A Self-Normalization Reconstruction Technique for PET Scans Using the Positron Emission Data

Abstract

Positron emission tomography (PET) image quality in both clinical and preclinical environments highly depends on an accurate knowledge of the detector hardware to correct for image quality degrading effects like gain, temperature, and photon detection efficiency variations of the individual crystals. In conventional PET systems some of these variations are typically corrected using a dedicated calibration scan in which the scanner performance for a well-known activity source is measured. We propose an alternative method for estimating the relative sensitivity of each detector pixel using the coincidences as well as the singles emission data of each PET scan. The overall idea is to compare the total sum of all measured single photons before coincidence processing in each crystal with a steadily low-frequent distribution that can normally be expected. Both the estimated activity and the estimated detector sensitivity are simultaneously improved by using an extended iterative reconstruction scheme. This way we ensure the use of an optimal calibration correction (with the exception of a global factor) for each data set, even if the scanner performance has changed between two scans. Data measured with a preclinical PET scanner (HYPERIon-I) which uses analog silicon photomultipliers in combination with a custom-made ASIC shows a significant increase of image quality and homogeneity using the proposed method.