First Results From All-Digital PET Dual Heads for In-Beam Beam-On Proton Therapy Monitoring

Gao, Min; Chen, Hsien-Hsin; Chen, Fang-Hsin; Hong, Ji-Hong; Hsiao, Ing‐Tsung; Yen, Tzu-Chen; Mao, Jingfang; Lu, Jiade J.; Wang, Weidong; D’Ascenzo, N.; Xie, Qingguo

doi:10.1109/trpms.2020.3041857

Cited by 10 publications

(7 citation statements)

References 41 publications

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“…There are two challenges in beam-on operation: first, the PET system needs to have a high count rate for short bursts of signals. Second, the interaction of the beam with biological tissues will produce a large number of secondary particles such as prompt gamma photons and neutrons, which will cause prompt background events to PET acquisition (Gao 2020). To avoid the influence of prompt radiation events, in-beam PET focused on the data acquisition after irradiation(beam-off) in previous studies (Nishio et al 2010, Brombal 2017, Topi 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Until now, most of the prompt radiation events filtering methods are based on the radio frequency (RF) property of the accelerator. Among this, the RF period of the accelerator can be calculated by the delayed-coincidence method (Piliero 2016, Gao 2020) and Fourier Transform method in the frequency domain (Kostara 2019). After the accelerator period is obtained, this requires determining the start time of each period to calculate the phase of the event.…”

Section: Introductionmentioning

confidence: 99%

“…After the accelerator period is obtained, this requires determining the start time of each period to calculate the phase of the event. Since the prompt gamma is produced almost simultaneously with the incident proton, Piliero (2016) and Gao (2020) used the time of prompt gamma generation to approximate the start time of the period, rather than relying on the time recorded by an additional particle detector (Crespo et al 2005). They performed a time-resolved spectroscopic analysis of the events in a microbunch to filter out the prompt radiation events.…”

Section: Introductionmentioning

confidence: 99%

“…Then this period works with the energy threshold of prompt gamma for event selection to filter out the prompt radiation events. In general, the energy threshold of prompt gamma is set as 1 MeV (Piliero 2016, Gao 2020. In this paper, we explore the influence of various thresholds and identify the optimal value.…”

Section: Introductionmentioning

confidence: 99%

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In-beam PET monitoring of proton therapy: a method for filtering prompt radiation events

Ma,

Yang,

et al. 2024

Phys. Med. Biol.

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Objective. In-beam Positron Emission Tomography (PET) is a promising technology for real-time monitoring of proton therapy. Random coincidences between prompt radiation events and positron annihilation photon pairs can deteriorate imaging quality during beam-on operation. This study aimed to improve the PET image quality by filtering out the prompt radiation events. Approach. We investigated a prompt radiation event filtering method based on the accelerator radio frequency (RF) phase and assessed its performance using various prompt gamma energy thresholds. An in-beam PET prototype was used to acquire the data when the 70 MeV proton beam irradiated a water phantom and a mouse. The signal-to-background ratio indicator was utilized to evaluate the quality of the PET reconstruction image. Main results. The selection of the prompt gamma energy threshold will affect the quality of the reconstructed image. Using the optimal energy threshold of 580 keV can obtain a signal-to-background ratio of 1.6 times for the water phantom radiation experiment and 2.0 times for the mouse radiation experiment compared to those without background removal, respectively. Significance. Our results show that using this optimal threshold can reduce the prompt radiation events, enhancing the signal-to-background ratio of the reconstructed image. This advancement contributes to more accurate real-time range verification in subsequent steps.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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In-beam PET monitoring of proton therapy: a method for filtering prompt radiation events

Ma,

Yang,

et al. 2024

Phys. Med. Biol.

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“…Limited-angle TOF-PET systems are being investigated for intraoperative surgical applications [6] and for multi-modal compatible flexible PET imaging to fit existing magnetic resonance imaging (MRI) or computed tomography (CT) [7]. Dual panel detectors are being studied to be used for in-beam PET systems used for monitoring proton or heavy ion therapies [8], [9], [10] and also for positron emission mammography [11], [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Multipanel Limited Angle PET System With 50 ps FWHM Coincidence Time Resolution: A Simulation Study

Razdevsek

Dolenec

Križan

et al. 2022

IEEE Trans. Radiat. Plasma Med. Sci.

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In this simulation study, we evaluate the performance of a limited angular coverage PET system consisting of two/four fast-timing 50 ps FWHM CTR flat-panel detectors made of 5 -20 mm long pixelated LSO crystals. We studied image quality and count rates following the NEMA standard, spatial resolution by imaging a Derenzo phantom and a hot rod, and investigated the sensitivity of different scanner designs. We demonstrated the possible use of such a scanner by imaging a human head and a torso of the XCAT digital phantom. All the designs were compared to the reference scanner, based on Siemens Biograph Vision PET/CT scanner geometry. We show that good CTR can compensate for lower detection efficiency or smaller angular coverage. Good image quality can be obtained with a simple limited-angle PET system without distortions or artefacts. Substantial degradation of the spatial resolution with increased crystal length is observed in the two-panel design due to the parallax error, but not in the four-panel design. The four-panel design simulated with a CTR of 50 ps FWHM is comparable to that of the current state-of-the-art clinical PET/CT scanner. Similar fast-timing limited-angle planar detectors could enable much less expensive total-body or single organ (dynamically selectable) imaging devices.

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Proton spot dose estimation based on positron activity distributions with neural network

Zhang,

Mu,

et al. 2024

Medical Physics

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BackgroundPositron emission tomography (PET) has been investigated for its ability to reconstruct proton‐induced positron activity distributions in proton therapy. This technique holds potential for range verification in clinical practice. Recently, deep learning‐based dose estimation from positron activity distributions shows promise for in vivo proton dose monitoring and guided proton therapy.PurposeThis study evaluates the effectiveness of three classical neural network models, recurrent neural network (RNN), U‐Net, and Transformer, for proton dose estimating. It also investigates the characteristics of these models, providing valuable insights for selecting the appropriate model in clinical practice.MethodsProton dose calculations for spot beams were simulated using Geant4. Computed tomography (CT) images from four head cases were utilized, with three for training neural networks and the remaining one for testing. The neural networks were trained with one‐dimensional (1D) positron activity distributions as inputs and generated 1D dose distributions as outputs. The impact of the number of training samples on the networks was examined, and their dose prediction performance in both homogeneous brain and heterogeneous nasopharynx sites was evaluated. Additionally, the effect of positron activity distribution uncertainty on dose prediction performance was investigated. To quantitatively evaluate the models, mean relative error (MRE) and absolute range error (ARE) were used as evaluation metrics.ResultsThe U‐Net exhibited a notable advantage in range verification with a smaller number of training samples, achieving approximately 75% of AREs below 0.5 mm using only 500 training samples. The networks performed better in the homogeneous brain site compared to the heterogeneous nasopharyngeal site. In the homogeneous brain site, all networks exhibited small AREs, with approximately 90% of the AREs below 0.5 mm. The Transformer exhibited the best overall dose distribution prediction, with approximately 92% of MREs below 3%. In the heterogeneous nasopharyngeal site, all networks demonstrated acceptable AREs, with approximately 88% of AREs below 3 mm. The Transformer maintained the best overall dose distribution prediction, with approximately 85% of MREs below 5%. The performance of all three networks in dose prediction declined as the uncertainty of positron activity distribution increased, and the Transformer consistently outperformed the other networks in all cases.ConclusionsBoth the U‐Net and the Transformer have certain advantages in the proton dose estimation task. The U‐Net proves well suited for range verification with a small training sample size, while the Transformer outperforms others at dose‐guided proton therapy.

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First Results From All-Digital PET Dual Heads for In-Beam Beam-On Proton Therapy Monitoring

Cited by 10 publications

References 41 publications

In-beam PET monitoring of proton therapy: a method for filtering prompt radiation events

In-beam PET monitoring of proton therapy: a method for filtering prompt radiation events

Multipanel Limited Angle PET System With 50 ps FWHM Coincidence Time Resolution: A Simulation Study

Proton spot dose estimation based on positron activity distributions with neural network

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