Current and future accelerator technologies for charged particle therapy

Owen, Hywel; Lomax, Anthony; Jolly, S.

doi:10.1016/j.nima.2015.08.038

Cited by 39 publications

(16 citation statements)

References 118 publications

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“…The R 2 -parameter of this fit is 0.999976. Figure 3 shows the A(E) data points together with the above described fitting function given by (3). It can be seen that the fitting model adopted from [14] fits extremely well our data.…”

Section: Energy -Scalingmentioning

confidence: 55%

“…2 changed to the fitting parameters according to Tab. 4 containing the fitting parameters calculated according to (3) and the average value of the B-exponent. We have repeated the fit-quality test in the same manner as described in the previous subsection using the fitting parameters from Tab.…”

Section: Energy -Scalingmentioning

confidence: 99%

“…Its application requires technological development in the field of accelerator technology [1][2][3], beam transport [4][5][6][7], radiobiology, dosimetry, treatment planning, etc. Many of these development fields are based on the physics of ion interaction with matter, which is tightly connected to the precise knowledge of ion ranges in different biological targets.…”

Section: Introductionmentioning

confidence: 99%

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Ranges of protons in biological targets

Pavlovič

Hammerle

2017

Journal of Electrical Engineering

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The paper introduces a simple fitting function for quick assessment of proton ranges in biological targets and human tissues. The function has been found by fitting an extensive data set of Monte Carlo proton ranges obtained with the aid of the SRIM-2013 code. The data has been collected for 28 different targets at 8 energies in the interval from 60 MeV to 220 MeV. The paper shows that at a given kinetic proton-beam energy, the Monte Carlo ranges can be satisfactorily fitted by a power function that depends solely on the target density. This is a great advantage for targets, for which the exact chemical composition is not known, or the mean ionizing potential is not reliably known. The satisfactory fit is meant as the fit that stays within the natural range straggling of the Monte Carlo ranges. In the second step, the energy-scaling yielding a universal fitting formula for proton ranges as a function of proton-beam energy and target density is introduced and discussed. This paper is an extended version of the contribution presented at the APCOM2017 conference.K e y w o r d s: biological targets, proton range, proton therapy, range straggling, SRIM

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Section: Energy -Scalingmentioning

confidence: 55%

Section: Energy -Scalingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ranges of protons in biological targets

Pavlovič

Hammerle

2017

Journal of Electrical Engineering

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“…Depending on the beam angle, existing accelerators with beam ranges of approximately 30 cm H 2 O‐equivalent should be sufficient for this purpose. Also, some centers develop solutions to extend the energy range of their systems to enable particle imaging . An interesting option would be to mix ions of the same magnetic stiffness, such as 12 C and 4 He, where the He would have a considerably higher beam range than C. Therefore, the beam energy would be adjusted so that treatment with C Bragg Peaks are performed, while He ions pass through the patient and are continuously detected for range verification.…”

Section: Requirements For a Clinical Applicationmentioning

confidence: 99%

Noninvasive cardiac arrhythmia ablation with particle beams

Graeff

Bert

2018

Medical Physics

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Cardiac arrhythmias are a major health burden, associated with reduced quality of life and substantial morbidity and mortality. Current therapy includes moderately effective medication and catheter‐based ablation of arrhythmogenic substrates in the heart. Catheter interventions frequently have to be repeated due to recurrent arrhythmia, can have rare but severe side‐effects and are less suited especially for potentially lethal left ventricular tachycardia. Noninvasive alternatives are therefore warranted. Photon and ion beam radiotherapy has been studied in animal models and first patient cases have been reported using photons. Ion beams might offer the possibility to greatly reduce dose to surrounding healthy tissue, including critical cardiac substructures. Based on a recently conducted animal study, we report advantages and disadvantages of 4D‐ion beam therapy, and strategies necessary for a clinical transition. Motion management of both respiration and heartbeat are discussed, as well as range uncertainty resulting from both regular motion and interfractional anatomic changes. Image guidance both in 3D and 4D has to be employed for a safe irradiation, but also population‐based data on motion variability and time behavior of interfractional changes are necessary. Range verification could play a crucial role at least during development of clinical protocols. For clinical realization, it appears necessary to suppress or conformally mitigate the large respiratory motion to avoid normal tissue complications. Cardiac motion has to be incorporated into treatment planning, either through adequate range‐considering internal margins or through more conformal strategies such as ECG‐based gating or even 4D‐optimization. The latter strategies would necessitate online 4D image guidance.

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“…On the other hand, heavier ions suffer less from MCS due to smaller average angular deviations and are viable candidates to acquire high-quality tomographic images. However, more sophisticated accelerators are required to produce a beam of heavier particles with a minimal energy to cross a clinically relevant distance (Pedroni, 1993;Lomax, 2009;Kitagawa et al, 2010;Owen et al, 2016). Moreover, as of now no trajectory estimate has been proposed to extract the ions MLP.…”

Section: Introductionmentioning

confidence: 99%

A theoretical framework to predict the most likely ion path in particle imaging

et al. 2017

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In this work, a generic rigorous Bayesian formalism is introduced to predict the most likely path of any ion crossing a medium between two detection points. The path is predicted based on a combination of the particle scattering in the material and measurements of its initial and final position, direction and energy. The path estimate's precision is compared to the Monte Carlo simulated path. Every ion from hydrogen to carbon is simulated in two scenarios to estimate the accuracy achievable: one where the range is fixed and one where the initial velocity is fixed. In the scenario where the range is kept constant, the maximal root-mean-square error between the estimated path and the Monte Carlo path drops significantly between the proton path estimate (0.50 mm) and the helium path estimate (0.18 mm), but less so up to the carbon path estimate (0.09 mm). In the scenario where the initial velocity is kept constant, helium have systematically the minimal root-mean-square error throughout the path. As a result, helium is found to be the optimal particle for ion imaging.

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Current and future accelerator technologies for charged particle therapy

Cited by 39 publications

References 118 publications

Ranges of protons in biological targets

Ranges of protons in biological targets

Noninvasive cardiac arrhythmia ablation with particle beams

A theoretical framework to predict the most likely ion path in particle imaging

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