High resolution Cerenkov light imaging of induced positron distribution in proton therapy

Yamamoto, Seiichi; Toshito, T.; Fujii, Keisuke; Morishita, Yuki; Okumura, Satoshi; Komori, Masataka

doi:10.1118/1.4898592

Cited by 17 publications

(18 citation statements)

References 52 publications

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“…The most used imaging systems belong to the IVIS series [1–3, 11, 12, 22, 24, 32, 36, 53–58], but also custom-built systems have been employed [13–15, 17, 18, 21, 23, 59–62]. While IVIS imaging systems have been utilized mainly for in vitro and small animal imaging, custom systems have been built, featuring either intensified CCD cameras (ICCD) or electron-multiplying ones (EMCCD), for microscopic imaging, for phantom studies to test the feasibility of CLI applications and also for a few clinical human studies.…”

Section: Reviewmentioning

confidence: 99%

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Cerenkov luminescence imaging: physics principles and potential applications in biomedical sciences

Ciarrocchi

Belcari

2017

EJNMMI Phys

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Cerenkov luminescence imaging (CLI) is a novel imaging modality to study charged particles with optical methods by detecting the Cerenkov luminescence produced in tissue. This paper first describes the physical processes that govern the production and transport in tissue of Cerenkov luminescence. The detectors used for CLI and their most relevant specifications to optimize the acquisition of the Cerenkov signal are then presented, and CLI is compared with the other optical imaging modalities sharing the same data acquisition and processing methods. Finally, the scientific work related to CLI and the applications for which CLI has been proposed are reviewed. The paper ends with some considerations about further perspectives for this novel imaging modality.

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Section: Reviewmentioning

confidence: 99%

“…In proton therapy, the high inherent spatial resolution of CLI compared to PET has been used to monitor the proton range by imaging the proton-induced positron distribution [17]. In external therapy, CLI has been utilized also for patient positioning and movement tracking [18].…”

Section: Introductionmentioning

confidence: 99%

Cerenkov luminescence imaging: physics principles and potential applications in biomedical sciences

Ciarrocchi

Belcari

2017

EJNMMI Phys

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“…The rational is that the spatial distribution of secondary signals correlates with both dose distribution and proton range. A number of methods have been explored, including positron radioisotope, prompt gamma (PG), acoustic wave, and Cherenkov photons . As a promising tool, Positron Emission Tomography (PET) was extensively studied where the distribution of positron emitters (e.g., 11 C, 15 O) produced can be used to obtain dose distribution.…”

Section: Introductionmentioning

confidence: 99%

Technical Note: Machine learning approaches for range and dose verification in proton therapy using proton‐induced positron emitters

Wang

et al. 2019

Medical Physics

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Purpose/objective(s) Online proton range/dose verification based on measurements of proton‐induced positron emitters is a promising strategy for quality assurance in proton therapy. Because of the nonlinear correlation between the dose distribution and the activity distribution of positron emitters in addition to the presence of noise, machine learning approaches were proposed to establish their relationship. Materials/methods Simulations were carried out with a spot‐scanning proton system using GATE‐8.0 and Geant4‐10.3 toolkit with a computed tomography (CT)‐based patient phantom. The one‐dimensional (1D) distributions of positron emitters and radiation dose were obtained. A feedforward neural network classification model comprising two hidden layers, was developed to estimate whether the range is within a preset threshold. A recurrent neural network (RNN) regression model comprising three layers and ten neurons in each hidden layer was developed to estimate dose distribution. The performance was quantitatively studied in terms of mean squared error (MSE) and mean absolute error (MAE) under different signal‐to‐noise ratio (SNR) values. Results The feasibility of proton range and dose verification using the proposed neural network framework was demonstrated. The feedforward NN model achieves high classification accuracy close to 100% for individual classes without bias. The RNN model is able to accurately predict the 1D dose distribution for different energies and irradiation positions. When the SNR of the input activity profiles is above 4, the framework is able to predict with an MAE of ~0.60 mm and an MSE of ~0.066. Moreover, the model demonstrates a good capability of generalization. Conclusions The RNN model is found to be effective in identifying the relationship between the distributions of dose and positron emitters. The machine learning‐based framework and RNN models may be a useful tool to allow for accurate online range and dose verification based on proton‐induced positron emitters.

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“…Using an array of ion chambers can reduce the scanning time compared to a single-element ionization chamber, but the position accuracy is hindered by the limited number of chambers in an array. Ion-beam induced positrons can be measured by positron emission tomography (PET) or similar methods, [5][6][7][8][9][10][11][12][13] but these approaches fail to adequately reflect dose distribution, and a washout effect can blur the distribution. 14,15 Prompt gamma detection during ion-beam irradiation 16,17 is another candidate solution, but this method faces difficulty in obtaining high-resolution images.…”

Section: Introductionmentioning

confidence: 99%

Luminescence imaging of water during carbon-ion irradiation for range estimation

et al. 2016

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High resolution Cerenkov light imaging of induced positron distribution in proton therapy

Abstract: High resolution Cerenkov light imaging of proton-induced positron distribution was possible. The authors conclude that Cerenkov light imaging of proton-induced positron is promising for proton therapy.

Cited by 17 publications

References 52 publications

Cerenkov luminescence imaging: physics principles and potential applications in biomedical sciences

Cerenkov luminescence imaging: physics principles and potential applications in biomedical sciences

Technical Note: Machine learning approaches for range and dose verification in proton therapy using proton‐induced positron emitters

Luminescence imaging of water during carbon-ion irradiation for range estimation

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