Hybrid PET-MR list-mode kernelized expectation maximization reconstruction

The recently introduced kernelized expectation maximization (KEM) method has shown promise across varied applications. These studies have demonstrated the benefits and drawbacks of the technique when the kernel matrix is estimated from separate anatomical information, for example from magnetic resonance (MR), or from a preliminary PET reconstruction. The contribution of this work is to propose and investigate a list-mode-hybrid KEM (LM-HKEM) reconstruction algorithm with the aim of maintaining the benefits of the anatomically-guided methods and overcome their limitations by incorporating synergistic information iteratively. The HKEM is designed to reduce negative bias associated with low-counts, the problem of PET unique feature suppression reported in the previously mentioned studies using only the MR-based kernel, and to improve contrast of lesions at different count levels. The proposed algorithm is validated using a simulation study, a phantom dataset and two clinical datasets. For each of the real datasets high and low count-levels were investigated. The reconstructed images are assessed and compared with different LM algorithms implemented in STIR. The findings obtained using simulated and real datasets show that anatomically-guided techniques provide reduced partial volume effect and higher contrast compared to standard techniques, and HKEM provides even higher contrast and reduced bias in almost all the cases. This work, therefore argues that using synergistic information, via the kernel method, increases the accuracy of the PET clinical diagnostic examination. The promising quantitative features of the HKEM method give the opportunity to explore many possible clinical applications, such as cancer and inflammation.