Estimation of the optical errors on the luminescence imaging of water for proton beam

Yabe, Takuya; Komori, Masataka; Horita, Ryo; Toshito, T.; Yamamoto, Seiichi

doi:10.1016/j.nima.2018.01.030

Cited by 11 publications

(15 citation statements)

References 20 publications

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“…Measurement uncertainties caused by corrections of the CCD sensor efficiency distribution and the lens image distortion should be considered in the prediction estimation 30–32 . Additionally, obtaining a LD scale calibration from the CCD for measuring absolute phantom size would add another uncertainty to the prediction 32,33 . We are currently preparing a fully closed housing phantom, covering a CCD and a scintillator in the fixed position, for future experiments.…”

Section: Discussionmentioning

confidence: 99%

“…[30][31][32] Additionally, obtaining a LD scale calibration from the CCD for measuring absolute phantom size would add another uncertainty to the prediction. 32,33 We are currently preparing a fully closed housing phantom, covering a CCD and a scintillator in the fixed position, for future experiments. This phantom will help to minimize phantom setup uncertainty and maintain a consistent background light.…”

Section: Energymentioning

confidence: 99%

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Deep learning proton beam range estimation model for quality assurance based on two‐dimensional scintillated light distributions in simulations

et al. 2023

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BackgroundMany studies have utilized optical camera systems with volumetric scintillators for quality assurances (QA) to estimate the proton beam range. However, previous analytically driven range estimation methods have the difficulty to derive the dose distributions from the scintillation images with quenching and optical effects.PurposeIn this study, a deep learning method utilized to QA was used to predict the beam range and spread‐out Bragg peak (SOBP) for two‐dimensional (2D) map conversion from the scintillation light distribution (LD) into the dose distribution in a water phantom.MethodsThe 2D residual U‐net modeling for deep learning was used to predict the 2D water dose map from a 2D scintillation LD map. Monte Carlo simulations for dataset preparation were performed with varying monoenergetic proton beam energies, field sizes, and beam axis shifts. The LD was reconstructed using photons backpropagated from the aperture as a virtual lens. The SOBP samples were constructed based on monoenergetic dose distributions. The training set, including the validation set, consisted of 8659 image pairs of LD and water dose maps. After training, dose map prediction was performed using a 300 image pair test set generated under random conditions. The pairs of simulated and predicted dose maps were analyzed by Bragg peak fitting and gamma index maps to evaluate the model prediction.ResultThe estimated beam range and SOBP width resolutions were 0.02 and 0.19 mm respectively for varying beam conditions, and the beam range and SOBP width deviations from the reference simulation result were less than 0.1 and 0.8 mm respectively. The simulated and predicted distributions showed good agreement in the gamma analysis, except for rare cases with failed gamma indices in the proximal and field‐marginal regions.ConclusionThe deep learning conversion method using scintillation LDs in an optical camera system with a scintillator is feasible for estimating proton beam range and SOBP width with high accuracy.

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Section: Discussionmentioning

confidence: 99%

Section: Energymentioning

confidence: 99%

Deep learning proton beam range estimation model for quality assurance based on two‐dimensional scintillated light distributions in simulations

et al. 2023

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“…14 One possible reason for this smaller Bragg peak height is the parallax error, since the Bragg peak was not imaged at center and thus the peak height was smaller. 16 Another possible reason is the quenching effect of the luminescence of water, although this was not observed for protons after correction. 17…”

Section: Discussionmentioning

confidence: 99%

Imaging of polarized components of Cerenkov light and luminescence of water during carbon‐ion irradiation

et al. 2020

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Purpose The luminescence image of water during the irradiation of carbon ions showed higher intensity at shallow depths than dose distribution due to the contamination of Cerenkov light from secondary electrons. Since Cerenkov light is coherent and polarized for the light produced during the irradiation of carbon ions to water, the reduction of Cerenkov light may be possible with a polarizer. In addition, there is no information on the polarization of the luminescence of water. To clarify these points, we measured the optical images of water during the irradiation of carbon ions with a polarizer by changing the directions of the transmission axis. Methods Imaging was conducted using a cooled charge‐coupled device (CCD) camera during the irradiation of 241.5 MeV/n energy carbon ions to a water phantom with a polarizer in front of the lens by changing the transmission axis parallel and perpendicular to the carbon‐ion beam. Results With the polarizer parallel to the carbon‐ion beam, the intensity at the shallow depth was ~26% higher than that measured with the polarizer perpendicular to the beam. We found no significant intensity difference between these two images at deeper depths where the Cerenkov light was not included. The difference image of the parallel and perpendicular directions showed almost the same image as the simulated Cerenkov light distribution. Using the measured difference image, correction of the Cerenkov component was possible from the measured luminescence image of water during the irradiation of carbon ions. Conclusion We could measure the difference of the Cerenkov light component by changing the transmission axis of the polarizer. Also we clarified that there was no difference in the luminescence of water by changing the transmission axis of the polarizer.

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“…The signal generation in XLCT is a form of radioluminescence where the ionizing radiation (in this case, x-ray photons) causes the emission of optical photons from the embedded contrast agents, and it is generally assumed that all the optical photons generated are emitted only from the contrast agents. However, numerous studies have reported other sources of optical photons from the radioluminescence of air, water, and biological tissue [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36] at energies below the Cerenkov radiation threshold which will provide background noise and limit the molecular sensitivity of XLCT imaging. Yamamoto et al conducted various luminescence imaging experiments with different sources of radiation to image both water and air.…”

Section: Introductionmentioning

confidence: 99%

“…Yamamoto et al conducted various luminescence imaging experiments with different sources of radiation to image both water and air. Using proton-beam irradiation, they found that water was able to luminesce even during traditional proton-therapy, and determined that this information could be useful for dose and range estimation [20][21][22][23]30]. With carbon-ion irradiation, they performed similar luminescence imaging (also with energy below the Cerenkov-threshold) and determined, by measuring and deriving the light spectra, that this water luminescence was likely caused by an electromagnetic pulse produced from the dipole displacement inside water molecules as the derived spectra was found to be proportional to λ −2.0 [24,25].…”

Section: Introductionmentioning

confidence: 99%

Background luminescence in x-ray luminescence computed tomography (XLCT) imaging

Lun

2019

Appl. Opt.

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X-ray luminescence computed tomography (XLCT) is an emerging hybrid imaging modality. It has been recently reported that materials like water, tissue, or even air can generate optical photons upon x-ray irradiation which can increase the noises in measurements of XLCT. In this study, we have investigated the x-ray luminescence from water, air, as well as tissue mimicking phantoms, including one embedded with a 0.01 mg/mL GOS:Eu 3+ microphosphor target. We have measured the optical emission spectrum from each sample, including samples of meat and fat, using a spectrograph. Our results indicate that there are plenty of optical photons emitted by x-ray irradiation and a small nanophosphor concentration, as low as 5.28 μM in a deep background can provide enough contrast for XLCT imaging.

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Estimation of the optical errors on the luminescence imaging of water for proton beam

Cited by 11 publications

References 20 publications

Deep learning proton beam range estimation model for quality assurance based on two‐dimensional scintillated light distributions in simulations

Deep learning proton beam range estimation model for quality assurance based on two‐dimensional scintillated light distributions in simulations

Imaging of polarized components of Cerenkov light and luminescence of water during carbon‐ion irradiation

Background luminescence in x-ray luminescence computed tomography (XLCT) imaging

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