Use of a LYSO-based Compton camera for prompt gamma range verification in proton therapy

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.

Development of a two-layer dense-pixel LYSO Compton camera prototype for prompt gamma imaging

Objective.Lutetium-yttrium orthosilicate (LYSO)-based Compton camera (CC) has been proposed for prompt gamma imaging due to its high detection efficiency and position resolution. However, very few LYSO CC prototypes have been built and used for practical evaluation. In this study, we built a lightweight dense-pixel silicon photomultiplier-based two-layer LYSO CC prototype for future prompt gamma imaging.Approach.We attempt the first-ever effort to use the double-encoding with the thick light guide and coding circuit structure for 46 × 46 dense-pixel LYSO detectors construction and use pixel segmentation based on centroid mapping to obtain 4232 spectral calibrations. We also present a framework for list-mode projection data acquisition based on the decoding of the time series data obtained by data acquisition card in this study. Finally, the standard source calibration, ring-like²²Na source with non-uniform intensity, and mixed point-like source with a wide energy spectrum experiments were implemented to evaluate the resolution metrics and imaging performance of the prototype.Main results.The lateral position resolution of the prototype was 1 mm, and the maximum measurement deviation is 2.5 mm and 5 mm in the depth direction for the scatterer and absorber, respectively. In the experiments, the measured energy resolution was 9.63% @ 1.33 MeV for the scatterer and 10.8% @ 1.33 MeV for the absorber. And the detection efficiency of the prototype for a spherical⁶⁰Co source with a diameter of 2.8 mm at 10 cm far was 5.7 × 10^-3@ 1.33 MeV and the full width at half maximum of the reconstruction was 5.5 mm. Besides, the spatial position offset within 2 mm of the radioactive source at 10 cm can be distinguished.Signification.The developed two-layer dense-pixel LYSO CC contributes to incorporating Compton imaging techniques for prompt gamma detection and multiple energy sources into nuclear medical imaging.

Machine learning-based event recognition in SiFi Compton camera imaging for proton therapy monitoring

Objective. Online monitoring of dose distribution in proton therapy is currently being investigated with the detection of prompt gamma (PG) radiation emitted from a patient during irradiation. The SiPM and scintillation Fiber based Compton Camera (SiFi-CC) setup is being developed for this aim. Approach. A machine learning approach to recognize Compton events is proposed, reconstructing the PG emission profile during proton therapy. The proposed method was verified on pseudo-data generated by a Geant4 simulation for a single proton beam impinging on a polymethyl methacrylate (PMMA) phantom. Three different models including the boosted decision tree (BDT), multilayer perception (MLP) neural network, and k-nearest neighbour (k-NN) were trained using 10-fold cross-validation and then their performances were assessed using the receiver operating characteristic (ROI) curves. Subsequently, after event selection by the most robust model, a software based on the List-Mode Maximum Likelihood Estimation Maximization (LM-MLEM) algorithm was applied for the reconstruction of the PG emission distribution profile. Main results. It was demonstrated that the BDT model excels in signal/background separation compared to the other two. Furthermore, the reconstructed PG vertex distribution after event selection showed a significant improvement in distal falloff position determination. Significance. A highly satisfactory agreement between the reconstructed distal edge position and that of the simulated Compton events was achieved. It was also shown that a position resolution of 3.5 mm full width at half maximum (FWHM) in distal edge position determination is feasible with the proposed setup.

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.

The effects of Compton camera data acquisition and readout timing on PG imaging for proton range verification

The purpose of this study was to determine how the characteristics of the data acquisition (DAQ) electronics of a Compton camera (CC) affect the quality of the recorded prompt gamma (PG) interaction data and the reconstructed images, during clinical proton beam delivery. We used the Monte-Carlo-plus-Detector-Effect (MCDE) model to simulate the delivery of a 150 MeV clinical proton pencil beam to a tissue-equivalent plastic phantom. With the MCDE model we analyzed how the recorded PG interaction data changed as two characteristics of the DAQ electronics of a CC were changed: (1) the number of data readout channels; and (2) the active charge collection, readout, and reset time. As the proton beam dose rate increased, the number of recorded PG single-, double-, and triple-scatter events decreased by a factor of 60× for the current DAQ configuration of the CC. However, as the DAQ readout channels were increased and the readout/reset timing decreased, the number of recorded events decreased by <5× at the highest clinical dose rate. The increased number of readout channels and reduced readout/reset timing also resulted in higher quality recorded data. That is, a higher percentage of the recorded double- and triple-scatters were "true" events (caused by a single incident gamma) and not "false" events (caused by multiple incident gammas). The increase in the number and the quality of recorded data allowed higher quality PG images to be reconstructed even at the highest clinical dose rates.

GaN-Based Readout Circuit System for Reliable Prompt Gamma Imaging in Proton Therapy

Prompt gamma imaging is one of the emerging techniques used in proton therapy for in-vivo range verification. Prompt gamma signals are generated during therapy due to the nuclear interaction between beam particles and nuclei of the tissue that is detected and processed in order to obtain the position and energy of the event so that the benefits of Bragg’s peak can be fully utilized. This work aims to develop a gallium nitride (GaN)-based readout system for position-sensitive detectors. An operational amplifier is the module most used in such a system to process the detector signal, and a GaN-based operational amplifier (OPA) is designed and simulated in LTSpice. The designed circuit had an open-loop gain of 70 dB and a unity gain frequency of 34 MHz. The slew rate of OPA was 20 V/μs and common mode rejection ratio was 84.2 dB. A simulation model of the readout circuit system using the GaN-based operational amplifier was also designed, and the result showed that the system can successfully process the prompt gamma signals. Due to the radiation hardness of GaN devices, the readout circuit system is expected to be more reliable than its silicon counterpart.

Accuracy of using high-energy prompt gamma to verify proton beam range with a Compton camera: A Monte Carlo simulation study

Prompt gamma (PG) rays emitted during proton therapy has been used for proton range verification. Because high-energy PG emission is well correlated to the Bragg peak (BP), high-energy PG rays are well-suited for proton range verification. However, the low production and detection of high-energy PG rays often lead to inaccurate BP position estimates. The aim of this study is to improve the BP position estimates obtained from high-energy PG rays. We propose a BP position estimation method based on the local maximum closest to the distal fall-off region. We present the results of Monte Carlo simulations in which a water phantom was irradiated with a proton beam. Our results show that the BP position estimated from the 6.13 MeV PG rays can be improved using the proposed position estimation method. Moreover, the 6.92 and 7.12 MeV PG rays can be used for predicting the BP position. However, the accuracy of the BP position estimation decreases with decreasing tissue oxygen levels. We also found that the subtraction of the PG images of 6.13 MeV from those of 6.92 and 7.12 MeV can be used to predict the BP position with a mean accuracy of < 2 mm. The accurate estimation of the BP position can be achieved using different high-energy PG rays, but factors including position estimation, irradiated tissue and event selection should be carefully taken into account.

Astatine-211 imaging by a Compton camera for targeted radiotherapy

Astatine-211 is a promising radionuclide for targeted radiotherapy. It is required to image the distribution of targeted radiotherapeutic agents in a patient's body for optimization of treatment strategies. We proposed to image ²¹¹At with high-energy photons to overcome some problems in conventional planar or single-photon emission computed tomography imaging. We performed an imaging experiment of a point-like ²¹¹At source using a Compton camera, and demonstrated the capability of imaging ²¹¹At with the high-energy photons for the first time.

A low-count reconstruction algorithm for Compton-based prompt gamma imaging

The Compton camera is an imaging device which has been proposed to detect prompt gammas (PGs) produced by proton-nuclear interactions within tissue during proton beam irradiation. Compton-based PG imaging has been developed to verify proton ranges because PG rays, particularly characteristic ones, have strong correlations with the distribution of the proton dose. However, accurate image reconstruction from characteristic PGs is challenging because the detector efficiency and resolution are generally low. Our previous study showed that point spread functions can be incorporated into the reconstruction process to improve image resolution. In this study, we proposed a low-count reconstruction algorithm to improve the image quality of a characteristic PG emission by pooling information from other characteristic PG emissions. PGs were simulated from a proton beam irradiated on a water phantom, and a two-stage Compton camera was used for PG detection. The results show that the image quality of the reconstructed characteristic PG emission is improved with our proposed method in contrast to the standard reconstruction method using events from only one characteristic PG emission. For the 4.44 MeV PG rays, both methods can be used to predict the positions of the peak and the distal falloff with a mean accuracy of 2 mm. Moreover, only the proposed method can improve the estimated positions of the peak and the distal falloff of 5.25 MeV PG rays, and a mean accuracy of 2 mm can be reached.