Proton beam range verification by means of ionoacoustic measurements at clinically relevant doses using a correlation-based evaluation

Schauer, Jannis; Wieser, Hans-Peter; Huang, Yuanhui; Ruser, H.; Lascaud, Julie; Würl, Matthias; Chmyrov, Andriy; Vidal, M.; Hérault, J.; Ntziachristos, Vasilis; Assmann, W.; Parodi, Katia; Dollinger, G.

doi:10.3389/fonc.2022.925542

Cited by 14 publications

(20 citation statements)

References 31 publications

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“…The total dose of an n-fold averaged measurement can thus be calculated by D = n × 2.40 cGy. The resulting n-fold averaged measurements M(t) are then filtered with a correlation filter utilising a simulated filter template F(t) (Schauer et al 2022). The template reflects the temporal structure of the expected ionoacoustic signal (Turin 1960).…”

Section: Signal-processing Of Ionoacoustic Measurementsmentioning

confidence: 99%

“…In this manuscript, the task of deconvolution is circumvented by applying a matched filter (Schauer et al 2022). In addition, the matched filter represents an ideal filter with respect to SNR maximisation (Turin 1960).…”

Section: Introduction and Purposementioning

confidence: 99%

“…In particular, it must be mentioned that the studies (Sueyasu et al 2022) and (Caron et al 2023) both use a water phantom and a substantially higher beam current (more than 4-fold) than in this study. It has already been shown that a higher instantaneous beam current contributes to a higher SNR at the same number of protons or dose (Schauer et al 2022), respectively. Nevertheless, the cited papers suggest that the Cetacean hydrophone used in this study is not ideal for the detection of ionoacoustic signals.…”

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confidence: 96%

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Range verification of a clinical proton beam in an abdominal phantom by co-registration of ionoacoustics and ultrasound

et al. 2023

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Objective: The range uncertainty in proton radiotherapy is a limiting factor to achieve optimum dose conformity to the tumour volume. Ionoacoustics is a promising approach for in-situ range verification, which would allow to reduce the size of the irradiated volume relative to the tumour volume. The energy deposition of a pulsed proton beam leads to an acoustic pressure wave (ionoacoustics), the detection of which allows conclusion about the distance between the Bragg peak and the acoustic detector. This information can be transferred into a co-registered ultrasound image, marking the Bragg peak position relative to the surrounding anatomy.Approach: A CIRS 3D abdominal phantom was irradiated with 126 MeV protons at a clinical proton therapy centre. Acoustic signals were recorded on the beam axis distal to the Bragg peak with a Cetacean C305X hydrophone. The ionoacoustic measurements were processed with a correlation filter using simulated filter templates. The hydrophone was rigidly attached to an ultrasound device (Interson GP-C01) recording ultrasound images of the irradiated region.Main results: The time of flight obtained from ionoacoustic measurements were transferred to an ultrasound image by means of an optoacoustic calibration measurement. The Bragg peak position was marked in the ultrasound image with a statistical uncertainty of σ=0.5 mm of 24 individual measurements depositing 1.2 Gy at the Bragg peak. The difference between the evaluated Bragg peak position and the one obtained from irradiation planning (1.0 mm) is smaller than the typical range uncertainty (∼4 mm) at the given penetration depth (∼10 cm). Significance: The measurements show that it is possible to determine the Bragg peak position of a clinical proton beam with submillimetre precision and transferring the information to an ultrasound image of the irradiated region. The dose required for this is smaller than that used for a typical irradiation fraction.

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Section: Signal-processing Of Ionoacoustic Measurementsmentioning

confidence: 99%

Section: Introduction and Purposementioning

confidence: 99%

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confidence: 96%

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Range verification of a clinical proton beam in an abdominal phantom by co-registration of ionoacoustics and ultrasound

et al. 2023

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“…More recent efforts have focused on range verification via prompt gamma-rays emitted as a result of proton irradiation (Min et al 2006, Verburg andSeco 2014). Proton range verification via ionoacoustic measurements is also being pursued but has thus far largely focussed on homogeneous phantoms (Tada et al 1991, Schauer et al 2022.…”

Section: Introductionmentioning

confidence: 99%

Impact of and interplay between proton arc therapy and range uncertainties in proton therapy for head-and-neck cancer

Tattenberg,

Liu,

Mulhem

et al. 2024

Phys. Med. Biol.

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Objective: Proton therapy reduces the integral dose to the patient compared to conventional photon treatments. However, in vivo proton range uncertainties remain a considerable hurdle. Range uncertainty reduction benefits depend on clinical practices. During intensity-modulated proton therapy (IMPT), the target is irradiated from only a few directions, but proton arc therapy (PAT), for which the target is irradiated from dozens of angles, may see clinical implementation by the time considerable range uncertainty reductions are achieved. It is therefore crucial to determine the impact of PAT on range uncertainty reduction benefits.Approach: For twenty head-and-neck cancer patients, four different treatment plans were created: an IMPT and a PAT treatment plan assuming current clinical range uncertainties of 3.5% (IMPT3.5% and PAT3.5%), and an IMPT and a PAT treatment plan assuming that range uncertainties can be reduced to 1% (IMPT1% and PAT1%). Plans were evaluated with respect to target coverage and organ-at-risk doses as well as normal tissue complication probabilities (NTCPs) for parotid glands (endpoint: parotid gland flow < 25%) and larynx (endpoint: larynx edema). Main results: Implementation of PAT (IMPT3.5%-PAT3.5%) reduced mean NTCPs in the nominal and worst-case scenario by 3.2 percentage points (pp) and 4.2 pp, respectively. Reducing range uncertainties from 3.5% to 1% during use of IMPT (IMPT3.5%-IMPT1%) reduced evaluated NTCPs by 0.9 pp and 2.0 pp. Benefits of range uncertainty reductions subsequently to PAT implementation (PAT3.5%- PAT1%) were 0.2 pp and 1.0 pp, with considerably higher benefits in bilateral compared to unilateral cases.Significance: The mean clinical benefit of implementing PAT was more than twice as high as the benefit of a 3.5% to 1% range uncertainty reduction. Range uncertainty reductions are expected to remain beneficial even after PAT implementation, especially in cases with target positions allowing for full leveraging of the higher number of gantry angles during PAT.

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“…In previous studies, denoising techniques such as low-pass filters [16], wavelet-based transformations (WT) [17,18] and correlation based methods [19] were used to eliminate high-frequency noise in signals.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based protoacoustic signal denoising for proton range verification

Wang

Sohn

Lei

et al. 2023

Biomed. Phys. Eng. Express

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Proton therapy is a type of radiation therapy that can provide better dose distribution compared to photon therapy by delivering most of the energy at the end of range, which is called the Bragg peak (BP). The protoacoustic technique was developed to determine the BP locations in vivo, but it requires a large dose delivery to the tissue to obtain a high number of signal averaging (NSA) to achieve a sufficient signal-to-noise ratio (SNR), which is not suitable for clinical use. A novel deep learning-based technique has been proposed to denoise acoustic signals and reduce BP range uncertainty with much lower doses. Three accelerometers were placed on the distal surface of a cylindrical polyethylene (PE) phantom to collect protoacoustic signals. In total, 512 raw signals were collected at each device. Device-specific stack autoencoder (SAE) denoising models were trained to denoise the noise-containing input signals, which were generated by averaging only 1, 2, 4, 8, 16, or 24 raw signals(low NSA signals), while the cleansignals were obtained by averaging 192 raw signals (high NSA). Both supervised and unsupervised training strategies were employed, and the evaluation of the models was based on mean squared error (MSE), SNR, and BP range uncertainty. Overall, the supervised SAEs outperformed the unsupervised SAEs in BP range verification. For the high accuracy detector, it achieved a BP range uncertainty of 0.20 ± 3.44 mm by averaging over 8 raw signals, while for the other two low accuracy detectors, they achieved the BP uncertainty of 1.44 ± 6.45 mm and -0.23 ± 4.88 mm by averaging 16 raw signals, respectively. This deep learning-based denoising method has shown promising results in enhancing the SNR of protoacoustic measurements and improving the accuracy in BP range verification. It greatly reduces the dose and time for potential clinical applications.

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Proton beam range verification by means of ionoacoustic measurements at clinically relevant doses using a correlation-based evaluation

Cited by 14 publications

References 31 publications

Range verification of a clinical proton beam in an abdominal phantom by co-registration of ionoacoustics and ultrasound

Range verification of a clinical proton beam in an abdominal phantom by co-registration of ionoacoustics and ultrasound

Impact of and interplay between proton arc therapy and range uncertainties in proton therapy for head-and-neck cancer

Deep learning-based protoacoustic signal denoising for proton range verification

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