Compton-based prompt gamma imaging using ordered origin ensemble algorithm with resolution recovery in proton therapy

Hybrid PET/Compton-camera imaging: an imager for the next generation

Compton cameras can offer advantages over gamma cameras for some applications, since they are well suited for multitracer imaging and for imaging high-energy radiotracers, such as those employed in radionuclide therapy. While in conventional clinical settings state-of-the-art Compton cameras cannot compete with well-established methods such as PET and SPECT, there are specific scenarios in which they can constitute an advantageous alternative. The combination of PET and Compton imaging can benefit from the improved resolution and sensitivity of current PET technology and, at the same time, overcome PET limitations in the use of multiple radiotracers. Such a system can provide simultaneous assessment of different radiotracers under identical conditions and reduce errors associated with physical factors that can change between acquisitions. Advances are being made both in instrumentation developments combining PET and Compton cameras for multimodal or three-gamma imaging systems, and in image reconstruction, addressing the challenges imposed by the combination of the two modalities or the new techniques. This review article summarizes the advances made in Compton cameras for medical imaging and their combination with PET.

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.

Technical note: Rapid and high-resolution deep learning-based radiopharmaceutical imaging with 3D-CZT Compton camera and sparse projection data

BACKGROUND

The Compton camera (CC) has great potential in nuclear medicine imaging due to the high detection efficiency and the ability to simultaneously detect multi-energy radioactive sources. However, the finite resolution of the detectors will degrade the images that the real-world CC can obtain. Besides, the CC sometimes can be limited by the detection efficiency, leading to difficulty in using sparse projection data to realize high-resolution reconstruction with short-time measurement, which limits its clinical application for real-time or rapid radiopharmaceutical imaging.

PURPOSE

To overcome the difficulty and promote the usage of the CC in radiopharmaceutical imaging, we present a deep learning (DL)-based CC reconstruction method to realize rapid and high-resolution imaging with short-time measurement.

METHODS

We developed a DL-based algorithm MCBP-CCnet via Monte Carlo sampling-based back projection and a dedicated convolutional neural network, called CC-Net, to realize the rapid and high-resolution reconstruction with sparse projection data. A CC prototype based on a single three-dimensional position-sensitive CdZnTe (3D-CZT) detector was used to demonstrate the feasibility of our proposed method. The simulations and experiments of radiopharmaceutical imaging used the 3D-CZT CC and [¹⁸ F]NaF. A 3D-printing mouse phantom was also further used to evaluate the performance of the proposed method in animal molecular imaging.

RESULTS

The simulation and experimental results showed that the proposed method could realize the images reconstruction within 5 s for list-mode projection data and realized a rapid reconstruction within 35 s for experimental radiopharmaceutical imaging based on the 3D-printing mouse phantom, as well as realized the high-resolution imaging with an accuracy of within 0.78 mm in terms of the sparse projection data that only contained hundreds of events. Besides, the deviations between the reconstructed radiative activities and the exact values were less than 1.51%.

CONCLUSION

The results demonstrated that the proposed method could realize the rapid and high-resolution CC reconstruction with sparse projection data obtained by the 3D-CZT CC and realize the high-resolution radiopharmaceutical imaging. The study in this paper also demonstrated the potential and feasibility of future applications of a 3D-CZT CC for real-time high-resolution radiopharmaceutical imaging with short-time measurement.

Joint image reconstruction algorithm in Compton cameras

Objective. To demonstrate the benefits of using an joint image reconstruction algorithm based on the List Mode Maximum Likelihood Expectation Maximization that combines events measured in different channels of information of a Compton camera. Approach. Both simulations and experimental data are employed to show the algorithm performance. Main results. The obtained joint images present improved image quality and yield better estimates of displacements of high-energy gamma-ray emitting sources. The algorithm also provides images that are more stable than any individual channel against the noisy convergence that characterizes Maximum Likelihood based algorithms. Significance. The joint reconstruction algorithm can improve the quality and robustness of Compton camera images. It also has high versatility, as it can be easily adapted to any Compton camera geometry. It is thus expected to represent an important step in the optimization of Compton camera imaging.

Compton imaging for medical applications

Compton imaging exploits inelastic scattering, known as Compton scattering, using a Compton camera consisting of a scatterer detector in the front layer and an absorber detector in the back layer. This method was developed for astronomy, and in recent years, research and development for environmental and medical applications has been actively conducted. Compton imaging can discriminate gamma rays over a wide energy range from several hundred keV to several MeV. Therefore, it is expected to be applied to the simultaneous imaging of multiple nuclides in nuclear medicine and prompt gamma ray imaging for range verification in particle therapy. In addition, multiple gamma coincidence imaging is expected to be realized, which allows the source position to be determined from a single coincidence event using nuclides that emit multiple gamma rays simultaneously, such as nuclides that emit a single gamma ray simultaneously with positron decay. This review introduces various efforts toward the practical application of Compton imaging in the medical field, including in vivo studies, and discusses its prospects.

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.

Monte Carlo evaluation of a LYSO-based Compton camera using two origin ensemble algorithms with resolution recovery

PURPOSE

Due to the lack of depth-of-interaction information, a Compton camera made of lutetium-yttrium orthosilicate (LYSO) crystals suffers from poor spatial resolution, which may lead to an unreliable range verification in proton therapy. The aim of this study is to evaluate the performance of a LYSO-based Compton camera using the origin ensemble algorithm with resolution recovery (OE-RR). We also proposed a regularized version of OE-RR called ROE-RR.

METHODS

We simulated a two-layer LYSO-based Compton camera which was used to detect prompt gammas (PGs) produced by a proton beam irradiated on a water phantom. PG images reconstructed by the OE-RR algorithm were evaluated and compared with those reconstructed by the proposed ROE-RR algorithm.

RESULTS

Our simulated results show that both the OE-RR and ROE-RR algorithms could provide an accurate estimate of the Bragg peak position, with a mean positioning error of 2.5 mm. Compared to the OE-RR algorithm, the proposed ROE-RR algorithm is less sensitive with respect to initial conditions and requires less iterations for converging to equilibrium. More importantly, the proposed ROE-RR algorithm could provide better image quality than the OE-RR algorithm, especially in low-count data.

CONCLUSIONS

For LYSO-based Compton cameras, using a resolution-recovery image reconstruction algorithm is essential for reliable range verification.

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

The applicability extent of hadron therapy for tumor treatment is currently limited by the lack of reliable online monitoring techniques. An active topic of investigation is the research of monitoring systems based on the detection of secondary radiation produced during treatment. MACACO, a multi-layer Compton camera based on LaBr₃ scintillator crystals and SiPMs, is being developed at IFIC-Valencia for this purpose. This work reports the results obtained from measurements of a 150 MeV proton beam impinging on a PMMA target. A neural network trained on Monte Carlo simulations is used for event selection, increasing the signal to background ratio before image reconstruction. Images of the measured prompt gamma distributions are reconstructed by means of a spectral reconstruction code, through which the 4.439 MeV spectral line is resolved. Images of the emission distribution at this energy are reconstructed, allowing calculation of the distal fall-off and identification of target displacements of 3 mm.

Computational model for detector timing effects in Compton-camera based prompt-gamma imaging for proton radiotherapy

This paper describes a realistic simulation of a Compton-camera (CC) based prompt-gamma (PG) imaging system for proton range verification for a range of clinical dose rates, and its comparison to PG measured data with a pre-clinical CC. We used a Monte Carlo plus Detector Effects (MCDE) model to simulate the production of prompt gamma-rays (PG) and their energy depositions in the CC. With Monte Carlo, we simulated PG emission resulting from irradiation of a high density polyethylene phantom with a 150 MeV proton pencil beam at dose rates of 5.0 × 10⁸, 2.6 × 10⁹, and 4.6 × 10⁹ p⁺ s^-1. Realistic detector timing effects (e.g. delayed triggering time, event-coincidence, dead time, etc,) were added in post-processing to allow for flexible count rate variations. We acquired PG emission measurements with our pre-clinical CC during irradiation with a clinical 150 MeV proton pencil beam at the same dose rates. For simulations and measurements, three primary changes could be seen in the PG emission data as the dose rate increased: (1) reduction in the total number of detected events due to increased dead-time percentage; (2) increase in false-coincidence events (i.e. multiple PGs interacting, rather than a single PG scatter); and (3) loss of distinct PG emission peaks in the energy spectrum. We used the MCDE model to estimate the quality of our measured PG data, primarily with regards to true and false double-scatters and triple-scatters recorded by the CC. The simulation results showed that of the recorded double-scatter PG interactions 22%, 57%, and 70% were false double-scatters and for triple-scatter interactions 3%, 21%, and 35% were false events at 5.0 × 10⁸, 2.6 × 10⁹, and 4.6 × 10⁹ p⁺ s^-1, respectively. These false scatter events represent noise in the data, and the high percentage of these events in the data represents a major limitation in our ability to produce usable PG images with our prototype CC.