A Monte Carlo feasibility study for neutron based real-time range verification in proton therapy

Development and evaluation of an in-beam PET system for proton therapy monitoring

Objective. In-beam positron emission tomography (PET) has important development prospects in real-time monitoring of proton therapy. However, in the beam-on operation, the high bursts of radiation events pose challenges to the performance of the PET system.Approach. In this study, we developed a dual-head in-beam PET system for proton therapy monitoring and evaluated its performance. The system has two PET detection heads, each with6×3Plug&Imaging (PnI) detection units. Each PnI unit consists of6×6lutetium-yttrium oxyorthosilicate crystal arrays. The size of each crystal strip is3.95×3.95×20 mm3, which is one-to-one coupled with a silicon photomultiplier. The overall size of the head is15.3×7.65 cm2.Main results. The in-beam PET system achieved a single count rate of 48 Mcps at the activity of 144.9 MBq, an absolute sensitivity of 2.717%, and a spatial resolution of approximately 2.6 mm (full width at half maximum) at the center of the field-of-view. When imaging a Derenzo phantom, the system could resolve rods with a diameter of 2.0 mm. Time-dynamic [18F]-Fluorodeoxyglucose mouse imaging was performed, demonstrating the metabolic processes in the mouse. This shows that the in-beam PET system has the potential for biology-guided proton therapy. The in-beam PET system was used to monitor the range of a 130 MeV proton beam irradiating a polymethyl methacrylate (PMMA) phantom, with a beam intensity of6.0×109p s-1and an irradiation duration of one minute. PET data were acquired only during the one-minute irradiation. We simulated the range shift by moving the PMMA and adding an air gap, showing that the error between the actual and the measured range is less than 1 mm.Significance. The results demonstrate that the system has a high count rate and the capability of range monitoring in beam-on operation, which is beneficial for achieving real-time range verification of proton beams in the future.

Machine learning approach for proton range verification using real-time prompt gamma imaging with Compton cameras: addressing the total deposited energy information gap

Objective.Compton camera imaging shows promise as a range verification technique in proton therapy. This work aims to assess the performance of a machine learning model in Compton camera imaging for proton beam range verification improvement.Approach.The presented approach was used to recognize Compton events and estimate more accurately the prompt gamma (PG) energy in the Compton camera to reconstruct the PGs emission profile during proton therapy. This work reports the results obtained from the Geant4 simulation for a proton beam impinging on a polymethyl methacrylate (PMMA) target. To validate the versatility of such an approach, the produced PG emissions interact with a scintillating fiber-based Compton camera.Main results.A trained multilayer perceptron (MLP) neural network shows that it was possible to achieve a notable three-fold increase in the signal-to-total ratio. Furthermore, after event selection by the trained MLP, the loss of full-energy PGs was compensated by means of fitting an MLP energy regression model to the available data from true Compton (signal) events, predicting more precisely the total deposited energy for Compton events with incomplete energy deposition.Significance.A considerable improvement in the Compton camera's performance was demonstrated in determining the distal falloff and identifying a few millimeters of target displacements. This approach has shown great potential for enhancing online proton range monitoring with Compton cameras in future clinical applications.

A systematic approach for calibrating a Monte Carlo code to a treatment planning system for obtaining dose, LET, variable proton RBE and out-of-field dose

Objective. While integration of variable relative biological effectiveness (RBE) has not reached full clinical implementation, the importance of having the ability to recalculate proton treatment plans in a flexible, dedicated Monte Carlo (MC) code cannot be understated . Here we provide a step-wise method for calibrating dose from a MC code to a treatment planning system (TPS), to obtain required parameters for calculating linear energy transfer (LET), variable RBE and in general enabling clinical realistic research studies beyond the capabilities of a TPS.Approach. Initially, Pristine Bragg peaks (PBP) were calculated in both the Eclipse TPS and the FLUKA MC code. A rearranged Bortfeld energy-range relation was applied to the initial energy of the beam to fine-tune the range of the MC code at 80% dose level distal to the PBP. The energy spread was adapted by dividing the TPS range by the MC range for dose level 80%-20% distal to the PBP. Density and relative proton stopping power were adjusted by comparing the TPS and MC for different Hounsfield units. To find the relationship of dose per primary particle from the MC to dose per monitor unit in the TPS, integration was applied to the area of the Bragg curve. The calibration was validated for spread-out Bragg peaks (SOBP) in water and patient treatment plans. Following the validation, variable RBE were calculated using established models.Main results.The PBPs ranges were within ±0.3mm threshold, and a maximum of 5.5% difference for the SOBPs was observed. The patient validation showed excellent dose agreement between the TPS and MC, with the greatest differences for the lung tumor patient.Significance. Aprocedure for calibrating a MC code to a TPS was developed and validated. The procedure enables MC-based calculation of dose, LET, variable RBE, advanced (secondary) particle tracking and more from treatment plans.

A hybrid multi-particle approach to range assessment-based treatment verification in particle therapy

Particle therapy (PT) used for cancer treatment can spare healthy tissue and reduce treatment toxicity. However, full exploitation of the dosimetric advantages of PT is not yet possible due to range uncertainties, warranting development of range-monitoring techniques. This study proposes a novel range-monitoring technique introducing the yet unexplored concept of simultaneous detection and imaging of fast neutrons and prompt-gamma rays produced in beam-tissue interactions. A quasi-monolithic organic detector array is proposed, and its feasibility for detecting range shifts in the context of proton therapy is explored through Monte Carlo simulations of realistic patient models and detector resolution effects. The results indicate that range shifts of [Formula: see text] can be detected at relatively low proton intensities ([Formula: see text] protons/spot) when spatial information obtained through imaging of both particle species are used simultaneously. This study lays the foundation for multi-particle detection and imaging systems in the context of range verification in PT.

Simultaneous neutron and gamma imaging system for real time range and dose monitoring in Hadron Therapy and nuclear security applications

GN-Vision is a novel dual γ-ray and neutron imaging system, which aims at imaging, simultaneously to the prompt gammas, the spatial origin of the slow and thermal neutron dose (<100 eV) generated during hadron therapy treatments. The proposed device can also be of interest for industrial applications as well as in nuclear security. The GN-Vision system has been designed following the technical developments of the iTED detector, an array of high efficiency Compton cameras intended for neutron-capture experiments, in which γ-ray energies span up to 5-6 MeV, similar to the energies encountered in hadron therapy. This manuscript presents the evolution of the i-TED detector towards the GN-Vision system and the first conceptual study of the simultaneous neutron and γ-ray imaging capability. Last, it reviews the status of the development and first results of the promising performance of this device for PG imaging in proton therapy, based on MC simulations.

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

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.

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

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 [Formula: see text]-ray energies spanning up to 5-6 MeV, rather low [Formula: see text]-ray emission yields and very intense neutron induced [Formula: see text]-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 [Formula: see text]-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[Formula: see text] crystals, when compared to other similar systems based on LYSO, CdZnTe or LaBr[Formula: see text]. Its high time-resolution (CRT [Formula: see text] 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[Formula: see text] monolithic crystals, thereby achieving a high efficiency in coincidence of 0.2% for a point-like 1 MeV [Formula: see text]-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[Formula: see text] 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 [Formula: see text]-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.

Thermal neutron cross sections of amino acids from average contributions of functional groups

The experimental thermal neutron cross sections of the 20 proteinogenic amino acids have been measured over the incident-neutron energy range spanning from 1 meV to 10 keV and data have been interpreted using the multi-phonon expansion based on first-principles calculations. The scattering cross section, dominated by the incoherent inelastic contribution from the hydrogen atoms, can be rationalised in terms of the average contributions of different functional groups, thus neglecting their correlation. These results can be used for modelling the total neutron cross sections of complex organic systems like proteins, muscles, or human tissues from a limited number of starting input functions. This simplification is of crucial importance for fine-tuning of transport simulations used in medical applications, including boron neutron capture therapy as well as secondary neutrons-emission induced during proton therapy. Moreover, the parametrized neutron cross sections allow a better treatment of neutron scattering experiments, providing detailed sample self-attenuation corrections for a variety of biological and soft-matter systems.

Compact Method for Proton Range Verification Based on Coaxial Prompt Gamma-Ray Monitoring: a Theoretical Study

Range uncertainties in proton therapy hamper treatment precision. Prompt gamma-rays were suggested 16 years ago for real-time range verification, and have already shown promising results in clinical studies with collimated cameras. Simultaneously, alternative imaging concepts without collimation are investigated to reduce the footprint and price of current prototypes. In this manuscript, a compact range verification method is presented. It monitors prompt gamma-rays with a single scintillation detector positioned coaxially to the beam and behind the patient. Thanks to the solid angle effect, proton range deviations can be derived from changes in the number of gamma-rays detected per proton, provided that the number of incident protons is well known. A theoretical background is formulated and the requirements for a future proof-of-principle experiment are identified. The potential benefits and disadvantages of the method are discussed, and the prospects and potential obstacles for its use during patient treatments are assessed. The final milestone is to monitor proton range differences in clinical cases with a statistical precision of 1 mm, a material cost of 25000 USD and a weight below 10 kg. This technique could facilitate the widespread application of in vivo range verification in proton therapy and eventually the improvement of treatment quality.