Proton range verification with MACACO II Compton camera enhanced by a neural network for event selection

Muñoz, Enrique; Ros, Alberto; Borja-Lloret, Marina; Barrio, John; Dendooven, P.; Oliver, J.; Ozoemelam, Ikechi; Röser, J.; Llosá, G.

doi:10.1038/s41598-021-88812-5

Cited by 30 publications

(39 citation statements)

References 56 publications

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“…The data presented show how NN processing of measured CC data can improve the reconstructed PG images, which agrees with previously published studies [26,38,39]. In a previous study [40], we have shown that the NN used in this study can not only detect true and false events, but can also simultaneously predict interaction order of the true events with an overall accuracy of 84%.…”

Section: Discussionsupporting

confidence: 89%

“…The use of CCs for proton beam range verification is of particular interest due to their ability to reconstruct full 3D images of PG emission, which could, in principle, be registered and overlaid onto the patients' CT dataset for visual (and analytical) comparison to the planned treatment dose [8,11]. While 3D image reconstruction of PG emission with a CC during proton beam delivery has been proven feasible [25,26], the ability to do so at full clinical proton RT dose rates and under full clinical treatment conditions has thus far not been possible. Several studies of prototype CCs with high energy accelerator beams and clinical proton beams have shown rather poor performance for detecting the "true" double-scatter (DS; a single PG interacting twice in the CC, including Compton-photo-absorption, Compton-Compton, and Compton-pair production interactions) and "true" triplescatter (TS; a single PG interacting three times in the CC, including two Compton interactions and a third Compton, photo-absorption, or pair production interaction) PG events needed for CC image reconstruction [25,[27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

“…In particular, Zoglauer et al [38] and Basalyga et al [39] showed that relatively simple NNs can be used to predict the correct ordering of TS interactions in a CC. Also, Muñoz et al [26] showed that simple NNs can be used to identify true and false TS events recorded by a CC during delivery of experimental, low intensity proton beams and that using the NN predicted true TS events led to modest improvements in the final images.…”

Section: Introductionmentioning

confidence: 99%

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Applications of Machine Learning to Improve the Clinical Viability of Compton Camera Based in vivo Range Verification in Proton Radiotherapy

et al. 2022

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We studied the application of a deep, fully connected Neural Network (NN) to process prompt gamma (PG) data measured by a Compton camera (CC) during the delivery of clinical proton radiotherapy beams. The network identifies 1) recorded “bad” PG events arising from background noise during the measurement, and 2) the correct ordering of PG interactions in the CC to help improve the fidelity of “good” data used for image reconstruction. PG emission from a tissue-equivalent target during irradiation with a 150 MeV proton beam delivered at clinical dose rates was measured with a prototype CC. Images were reconstructed from both the raw measured data and the measured data that was further processed with a neural network (NN) trained to identify “good” and “bad” PG events and predict the ordering of individual interactions within the good PG events. We determine if NN processing of the CC data could improve the reconstructed PG images to a level in which they could provide clinically useful information about the in vivo range and range shifts of the proton beams delivered at full clinical dose rates. Results showed that a deep, fully connected NN improved the achievable contrast to noise ratio (CNR) in our images by more than a factor of 8x. This allowed the path, range, and lateral width of the clinical proton beam within a tissue equivalent target to easily be identified from the PG images, even at the highest dose rates of a 150 MeV proton beam used for clinical treatments. On average, shifts in the beam range as small as 3 mm could be identified. However, when limited by the amount of PG data measured with our prototype CC during the delivery of a single proton pencil beam (∼1 × 109 protons), the uncertainty in the reconstructed PG images limited the identification of range shift to ∼5 mm. Substantial improvements in CC images were obtained during clinical beam delivery through NN pre-processing of the measured PG data. We believe this shows the potential of NNs to help improve and push CC-based PG imaging toward eventual clinical application for proton RT treatment delivery verification.

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Section: Discussionsupporting

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Applications of Machine Learning to Improve the Clinical Viability of Compton Camera Based in vivo Range Verification in Proton Radiotherapy

et al. 2022

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“…In comparison, the recent MC study by Yao et al 37 , obtained accuracies within 2 mm for the same F 80% and F 50% magnitudes using also a combination of γ-ray lines. Moreover, our results are also at the level of previous works on MC simulations of Compton cameras applied to PGI 18,26,51 , and better than the 5 mm obtained in Ref. 20 or the 7 mm reported in Ref.…”

Section: Discussionsupporting

confidence: 69%

Towards machine learning aided real-time range imaging in proton therapy

Lerendegui-Marco¹,

Balibrea-Correa²,

Babiano-Súarez³

et al. 2022

Preprint

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Compton imaging represents a promising technique for range verification in proton therapy treatments. In this work, we report on the advantageous aspects of the i-TED detector for proton-range monitoring, based on the results of the first Monte Carlo study of its applicability to this field. i-TED is an array of Compton cameras, that have been specifically designed for neutron-capture nuclear physics experiments, which are characterized by γ-ray energies spanning up to 5-6 MeV, rather low γ-ray emission yields and very intense neutron induced γ-ray backgrounds. Our developments to cope with these three aspects are concomitant with those required in the field of hadron therapy, especially in terms of high efficiency for real-time monitoring, low sensitivity to neutron backgrounds and reliable performance at the high γ-ray energies. We find that signal-to-background ratios can be appreciably improved with i-TED thanks to its light-weight design and the low neutron-capture cross sections of its LaCl 3 crystals, when compared to other similar systems based on LYSO, CdZnTe or LaBr 3 . Its high time-resolution (CRT∼500 ps) represents an additional advantage for background suppression when operated in pulsed HT mode. Each i-TED Compton module features two detection planes of very large LaCl 3 monolithic crystals, thereby achieving a high efficiency in coincidence of 0.2% for a point-like 1 MeV γ-ray source at 5 cm distance. This leads to sufficient statistics for reliable image reconstruction with an array of four i-TED detectors assuming clinical intensities of 10 8 protons per treatment point. The use of a two-plane design instead of three-planes has been preferred owing to the higher attainable efficiency for double time-coincidences than for threefold events. The loss of full-energy events for high energy γ-rays is compensated by means of Machine-Learning based algorithms, which allow one to enhance the signal-to-total ratio up to a factor of 2.

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“…In Compton imaging devices, ML has been investigated for sequence ordering of multiple-interaction events [157] 2 and for signal and background discrimination of Compton camera data in the context of prompt gamma imaging [100]. It is also worth mentioning that DL-based methods have been studied for event selection in data measured by radiation detectors, in particular in HEP, as shown for example in [51].…”

Section: Deep Learning In Nuclear Imagingmentioning

confidence: 99%

Artificial Intelligence for Monte Carlo Simulation in Medical Physics

et al. 2021

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Monte Carlo simulation of particle tracking in matter is the reference simulation method in the field of medical physics. It is heavily used in various applications such as 1) patient dose distribution estimation in different therapy modalities (radiotherapy, protontherapy or ion therapy) or for radio-protection investigations of ionizing radiation-based imaging systems (CT, nuclear imaging), 2) development of numerous imaging detectors, in X-ray imaging (conventional CT, dual-energy, multi-spectral, phase contrast … ), nuclear imaging (PET, SPECT, Compton Camera) or even advanced specific imaging methods such as proton/ion imaging, or prompt-gamma emission distribution estimation in hadrontherapy monitoring. Monte Carlo simulation is a key tool both in academic research labs as well as industrial research and development services. Because of the very nature of the Monte Carlo method, involving iterative and stochastic estimation of numerous probability density functions, the computation time is high. Despite the continuous and significant progress on computer hardware and the (relative) easiness of using code parallelisms, the computation time is still an issue for highly demanding and complex simulations. Hence, since decades, Variance Reduction Techniques have been proposed to accelerate the processes in a specific configuration. In this article, we review the recent use of Artificial Intelligence methods for Monte Carlo simulation in medical physics and their main associated challenges. In the first section, the main principles of some neural networks architectures such as Convolutional Neural Networks or Generative Adversarial Network are briefly described together with a literature review of their applications in the domain of medical physics Monte Carlo simulations. In particular, we will focus on dose estimation with convolutional neural networks, dose denoising from low statistics Monte Carlo simulations, detector modelling and event selection with neural networks, generative networks for source and phase space modelling. The expected interests of those approaches are discussed. In the second section, we focus on the current challenges that still arise in this promising field.

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Proton range verification with MACACO II Compton camera enhanced by a neural network for event selection

Cited by 30 publications

References 56 publications

Applications of Machine Learning to Improve the Clinical Viability of Compton Camera Based in vivo Range Verification in Proton Radiotherapy

Applications of Machine Learning to Improve the Clinical Viability of Compton Camera Based in vivo Range Verification in Proton Radiotherapy

Towards machine learning aided real-time range imaging in proton therapy

Artificial Intelligence for Monte Carlo Simulation in Medical Physics

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