A comparison of two prompt gamma imaging techniques with collimator-based cameras for range verification in proton therapy

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

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 - 4 PGs Emitted PGs ( 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 gap d e t = 0.98 × gap real ${{\rm gap}}_{det}=0.98 \times {{\rm gap}}_{{\rm real}}$ , where gap d e t ${{\rm gap}}_{det}$ and gap_real 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.

Proton range monitoring based on picosecond detection using a Cherenkov radiation detector: A Monte Carlo study

In this study, we analyzed the performance of a PbF₂ crystal-based detector at proton range monitoring with Monte Carlo simulations. The correlations between the depth-dose and Cherenkov profiles showed that the changes in the peak position in the Cherenkov profiles corresponded to the changes in the corresponding depth-dose profiles. Moreover, the deviations between the changes in the peak positions in the two curves were generally less than 2 mm. The results also showed that the actual proton range could be obtained using flight time information. When the proton energy was 160 MeV, the peak position detected in the Cherenkov profile detected was 14.83 cm with a flight time of 5.3-5.4 ns (starting from the time when protons were emitted), and the actual proton range in polymethyl-methacrylate was 15 cm. Therefore, the accuracy of the proton range measurements could be improved and the absolute range obtained by using the fast and time-sensitive characteristics of the proposed Cherenkov radiator.

Distal edge determination precision for a multi-slat prompt-gamma camera: A comprehensive simulation and optimization of the detection system

Multi-slat prompt-gamma camera is a promising tool for range monitoring during proton therapy. We report the results of a comprehensive simulation study analyzing the precision which is possible to reach with this camera in determination of the position of the distal edge of the Bragg peak. For the first time we include simulation of optical photons. The proton beam (single pencil beam, 130 MeV, 10 ns bunch period, total of 1·10⁸ protons) is interacting with a polymethyl methacrylate (PMMA) phantom, which is a cylinder of 200 mm in diameter and length. The prompt gamma rays generated in the phantom are collimated with a multi-slat collimator and detected using a combination of yttrium aluminum perovskite (YAP) scintillators, installed in the collimator apertures, and light sensors. Two scintillator packing schemes, with one and with two scintillator plates per aperture, are considered. The collimator configuration (the septal thickness, aperture and height), resulting in the best precision, is determined using two methods of detector optimization. Precision of 2.1 mm (full width at half maximum) in the edge position determination is demonstrated.

Classification of the source of treatment deviation in proton therapy using prompt-gamma imaging information

PURPOSE

Prompt-gamma imaging (PGI)-based range verification has been successfully implemented in clinical proton therapy recently and its sensitivity to detect treatment deviations is currently investigated. The cause of treatment deviations can be multiple - for example, computed tomography (CT)-based range prediction, patient setup, and anatomical changes. Hence, it would be beneficial, if PGI-based verification would not only detect a treatment deviation but would also be able to directly identify its most probable source. Here, we propose a heuristically derived decision tree approach to automatically classify the sources of range deviation in proton pencil-beam scanning (PBS) treatments of head and neck tumors based on range information obtained with a PGI slit camera.

MATERIALS AND METHODS

The decision tree model was iteratively generated on a training dataset of different anatomical complexities, for an anthropomorphic head phantom and patient CT data (planning and control CTs) from five patients. Different range prediction errors, setup changes and relevant and nonrelevant anatomical changes were introduced or derived from control CTs, summing up to a total of 98 training scenarios. Independent validation was performed for another 98 scenarios, derived solely from patient CT data of another seven patients. PBS head and neck treatment plans were generated for the nominal scenario. For all PBS spots in the investigated field, PGI profiles were simulated using a dedicated analytical model of the slit camera for the nominal as well as the different error scenarios. From comparison of PGI profiles for nominal and error scenarios, a spot-wise range shift after spot aggregation with a kernel of 7 mm sigma was determined for each error scenario. The heuristic approach includes a prefiltering of the most suitable PBS spots for PGI treatment verification. From the validation, the accuracy, sensitivity, and specificity of the model were determined.

RESULTS

A five-step consecutive filter was developed to preselect PBS spots. On average, 25% of spots (1044 spots) remained as input for the classification model. The derived heuristic decision tree model is based on five parameters: The coefficient of determination (R² ), the slope and intercept of the linear regression between PGI-derived range shifts and the respectively predicted proton ranges for the investigated PBS spots, as well as the average and standard deviation of the PGI-derived shifts. With this approach, 94 of 98 error scenarios could be classified correctly in validation (accuracy of 96%). A sensitivity and specificity of 100% and 86% were reached.

CONCLUSIONS

In this simulation study it was demonstrated that the source of a treatment deviation can be identified from simulated noiseless PGI information in head and neck tumor treatments with high sensitivity and specificity. The application, refinement, and evaluation of the approach on measured PGI data will be the next step to show the clinical feasibility of PGI-based error source classification.

Development of integrated prompt gamma imaging and positron emission tomography system for in vivo 3-D dose verification: a Monte Carlo study

An accurate knowledge of in vivo proton dose distribution is key to fully utilizing the potential advantages of proton therapy. Two representative indirect methods for in vivo range verification, namely, prompt gamma (PG) imaging and positron emission tomography (PET), are available. This study proposes a PG-PET system that combines the advantages of these two methods and presents detector geometry and background reduction techniques optimized for the PG-PET system. The characteristics of the secondary radiations emitted by a water phantom by interaction with a 150 MeV proton beam were analysed using Geant4.10.00, and the 2-D PG distributions were obtained and assessed for different detector geometries. In addition, the energy window (EW), depth-of-interaction (DOI), and time-of-flight (TOF) techniques are proposed as the background reduction techniques. To evaluate the performance of the PG-PET system, the 3-D dose distribution in the water phantom caused by two proton beams of energies 80 MeV and 100 MeV was verified using 16 optimal detectors. The thickness of the parallel-hole tungsten collimator of pitch 8 mm and width 7 mm was determined as 200 mm, and that of the GAGG scintillator was determined as 30 mm, by an optimization study. Further, 3-7 MeV and 2-7 MeV were obtained as the optimal EWs when the DOI and both the DOI and TOF techniques were applied for data processing, respectively; the detector performances were improved by about 38% and 167%, respectively, compared with that when applying only the 3-5 MeV EW. In this study, we confirmed that the PG distribution can be obtained by simply combining the 2-D parallel hole collimator and the PET detector module. In the future, we will develop an accurate 3-D dose evaluation technique using deep learning algorithms based on the image sets of dose, PG, and PET distributions for various proton energies.