Resolution recovery for Compton camera using origin ensemble algorithm

• Published in: Medical physics (Lancaster) 2016-08, Vol.43 (8), p.4866-4876
• Main Authors: Andreyev, A., Celler, A., Ozsahin, I., Sitek, A.
• Format: Article
• Language: English
• Subjects: 60 APPLIED LIFE SCIENCES; ALGORITHMS; Biological material, e.g. blood, urine; Haemocytometers; BIOLOGICAL RECOVERY; BIOMEDICAL RADIOGRAPHY; CAMERAS; Compton camera; Compton scattering; COMPUTERIZED SIMULATION; DIAGNOSTIC IMAGING (IONIZING AND NON-IONIZING); Digital computing or data processing equipment or methods, specially adapted for specific applications; expectation‐maximisation algorithm; Gamma Rays; Image data processing or generation, in general; Image detection systems; IMAGE PROCESSING; Image Processing, Computer-Assisted - methods; image reconstruction; image resolution; Image sensors; ITERATIVE METHODS; Measuring half‐life of a radioactive substance; Measuring radioactive content of objects, e.g. contamination (whole‐body counters G01T011/63); medical image processing; Medical image reconstruction; MONTE CARLO METHOD; Monte Carlo methods; Monte Carlo simulations; Numerical approximation and analysis; origin ensemble; phantoms; Photon scattering; Photons; POSITRON COMPUTED TOMOGRAPHY; positron emission tomography; Positron emission tomography (PET); Probability theory, stochastic processes, and statistics; RADIATION PROTECTION AND DOSIMETRY; Reconstruction; resolution recovery; Scintigraphy; Signal-To-Noise Ratio; single photon emission computed tomography; Single photon emission computed tomography (SPECT); SPATIAL RESOLUTION
• ISSN: 0094-2405; 2473-4209
• DOI: 10.1118/1.4959551

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.