Evaluation of Image Quality Improvements When Adding Patient Outline Constraints into a Generalized Scatter PET Reconstruction Algorithm

Scattered coincidences degrade image contrast and compromise quantitative accuracy in positron emission tomography (PET). A number of approaches to 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 distribution 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. The results have shown that for scatter fractions of 10–60% this algorithm improves the contrast recovery coefficients (CRC) by 4 to 28.6% for a 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 further improvements in the contrast and noise properties of the reconstructed images can be made by including the patient outline in the reconstruction algorithm as a constraint. 1. Introduction Scattered photons are a significant source of image quality degradation and lead to quantitative errors in positron emission tomography (PET) [1]. The scatter fraction can be as high as 40–60% when a tomograph operates in 3D mode without slice-defining septa and in large patients, making this of greater consequence for cardiac imaging [2–4]. In conventional PET reconstruction methods, scattered coincidences are assumed to be noise and consequently a number of ways for estimating and correcting scattered coincidences in measured data have been proposed. However, most of these techniques are based on the estimation and subtraction of scatter from the projection data instead of exploring the possibilities of using scattered coincidences in the reconstruction [2, 5]. During the scatter correction process, artifacts may be introduced in the source distribution due to inaccurate estimation of the scatter sonogram [1] while at the same time, the system’s sensitivity will be reduced and image noise will be amplified [2, 6]. The energy resolution of PET detectors has improved in [7, 8] recently, making it conceivable to use the energy and detected location of