Abstract
Improvements in energy resolution of modern positron emission tomography (PET) detectors have created opportunities to implement energy-based scatter correction algorithms. Here, we use the energy information of auxiliary windows to estimate the scatter component. Our method is directly implemented in an iterative reconstruction algorithm, generating a scatter-corrected image without the need for sinograms. The purpose was to implement a fast energy-based scatter correction method on list-mode PET data, when it was not possible to use an attenuation map as a practical approach for the scatter degradation. The proposed method was evaluated using Monte Carlo simulations of various digital phantoms. It accurately estimated the scatter fraction distribution, and improved the image contrast in the simulated studied cases. We conclude that the proposed scatter correction method could effectively correct the scattered events, including multiple scatters and those originated in sources outside the field of view.
Highlights
In positron emission tomography (PET), Compton scattering refers to a scenario in which one or both annihilation photons suffer a change in direction and energy when interacting with matter
The proposed method uses energy information to locally estimate the right proportion of scatter events in PET data for all field of view (FOV) regions
Two configurations are analyzed in this study: the double energy window, with a low energy window below the photopeak energy region, and the triple energy window, which incorporates a high energy window above the photopeak
Summary
In positron emission tomography (PET), Compton scattering refers to a scenario in which one or both annihilation photons suffer a change in direction and energy when interacting with matter. Among several existing approaches to solve the scatter problem in PET [1,2,3], the most popular one is the single scatter simulation (SSS), in which a scattered image is simulated and incorporated into the reconstruction process [4]. This method is quite accurate, but it requires prior knowledge of the activity distribution, inside the field of view (FOV), combined with an attenuation map of the object. Our purpose is to obtain a good estimation of scatter without using
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