Multitracer Guided PET Image Reconstruction

Multitracer positron emission tomography (PET) has the potential to enhance PET imaging by providing complementary information from different physiological processes. However, one or more of the images may present high levels of noise. Guided image reconstruction methods transfer information from a guide image into the PET image reconstruction to encourage edge-preserving noise reduction. In this paper, we aim to reduce noise in poorer quality PET datasets via guidance from higher quality ones by using a weighted quadratic penalty approach. In particular, we applied this methodology to [18F]fluorodeoxyglucose (FDG) and [11C]methionine imaging of gliomas. 3-D simulation studies showed that guiding the reconstruction of methionine datasets using pre-existing FDG images reduced reconstruction errors across the whole-brain (−8%) and within a tumor (−36%) compared to maximum likelihood expectation-maximization (MLEM). Furthermore, guided reconstruction outperformed a comparable nonlocal means filter, indicating that regularizing during reconstruction is preferable to post-reconstruction approaches. Hyperparameters selected from the 3-D simulation study were applied to real data, where it was observed that the proposed FDG-guided methionine reconstruction allows for better edge preservation and noise reduction than standard MLEM. Overall, the results in this paper demonstrate that transferring information between datasets in multitracer PET studies improves image quality and quantification performance.