Resolution recovery for Compton camera using origin ensemble algorithm

Abstract

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

Improved iterative reconstruction method for Compton imaging using median filter

“…In this study, β was set to 1 to accent the effect of median filter, and the size of the median mask was set to 7 × 7 (S1 Fig). The SOE approach to Compton imaging based on Markov chains was developed by Andreyev [14,15]. It does not require forward and backward projection operations.…”

“…Spatial resolution, several image quality indices, the semi-quantitative ability, and uniformity of MRP-EM were evaluated using an ellipsoid phantom to ensure that the proposed method can be used effectively in nuclear medicine. MRP-EM was compared with simple backprojection (BP), OS-EM, and the stochastic origin ensemble (SOE) method [13][14][15] as well as with the analytical method developed by Tomitani and Hirasawa [16][17][18].…”

Compton imaging reconstruction methods: a comparative performance study of direct back-projection, SOE, a new Bayesian algorithm and a new Compton inversion method applied to real data with Caliste

“…The SOE version with Resolution Recovery [9] constructs the back-projection cone randomly according to the spatial and spectral resolutions of the detector. In this work, we consider only the spatial resolution: for each interaction, we pick up a position randomly around the estimated position of the event, uniformly in a size of one detector pixel, and we randomly select the interaction depth, uniformly in the detector volume.…”

“…In this paper, we demonstrate the feasibility of Compton imaging with a single plane pixelated miniature detector, namely Caliste [8], by exploiting its advantages, especially its spectral and spatial resolution through Compton event selection. We study different Compton reconstruction algorithms to achieve the best localization performance with our detector: Direct Back-Projection (DBP) of the cones obtained by Compton kinematics, Stochastic Origin Ensemble with Resolution Recovery [9] and we introduce a new Bayesian approach and a new inversion of the Compton DBP.…”

Stochastic image reconstruction for a dual-particle imaging system

“…To achieve better convergence of the event origins, we used a modified version of a method proposed by Andreyev et al for resolution recovery [14]. Each projected cone was broadened by a fixed amount to account for the effects of energy, time, and spatial uncertainty.…”