Proposal of a 4D ML reconstruction strategy for PET-based treatment verification in ion beam radiotherapy

Abstract

The aim of this work is to propose an adaptation of a 4D Maximum Likelihood (ML) reconstruction strategy as a tool to improve the sensitivity of PET-based treatment verification in ion beam radiotherapy. PET images acquired during/shortly after the treatment (Measured PET) and an estimate of the same PET images derived from the treatment plan (Estimated PET) are considered as two frames of a 4D dataset. The algorithm iteratively estimates the annihilation events distribution in a reference frame and the deformation motion fields that map it in the Expected and Measured PET frames. Expected PET images can be then mapped into the Measured PET frame to verify the treatment. The details of the algorithm are presented and the strategy is preliminarily tested on an analytically simulated dataset. Convergence at different count statistics and ability to detect mismatches are assessed.