Monte Carlo results from neural networks as an alternative to Compton photons LOR analysis

Abstract

Sensitivity is critical in small-animal PET, and including more of the discarded detections would increase it. However lowering the energy threshold compromises the spatial resolution. This paper is an update on a method to include triple coincidences in the image without significant image degradation. With the energy threshold set as low as 50 keV, the triple coincidences analyzed are the simplest inter-crystal Compton scatter scenario where one photoelectric 511-keV detection coincides with two detections whose energy sum is also 511-keV. The method uses neural networks instead of traditional Compton interaction mathematical models to compute the proper Line-of-Response (LOR) for that coincidence. The paper revisits the algorithm structure, and in particular the preprocessing steps required to simplify the data fed to the network, preprocessing which improves the LOR computation significantly. The paper then presents Monte-Carlo analysis of the method with various point and cylinder sources. The simulated scanner geometry is purposely made to encompass the very worst-case conditions seen in PET scanners today: small diameter, poor photoelectric fraction, poor 35% FWHM energy resolution. LOR identification error is around 20 to 25% and the sensitivity increase ranges from approximately 70 to 100%. Finally, images were recently obtained, with overall very good quality.