Maximum-likelihood scintillation detection for EM-CCD based gamma cameras

Abstract

Gamma cameras based on charge-coupled devices (CCDs) coupled to continuous scintillation crystals can combine a good detection efficiency with high spatial resolutions with the aid of advanced scintillation detection algorithms. A previously developed analytical multi-scale algorithm (MSA) models the depth-dependent light distribution but does not take statistics into account. Here we present and validate a novel statistical maximum-likelihood algorithm (MLA) that combines a realistic light distribution model with an experimentally validated statistical model. The MLA was tested for an electron multiplying CCD optically coupled to CsI(Tl) scintillators of different thicknesses. For (99m)Tc imaging, the spatial resolution (for perpendicular and oblique incidence), energy resolution and signal-to-background counts ratio (SBR) obtained with the MLA were compared with those of the MSA. Compared to the MSA, the MLA improves the energy resolution by more than a factor of 1.6 and the SBR is enhanced by more than a factor of 1.3. For oblique incidence (approximately 45°), the depth-of-interaction corrected spatial resolution is improved by a factor of at least 1.1, while for perpendicular incidence the MLA resolution does not consistently differ significantly from the MSA result for all tested scintillator thicknesses. For the thickest scintillator (3 mm, interaction probability 66% at 141 keV) a spatial resolution (perpendicular incidence) of 147 µm full width at half maximum (FWHM) was obtained with an energy resolution of 35.2% FWHM. These results of the MLA were achieved without prior calibration of scintillations as is needed for many statistical scintillation detection algorithms. We conclude that the MLA significantly improves the gamma camera performance compared to the MSA.

A Stochastic Model for Electron Multiplication Charge-Coupled Devices – From Theory to Practice

“…This model does not just give expected value and error as descriptive parameters, but provides a full probability distribution. The model agrees with that in [15] and theoretically confirms the excess noise factor of that was found empirically by other researchers [12]. Understanding noise model properties in detail allows acceptable approximations to be made where necessary for individual problems.…”

“…[13] considers this point, while others (e.g. [15]) make the same assumption that we do here. We obtain with (i.e.…”

“…Also [10] and [7] used probability density functions (PDF) to model parts of the EMCCD without taking full advantage of the result. A recently [15] published work used a detailed noise model likelihood for an EMCCD, exploiting it for maximum-likelihood scintillation detection.…”

“…In an effort to advance our own data analysis capabilities in the field of single molecule imaging in live cells, we developed and tested a detailed noise model likelihood function for EMCCDs. This work was performed independently of [15] and resulted in the same final model. We will show that empirical properties of the EMCCD noise, such as the excess noise factor can be derived from this model.…”

“…We will show that empirical properties of the EMCCD noise, such as the excess noise factor can be derived from this model. In contrast to [15] however, in this paper we present and explain this model in detail, test it and explain how to calibrate it, so that the wider biological imaging community can make better use of advanced quantitative data analysis techniques for EMCCD images.…”