Total variation minimization for in-beam PET image reconstruction

One of the challenging applications of positron emission tomography (PET) is in-beam PET, which is an in situ dose monitoring method for charged particle therapy. It is known that the activity of positron emitters produced through fragmentation reaction is generally low. Image reconstruction from low count data would require alternative algorithms to suppress noise in images, such as the maximum a posteriori (MAP) reconstruction algorithm, which is often used to improve an image reconstruction problem by introducing penalty functions. In particular, the total variation (TV) norm has been proposed as a penalty function to suppress noise while preserving edges. In this study, we applied a MAP-TV image reconstruction algorithm to in-beam PET imaging, and we evaluated the effect of TV constraint for the in-beam PET image in terms of detection performance of distal edge a of positron emitter distribution along beam irradiation. We applied the one-step-Iate algorithm combined with the TV norm to a new small prototype of the single-ring OpenPET (SROP), which is our second generation OpenPET geometry. The small SROP prototype consisted of two oval detector rings which were slanted by 45 degree and stacked. We carried out initial in-beam experiments in the Heavy Ion Medical Accelerator in Chiba (HIMAC). In the experiment, we used a ¹²C beam. The target was a rectangular parallelepiped phantom (40 × 40 mm² and 100 mm long) made of polymethyl methacrylate (PMMA). We calculated averaged peak position of profiles obtained in reconstructed images. In the experimental results, reconstructed images became smoother and less noisy with stronger constraint. It was shown that the MAP-TV algorithm enables smoothness and less noisy in reconstructed images even from the low count.