Determination of optimal collimation parameters for a rotating slat collimator system: a system matrix method using ML-EM

Nowadays, Single Photon imaging has become an essential part of molecular imaging and nuclear medicine. Whether to establish a diagnosis or in the therapeutic monitoring, this modality presents performance that continues to improve. For over 50 years, several collimators have been proposed. Mainly governed by collimation parameters, the resolution-sensitivity trade-off is the factor determining the collimator the most suitable for an intended study. One alternative to the common approaches is the rotating slat collimator (RSC). In the present study, we are aiming at developing a preclinical system equipped with a RSC dedicated to mice and rats imaging, which requires both high sensitivity and spatial resolution. We investigated the resolution-sensitivity trade-offs obtained by varying different collimation parameters: (i) the slats height (H), and (ii) the gap between two consecutive slats (g), considering different intrinsic spatial resolutions. One system matrix was generated for each set of collimation parameters (H,g). Spatial resolutions, Signal-to-Noise Ratio (SNR) and sensitivity obtained for all the set of collimation parameters (H,g) were measured in the 2D projections reconstructed with ML-EM. According to our results, 20 mm high slats and a 1 mm gap were chosen as a good RSC candidate for a preclinical detection module. This collimator will ensure a sensitivity greater than 0.2% and a system spatial resolution below 1 mm, considering an intrinsic spatial resolution below 0.8 mm.

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 well-known 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-to-noise ratio (differences <10%), or quality of image.