Stochastic Origin Ensemble Algorithm Research Articles

GPU Accelerated Stochastic Origin Ensemble Method With List-Mode Data for Compton Camera Imaging in Proton Therapy

Proton beam therapy has the advantages over other forms of radiation therapy because it does less damage to healthy tissue while treating cancerous tissue. These advantages arise from the high dose deposition at the end of the proton beam which is known as the Bragg peak. This paper proposes a new architecture for graphics processing unit (GPU)-accelerated stochastic origin ensemble (SOE) algorithm, eventually achieves a great acceleration and allows to reconstruct images of the gamma radiation produced by proton beam in real time. The object of this paper is to improve the run time of SOE algorithm for improving the accuracy of the proton therapy. In previous studies, it is difficult to reconstruct images of prompt gamma (PG) ray in real time in proton therapy. After analyzing the structure of the SOE algorithm, we correspond each gamma ray detected by Compton camera (CC) to each thread in GPU, initialize the origin cones in GPU, and use data structures which are suitable for parallelization, shared memory, and random number lists to speed up parallel program. To evaluate the proposed method, we use the parallel SOE algorithm to reconstruct the sources with the CC imaging system, which is simulated by Geant4. The parallel program on the GPU eventually achieves a 25× speedup over serial CPU run, reconstructing images of gamma radiation in 0.4 s. In the end, we also accelerate the Monte Carlo-based back projection (MC-BP) algorithm, the parallel program achieves a 79× speedup over serial CPU run.

Motion compensation using origin ensembles in awake small animal positron emission tomography

In emission tomographic imaging, the stochastic origin ensembles algorithm provides unique information regarding the detected counts given the measured data. Precision in both voxel and region-wise parameters may be determined for a single data set based on the posterior distribution of the count density allowing uncertainty estimates to be allocated to quantitative measures. Uncertainty estimates are of particular importance in awake animal neurological and behavioral studies for which head motion, unique for each acquired data set, perturbs the measured data. Motion compensation can be conducted when rigid head pose is measured during the scan. However, errors in pose measurements used for compensation can degrade the data and hence quantitative outcomes. In this investigation motion compensation and detector resolution models were incorporated into the basic origin ensembles algorithm and an efficient approach to computation was developed. The approach was validated against maximum liklihood—expectation maximisation and tested using simulated data. The resultant algorithm was then used to analyse quantitative uncertainty in regional activity estimates arising from changes in pose measurement precision. Finally, the posterior covariance acquired from a single data set was used to describe correlations between regions of interest providing information about pose measurement precision that may be useful in system analysis and design. The investigation demonstrates the use of origin ensembles as a powerful framework for evaluating statistical uncertainty of voxel and regional estimates. While in this investigation rigid motion was considered in the context of awake animal PET, the extension to arbitrary motion may provide clinical utility where respiratory or cardiac motion perturb the measured data.

Evaluation of a stochastic reconstruction algorithm for use in Compton camera imaging and beam range verification from secondary gamma emission during proton therapy

In this paper, we study the feasibility of using the stochastic origin ensemble (SOE) algorithm for reconstructing images of secondary gammas emitted during proton radiotherapy from data measured with a three-stage Compton camera. The purpose of this study was to evaluate the quality of the images of the gamma rays emitted during proton irradiation produced using the SOE algorithm and to measure how well the images reproduce the distal falloff of the beam. For our evaluation, we performed a Monte Carlo simulation of an ideal three-stage Compton camera positioned above and orthogonal to a proton pencil beam irradiating a tissue phantom. Scattering of beam protons with nuclei in the phantom produces secondary gamma rays, which are detected by the Compton camera and used as input to the SOE algorithm. We studied the SOE reconstructed images as a function of the number of iterations, the voxel probability parameter, and the number of detected gammas used by the SOE algorithm. We quantitatively evaluated the capabilities of the SOE algorithm by calculating and comparing the normalized mean square error (NMSE) of SOE reconstructed images. We also studied the ability of the SOE reconstructed images to predict the distal falloff of the secondary gamma production in the irradiated tissue. Our results show that the images produced with the SOE algorithm converge in ∼10 000 iterations, with little improvement to the image NMSE for iterations above this number. We found that the statistical noise of the images is inversely proportional to the ratio of the number of gammas detected to the SOE voxel probability parameter value. In our study, the SOE predicted distal falloff of the reconstructed images agrees with the Monte Carlo calculated distal falloff of the gamma emission profile in the phantom to within ±0.6 mm for the positions of maximum emission (100%) and 90%, 50% and 20% distal falloff of the gamma emission profile. We conclude that the SOE algorithm is an effective method for reconstructing images of a proton pencil beam from the data collected by an ideal Compton camera and that these images accurately model the distal falloff of secondary gamma emission during proton irradiation.

SU‐E‐T‐349: Evaluating a Stochastic Reconstruction Algorithm for Compton Camera Based Prompt Gamma Imaging during Proton Therapy

Purpose: Prompt gamma photons emitted during proton radiotherapy may provide a method to measure dose delivery and range. The utility of this method depends on how well the gammas can be detected and reconstructed. The purpose of this study is to demonstrate the feasibility of the stochastic origin ensemble (SOE) algorithm for reconstructing images of the origins of prompt gammas emitted during proton therapy and to introduce a metric for quantifying the accuracy of this image. Methods: Using Geant4 we simulated a Compton camera detecting prompt gamma emission from proton irradiated tissue phantoms. The information recorded by the simulated detector is used as the input to the SOE reconstruction algorithm, which iteratively determines the likely gamma emission origin. We introduce a metric to quantify the accuracy of the reconstruction and use the metric to provide a quantitative analysis of the SOE reconstructions. Results: We show both qualitatively and quantitatively that the SOE algorithm can reconstruct images which accurately predict the origins of prompt gammas emitted during proton therapy. We also show that the distal falloff of gamma production based on the reconstructed image agrees with the distal falloff in Monte Carlo calculated gamma origin distribution to within 1 mm for both the 50% and 20% regions. Conclusions: The SOE algorithm was able to reconstruct usable images of prompt gamma emission during proton irradiation. We believe these results justify further investigation of this technique for in‐vivo prompt gamma imaging.This work was supported by Award Number R21CA137362 from the National Cancer Institute.

Fast image reconstruction for Compton camera using stochastic origin ensemble approach

Compton camera has been proposed as a potential imaging tool in astronomy, industry, homeland security, and medical diagnostics. Due to the inherent geometrical complexity of Compton camera data, image reconstruction of distributed sources can be ineffective and/or time-consuming when using standard techniques such as filtered backprojection or maximum likelihood-expectation maximization (ML-EM). In this article, the authors demonstrate a fast reconstruction of Compton camera data using a novel stochastic origin ensembles (SOE) approach based on Markov chains. During image reconstruction, the origins of the measured events are randomly assigned to locations on conical surfaces, which are the Compton camera analogs of lines-of-responses in PET. Therefore, the image is defined as an ensemble of origin locations of all possible event origins. During the course of reconstruction, the origins of events are stochastically moved and the acceptance of the new event origin is determined by the predefined acceptance probability, which is proportional to the change in event density. For example, if the event density at the new location is higher than in the previous location, the new position is always accepted. After several iterations, the reconstructed distribution of origins converges to a quasistationary state which can be voxelized and displayed. Comparison with the list-mode ML-EM reveals that the postfiltered SOE algorithm has similar performance in terms of image quality while clearly outperforming ML-EM in relation to reconstruction time. 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.