AX-PET: Demonstration of an axial PET concept for brain and small animal imaging

Objectives Demonstrate the feasibility of a novel axial crystal arrangement, combined with Wave Length Shifter Strips (WLSs) and both read-out by G-APDs, for reducing parallax error and increasing sensitivity in brain and small animal PET.

Methods We have developed two modules with long LYSO crystals (3x3x100 mm³) axially arranged so that depth of interaction information is directly obtained from the crystals. The axial coordinate is reconstructed from the signal measured in the WLSs, placed orthogonally to the crystals. Crystals and WLSs are read-out individually by G-APDs, whose insensitivity to magnetic fields makes this detector a good candidate for multi-modality imaging. The photon tracking capabilities of the detector can be exploited to enhance sensitivity by properly identifying Compton scattering between the crystals (inter-crystal scatter, ICS). Post-acquisition processing of ICS events is then required prior to image reconstruction. The sensitivity could be also enhanced by adding additional crystal layers without introducing parallax error.

Results Two modules have been already built and set in coincidence with dedicated front-end electronics. Initial measurements show good energy resolution, ~12% (FWHM) at 511 keV. The intrinsic spatial resolution turns out to be similar for the three coordinates, ~2 mm (FWHM). A coincidence time resolution of 1.8 ns (FWHM) at 511 keV was measured, allowing for a narrow coincidence window. Dedicated software has been developed for simulation and image reconstruction purposes. Using simulated data, up to 70% of ICS events were properly identified. Imaging different phantoms is in progress.

Conclusions Axially oriented crystals combined with WLSs constitute a promising approach for reducing parallax error and increase sensitivity for new brain and small animal PET scanners. The modules we have developed show good energy and spatial resolution. Simulations suggest that it is feasible to exploit the detection and identification of Compton events for increasing the sensitivity through their inclusion in the image reconstruction process