MR-guided dynamic PET image reconstruction with the kernel method and spectral basis functions

Abstract

Regularization of iterative reconstruction for fully dynamic PET has often been achieved implicitly by estimating coefficients relating to temporal basis functions, such as data-derived temporal basis functions, wavelet temporal basis functions, or compartmental model based temporal basis functions (direct kinetic parameter estimation). In this work, we propose and evaluate a method for anatomy-guided dynamic PET reconstruction using a joint parameterization of the PET image in terms of spatial basis functions from the kernel method applied to a co-registered MR anatomical image, and temporal basis functions using the spectral analysis method. Since the model of the dynamic image is linear, the EM algorithm can be used to find an estimate for the coefficients. We demonstrate that the proposed method combining both basis functions outperforms reconstruction using either spectral temporal basis functions alone or kernel spatial basis functions alone, offering substantially reduced pixel-level RMSE in post-reconstruction parametric maps. Importantly, some benefits are retained even in the case where structures are present in the emission image but absent in the anatomical image.