Clinical investigations of a 4D ML reconstruction strategy for PET-based treatment verification in ion beam therapy

Abstract

In ion beam therapy, Positron Emission Tomography (PET) imaging can be applied to provide a consistency check of the delivered treatment with respect to the treatment plan. Treatment verification relies on the comparison between a PET distribution measured during or after treatment delivery (“measured PET”) and a PET simulation calculated on the basis of the treatment plan (“expected PET”). The method is challenged by the poor image quality of the measured PET, which may result in a low sensitivity to detectable mismatches between planned and applied treatment. In this work we propose the application of a motion-aware 4D Maximum Likelihood (ML) reconstruction strategy for PET-based treatment verification in ion beam therapy. This approach is specifically tested on simulated clinical-like scenarios, as well as on a real clinical dataset. The measured PET and the expected PET are interpreted as two different motion states of a 4D PET dataset. The idea is to estimate the deformation field mapping the expected PET onto the measured PET as a measure of mismatches. An enhanced measured PET is therefore obtained by warping the expected PET according to the estimated motion field. Clinical-like scenarios were reproduced by means of Monte Carlo simulations corresponding to a hypo-fractionated carbon ion treatment of liver tumour. The measured PET was attributed to different simulations introducing positioning mismatches by means of rigid translations, to be compared to the reference expected PET simulation without mismatch. Real clinical dataset include PET data acquired shortly after treatment at a dedicated PET-CT scanner installed at HIT (Heidelberg Ion beam Therapy Center, Germany), in comparison to the corresponding expected PET coming from Monte Carlo simulations. The accuracy of mismatch estimation and the robustness to noise are shown.