Improvements in image quality when using patient outline constraints with a generalized scatter PET reconstruction algorithm

Scattered coincidences degrade image contrast and compromise quantitative accuracy in Positron Emission Tomography (PET). A number of approaches for estimating and correcting scattered coincidences have been proposed, but most of them are based on estimating and subtracting a scatter sinogram from the measured data. We have previously shown that both true and scattered coincidences can be treated similarly by using Compton scattering kinematics to define a locus of scattering which may in turn be used to reconstruct the activity using a Generalized Scatter Maximum-likelihood Expectation-Maximization (GS-MLEM) algorithm. The annihilation position can be further confined by taking advantage of the patient outline (or a geometrical shape that encompasses the patient outline). The proposed method was tested on a phantom generated using GATE based Monte Carlo simulation. The results have shown that for scatter fractions of 10 to 60% this algorithm improves the Contrast Recovery Coefficients (CRC) by 4.0 to 28.6% for a hot source and 5.1 to 40% for a cold source while the Relative Standard Deviation (RSD) was reduced. Including scattered photons directly into the reconstruction eliminates the need for (often empirical) scatter corrections and increases the system´s sensitivity and image contrast. This could be used to either improve the diagnostic quality and/or to reduce patient dose and radiopharmaceutical cost.