Random Correction Method for Positron Emission Mammography Using Delayed Coincidence Data

A dedicated random correction algorithm is presented in this study for positron emission mammography (PEM) systems. PEM refers to a specified PET system that is optimized for breast imaging by its small FOV. Clinical imaging results from such systems, however, may be degraded by strong statistical noise caused from random coincidences, especially in the region that is near to the torso, due to the high amount of activity uptake outside the FOV, the low geometrical sensitivity of the detector elements near to the torso, and the large solid angle acceptance of random coincidence events. Because of the low statistics of detected coincidence events against the extremely high number of LORs, list-mode reconstruction algorithms are suggested for PEM systems. The conventional random correction methods cannot be directly implemented or can induce an even higher statistical noise. The correction method by single count rate requires a high hardware cost to record single events and needs an accurate calibration to reach a non-bias correction. The new random correction algorithm presented in this study can be implemented into list-mode reconstruction without single count acquisition. This algorithm estimates in a first step a smooth correction image with the delayed coincidences data. This correction image is then used to estimate the mean random coincidence rate for each detected event during the iterative list-mode reconstruction routine. The approach is tested on a ClearPEM system developed by the Crystal Clear Collaboration. Experimental data are acquired by two face-to-face detectors at four angular positions with a total acquisition time of 20 min for each breast. Results show that the proposed algorithm can largely suppress the statistical noise in the region near the torso.