Abstract
A common approach to improving the spatial resolution of small animal PET scanners is to reduce the size of scintillation crystals and/or employ high resolution pixellated semiconductor detectors. The large number of detector elements results in the system matrix—an essential part of statistical iterative reconstruction algorithms—becoming impractically large. In this paper, we propose a methodology for system matrix modelling which utilises a virtual single-layer detector ring to greatly reduce the size of the system matrix without sacrificing precision. Two methods for populating the system matrix are compared; the first utilises a geometrically-derived system matrix based on Siddon’s ray tracer method with the addition of an accurate detector response function, while the second uses Monte Carlo simulation to populate the system matrix. The effectiveness of both variations of the proposed technique is demonstrated via simulations of PETiPIX, an ultra high spatial resolution small animal PET scanner featuring high-resolution DoI capabilities, which has previously been simulated and characterised using classical image reconstruction methods. Compression factors of and are achieved using this methodology for the system matrices produced using the geometric and Monte Carlo-based approaches, respectively, requiring a total of 0.5–1.2 GB of memory-resident storage. Images reconstructed from Monte Carlo simulations of various point source and phantom models, produced using system matrices generated via both geometric and simulation methods, are used to evaluate the quality of the resulting system matrix in terms of achievable spatial resolution and the CRC, CoV and CW-SSIM index image quality metrics. The Monte Carlo-based system matrix is shown to provide the best image quality at the cost of substantial one-off computational effort and a lower (but still practical) compression factor. Finally, a straightforward extension of the virtual ring method to a three dimensional virtual cylinder is demonstrated using a 3D DoI PET scanner.
Highlights
Positron emission tomography (PET) is a functional imaging technique used in clinical diagnostic applications and biomedical research
For a reconstructed image with a total of I pixels generated using data from a scanner with J possible detector pairs or possible lines of response (LoR), each element pij of the system matrix represents the probability that the annihilation of a positron emitted from the ith image pixel (i ∈ [1, 2, . . . I]) is detected by the jth (j ∈ [1, 2, . . . J]) detector pair
We present a methodology for achieving very high rates of system matrix compression for PET systems featuring detectors with extremely fine granularity in the radial dimension
Summary
Positron emission tomography (PET) is a functional imaging technique used in clinical diagnostic applications and biomedical research. Statistical iterative image reconstruction techniques such as the maximum-likelihood expectation-maximisation (MLEM) algorithm [Shepp and Vardi, 1982] and the Bayesian reconstruction method [Mumcuoglu et al, 1996] have largely supplanted classical analytic image reconstruction algorithms such as filtered backprojection (FBP) [Kak and Slaney, 2001]. These iterative algorithms have been shown to significantly improve the quality of the reconstructed image at the cost of increased computational complexity and memory requirements in comparison to FBP[Tohme and Qi, 2009]. Several approaches have been developed for obtaining an accurate estimate of the system matrix for a given PET system, including analytical calculation, experimental measurements and Monte Carlo simulations
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
