Erratum: “Gamma electron vertex imaging and application to beam range verification in proton therapy”

Kim, Chan Hyeong; Park, Jin Hyung; Seo, Hee; Lee, Han Rim

doi:10.1118/1.4749930

Cited by 34 publications

(24 citation statements)

References 4 publications

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“…In particle therapy, a small beam range difference could be critical because normal tissues might be irradiated with unnecessary radiation dose if the ranges are not precisely measured. Techniques are being developed to measure the ranges from outside of the subject during beam irradiation 1–22 . In these techniques, imaging of the secondary electron bremsstrahlung (SEB) x ray emitted during proton or carbon ion irradiation is also a promising method for range estimation 23 and we have so far developed low‐energy x‐ray cameras and conducted the imaging of SEB x ray of proton 24,25 or carbon ion 26 by using these low‐energy x‐ray cameras.…”

Section: Introductionmentioning

confidence: 99%

Dose image prediction for range and width verifications from carbon ion‐induced secondary electron bremsstrahlung x‐rays using deep learning workflow

et al. 2020

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Purpose Imaging of the secondary electron bremsstrahlung (SEB) x rays emitted during particle‐ion irradiation is a promising method for beam range estimation. However, the SEB x‐ray images are not directly correlated to the dose images. In addition, limited spatial resolution of the x‐ray camera and low‐count situation may impede correctly estimating the beam range and width in SEB x‐ray images. To overcome these limitations of the SEB x‐ray images measured by the x‐ray camera, a deep learning (DL) approach was proposed in this work to predict the dose images for estimating the range and width of the carbon ion beam on the measured SEB x‐ray images. Methods To prepare enough data for the DL training efficiently, 10,000 simulated SEB x‐ray and dose image pairs were generated by our in‐house developed model function for different carbon ion beam energies and doses. The proposed DL neural network consists of two U‐nets for SEB x ray to dose image conversion and super resolution. After the network being trained with these simulated x‐ray and dose image pairs, the dose images were predicted from simulated and measured SEB x‐ray testing images for performance evaluation. Results For the 500 simulated testing images, the average mean squared error (MSE) was 2.5 × 10−5 and average structural similarity index (SSIM) was 0.997 while the error of both beam range and width was within 1 mm FWHM. For the three measured SEB x‐ray images, the MSE was no worse than 5.5 × 10−3 and SSIM was no worse than 0.980 while the error of the beam range and width was 2 mm and 5 mm FWHM, respectively. Conclusions We have demonstrated the advantages of predicting dose images from not only simulated data but also measured data using our deep learning approach.

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Section: Introductionmentioning

confidence: 99%

Dose image prediction for range and width verifications from carbon ion‐induced secondary electron bremsstrahlung x‐rays using deep learning workflow

et al. 2020

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“…42 Another camera for 2D imaging in proton therapy is GEVI system. 35 The efficiency of the SS collimator is about 10-fold better than that of the GEVI (1.8 × 10 −5 vs. 1.6 × 10 −6 ). Using the SS camera with 10 8 incident protons, the range precision was detected within 2 mm for 100 and 160 MeV which is comparable with that of GEVI predicted range precision reported for 6.24 × 10 9 incident protons.…”

Section: Discussionmentioning

confidence: 96%

“…Our optimized SS collimator efficiency decreases at a slower rate (1/h) 42 . Another camera for 2D imaging in proton therapy is GEVI system 35 . The efficiency of the SS collimator is about 10‐fold better than that of the GEVI (

1.8 \times {10}^{-5}\ {\rm vs}.\ 1.6 \times {10}^{-6}

).…”

Section: Discussionmentioning

confidence: 99%

Design and performance evaluation of a slit‐slat camera for 2D prompt gamma imaging in proton therapy monitoring: A Monte Carlo simulation study

et al. 2023

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Purpose We investigated the design of a prompt gamma camera for real‐time dose delivery verification and the partial mitigation of range uncertainties. Methods A slit slat (SS) camera was optimized using the trade‐off between the signal‐to‐noise ratio and spatial resolution. Then, using the GATE Monte Carlo package, the camera performances were estimated by means of target shifts, beam position quantification, changing the camera distance from the beam, and air cavity inserting. A homogeneous PMMA phantom and the air gaps induced PMMA phantom were used. The air gaps ranged from 5 mm to 30 mm by 5 mm increments were positioned in the middle of the beam range. To reduce the simulation time, phase space scoring was used. The batch method with five realizations was used for stochastic error calculations. Results The system's detection efficiency was 1.1×10−4PGsEmitted0.33emPGs(1.8×10−5$1.1 \times {10}^{-4}\frac{{\rm PGs}}{{\rm Emitted}\ {\rm PGs}}\ (1.8 \times {10}^{-5}$ PGs/proton) for a 10 × 20 cm2 detector (source‐to‐collimator distance = 15.0 cm). Axial and transaxial resolutions were 23 mm and 18 mm, respectively. The SS camera estimated the range as 69.0 ± 3.4 (relative stochastic error 1‐sigma is 5%) and 67.6 ± 1.8 mm (2.6%) for the real range of 67.0 mm for 107 and 108 protons of 100 MeV, respectively. Considering 160 MeV, these values are 155.5 ± 3.1 (2%) and 152.2 ± 2.0 mm (1.3%) for the real range of 152.0 mm for 107 and 108 protons, respectively. Considering phantom shift, for a 100 MeV beam, the precision of the quantification (1‐sigma) in the axial and lateral phantom shift estimation is 2.6 mm and 1 mm, respectively. Accordingly, the axial and lateral quantification precisions were 1.3 mm and 1 mm for a 160 MeV beam, respectively. Furthermore, the quantification of an air gap formulated as gapdet=0.98×gapreal${{\rm gap}}_{det}=0.98 \times {{\rm gap}}_{{\rm real}}$, where gapdet${{\rm gap}}_{det}$ and gapreal are the estimated and real air gap, respectively. The precision of the air gap quantification is 1.6 mm (1 sigma). Moreover, 2D PG images show the trajectory of the proton beam through the phantom. Conclusion The proposed slit‐slat imaging systems can potentially provide a real‐time, in‐vivo, and non‐invasive treatment monitoring method for proton therapy.

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“…In proton therapy, beam-range or shape measurement during beam irradiation from outside the subject is important because a proton beam forms a Bragg peak at the end of the beam's range, and a small difference in ranges would be critical to subjects. Several methods have attempted to evaluate the ranges of particle ions in therapy using prompt gamma imaging [1][2][3][4][5][6][7][8][9][10][11][12] or imaging positrons produced in a subject by irradiation with proton beams [13][14][15][16][17][18][19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Simultaneous imaging of luminescence and prompt x-rays during irradiation with spot-scanning proton beams at clinical dose level

Yamamoto¹,

Yamashita²,

Kobashi³

et al. 2023

Biomed. Phys. Eng. Express

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Prompt x-ray imaging is a promising method for observing the beam shape from outside a subject. However, its distribution is different from dose distribution, and thus a comparison with the dose is required. Meanwhile, luminescence imaging of water is a possible method for imaging the dose distribution. Consequently, we performed simultaneous imaging of luminescence and prompt x-rays during irradiation by proton beams to compare the distributions between these two different imaging methods. Optical imaging of water was conducted with spot-scanning proton beams at clinical dose level during irradiation to a fluorescein (FS) water phantom set in a black box. Prompt x-ray imaging was also conducted simultaneously from outside the black box using a developed x-ray camera during proton beam irradiation to the phantom. We measured images of the luminescence of FS water and prompt x-rays for various types of proton beams, including pencil beams, spread-out Bragg peak (SOBP) beams, and clinically used therapy beams. After the imaging, ranges were estimated from FS water and prompt x-rays and compared with those calculated with a treatment planning system (TPS). We could measure the prompt x-ray and FS water images simultaneously for all types of proton beams. The ranges estimated from the FS water and those calculated with the TPS closely matched, within a difference of several mm. Similar range difference was found between the results estimated from prompt x-ray images and those calculated with the TPS. We confirmed that the simultaneous imaging of luminescence and prompt x-rays were possible during irradiation with spot-scanning proton beams at a clinical dose level. This method can be applied to range estimation as well as comparison with the dose for prompt x-ray imaging or other imaging methods used in therapy with various types of proton beams at a clinical dose level.

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Erratum: “Gamma electron vertex imaging and application to beam range verification in proton therapy”

Cited by 34 publications

References 4 publications

Dose image prediction for range and width verifications from carbon ion‐induced secondary electron bremsstrahlung x‐rays using deep learning workflow

Dose image prediction for range and width verifications from carbon ion‐induced secondary electron bremsstrahlung x‐rays using deep learning workflow

Design and performance evaluation of a slit‐slat camera for 2D prompt gamma imaging in proton therapy monitoring: A Monte Carlo simulation study

Simultaneous imaging of luminescence and prompt x-rays during irradiation with spot-scanning proton beams at clinical dose level

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