In this study, the authors have implemented and tested a new image reconstruction algorithm for the Compton camera based on the stochastic origin ensembles with Markov chains. The algorithm uses list-mode data, is parallelizable, and can be used for any Compton camera geometry. SOE algorithm clearly outperforms list-mode ML-EM for simple Compton camera geometry in terms of reconstruction time. The difference in computational time will be much larger when full Compton camera system model, including resolution recovery, is implemented and realistic Compton camera geometries are used. It was also shown in this article that while correctly reconstructing the relative distribution of the activity in the object, the SOE algorithm tends to underestimate the intensity values and increase variance in the images; improvements to the SOE reconstruction algorithm will be considered in future work.

Positron emission tomography (PET) is widely recognized as a highly effective functional imaging modality. Unfortunately, standard PET cannot be used for dual-isotope imaging (which would allow for simultaneous investigation of two different biological processes), because positron-electron annihilation products from different tracers are indistinguishable in terms of energy. Methods that have been proposed for dual-isotope PET rely on differences in half-lives of the participating isotopes; these approaches, however, require making assumptions concerning kinetic behavior of the tracers and may not lead to optimal results. In this paper we propose a novel approach for dual-isotope PET and investigate its performance using GATE simulations. Our method requires one of the two radioactive isotopes to be a pure positron emitter and the second isotope to emit an additional high-energy gamma in a cascade simultaneously with positron emission. Detection of this auxiliary prompt gamma in coincidence with the annihilation event allows us to identify the corresponding 511 keV photon pair as originating from the same isotope. Two list-mode datasets are created: a primary dataset that contains all detected 511 keV photon pairs from both isotopes, and a second, tagged (much smaller) dataset that contains only those PET events for which a coincident prompt gamma has also been detected. An image reconstructed from the tagged dataset reflects the distribution of the second positron-gamma radiotracer and serves as a prior for the reconstruction of the primary dataset. Our preliminary simulation study with partially overlapping (18)F/(22)Na and (18)F/(60)Cu radiotracer distributions showed that in these two cases the dual-isotope PET method allowed for separation of the two activity distributions and recovered total activities with relative errors of about 5%.

Purpose: Compton cameras (CCs) use electronic collimation to reconstruct the images of activity distribution. Although this approach can greatly improve imaging efficiency, due to complex geometry of the CC principle, image reconstruction with the standard iterative algorithms, such as ordered subset expectation maximization (OSEM), can be very time-consuming, even more so if resolution recovery (RR) is implemented. We have previously shown that the origin ensemble (OE) algorithm can be used for the reconstruction of the CC data. Here we propose a method of extending our OE algorithm to include RR. Methods: To validate the proposed algorithm we used Monte Carlo simulations of a CC composed of multiple layers of pixelated CZT detectors and designed for imaging small animals. A series of CC acquisitions of small hot spheres and the Derenzo phantom placed in air were simulated. Images obtained from (a) the exact data, (b) blurred data but reconstructed without resolution recovery, and (c) blurred and reconstructed with resolution recovery were compared. Furthermore, the reconstructed contrast-to-background ratios were investigated using the phantom with nine spheres placed in a hot background.Results: Our simulations demonstrate that the proposed method allows for the recovery of the resolution loss that is due to imperfect accuracy of event detection. Additionally, tests of camera sensitivity corresponding to different detector configurations demonstrate that the proposed CC design has sensitivity comparable to PET. When the same number of events were considered, the computation time per iteration increased only by a factor of 2 when OE reconstruction with the resolution recovery correction was performed relative to the original OE algorithm. We estimate that the addition of resolution recovery to the OSEM would increase reconstruction times by 2-3 orders of magnitude per iteration. Conclusions: The results of our tests demonstrate the improvement of image resolution provided by the OE reconstructions with resolution recovery. The quality of images and their contrast are similar to those obtained from the OE reconstructions from scans simulated with perfect energy and spatial resolutions. C