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