A NORMALIZATION TECHNIQUE FOR 3D PET DATA

Prior to reconstruction, emission data from a multi-ring PET camera must be corrected (normalized) for variations in detector sensitivity. The appropriate correction coefficients are obtained by measuring the response of all coincidence lines to a calibrated source of activity (a blank scan). State-of-the-art cameras may contain up to a million such lines of response (LORs), and therefore around 400 million counts will be required to calibrate each LOR to a statistical accuracy of 5%. Alternatively, by modelling the LOR sensitivity as the product of the individual detector efficiencies and a geometrical factor, a calibration procedure has been proposed which requires the determination of only 6000 parameters from this same data set. A significant improvement in the statistical accuracy of the coefficients can therefore be expected. Recently, multi-ring scanners have been operated with the septa retracted, increasing the number of measured LORs by a factor of eight. The acquisition of the calibration data necessary to achieve adequate statistical accuracy then becomes prohibitive. We show that, by modelling the LOR sensitivity, it is possible, with certain approximations, to normalize a septa-retracted emission data set with good accuracy. The input to the model is a high statistics blank scan acquired with the septa extended, which offers a number of practical advantages.