the ideal treatment plan, where the target volume receives 100% of the prescribed dose and normal tissue receives 0%, is the holy grail of radiation therapy [1]. It is, however, impossible to achieve this perfect balance. Instead, multiple trade-offs are required to achieve a clinically acceptable plan, so the problem becomes one of optimisation. There are many factors that can affect how ''optimised'' a patient's treatment can be. This review focuses on the challenges of proton therapy plan optimisation, particularly in regard to range uncertainties, and how to incorporate them into the plan evaluation and verification process.The nature of proton therapy makes the aim of cure without complications potentially more achievable, owing to the highly localised deposition of dose in the characteristic Bragg peak [2]. This relates predominantly to the ability to deliver high doses of radiation close to normal tissue structures, which would be dose limiting in conventional X-ray treatments, and to the finite range of protons, which results in a reduced integral dose to surrounding normal tissues.From a clinical perspective, the exact role of proton therapy has yet to be defined. However, for childhood cancers, proton therapy delivers a lower dose to tissues around the tumour than X-rays, resulting in less growth disturbance and l...

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Target motion tracking with a scanned particle beam

Cited by 117 publications

References 15 publications

Optimization of an adaptive neural network to predict breathing

Optimization of an adaptive neural network to predict breathing

Optimization and evaluation of multiple gating beam delivery in a synchrotron-based proton beam scanning system using a real-time imaging technique

Treatment planning optimisation in proton therapy

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