Assessment of a fast generated analytical matrix for rotating slat collimation iterative reconstruction: a possible method to optimize the collimation profile.

In SPECT imaging, improvement or deterioration of performance is mostly due to collimator design. Classical SPECT systems mainly use parallel hole or pinhole collimators. Rotating slat collimators (RSC) can be an interesting alternative to optimize the tradeoff between detection efficiency and spatial resolution. The present study was conducted using a RSC system for small animal imaging called CLiR. The CLiR system was used in planar mode only. In a previous study, planar 2D projections were reconstructed using the wellknown filtered backprojection algorithm (FBP). In this paper, we investigated the use of the statistical reconstruction algorithm maximum likelihood expectation maximization (MLEM) to reconstruct 2D images with the CLiR system using a probability matrix calculated using an analytic approach. The primary objective was to propose a method to quickly generate a light system matrix, which facilitates its handling and storage, while providing accurate and reliable performance. Two other matrices were calculated using GATE Monte Carlo simulations to investigate the performance obtained using the matrix calculated analytically. The first matrix calculated using GATE took all the physics processes into account, where the second did not consider for the scattering, as the analytical matrix did not take this physics process into account either. 2D images were reconstructed using FBP and MLEM with the three different probability matrices. Both simulated and experimental data were used. A comparative study of these images was conducted using different metrics: the modulation transfert function, the signal-to-noise ratio and quantification measurement. All the results demonstrated the suitability of using a probability matrix calculated analytically. It provided similar results in terms of spatial resolution (about 0.6 mm with differences <5%), signal-tonoise ratio (differences <10%), or quality of image.