Guided Image Reconstruction for Multi-Tracer PET

Scanning a patient with two or more PET radiotracers provides complementary imaging information provided by the different tracers. Invariably, one of the datasets will provide a higher quality image than the others due to a number of possible reasons, such as i) the positron energy of the radioisotope used, ii) the contrast of the tracer uptake, iii) the specificity of the tracer uptake, or iv) the quantity of injected activity and the total detected counts. This work proposes use of a higher quality PET image (the prior) to guide the reconstruction of another, lower quality PET dataset to help compensate for the reduced image quality. The prior image is used to calculate the a priori similarities between each voxel and its neighbours. Similarity is quantified using a patchbased Gaussian kernel modulated by spatial distances between voxels. A patch-based sparsification step is also included to reduce the number of non-zero similarities. The second dataset is then reconstructed with a weighted quadratic prior using these similarities as spatially-variant weights. This method penalises intensity differences between voxel pairs in accordance with those which are similar in the prior. The proposed methodology has been tested for [
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F] fluorodeoxyglucose (FDG)/[
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C] methionine (MET) paired datasets. In a 3D simulation study the FDGguided MET reconstruction produced images with lower wholebrain error levels compared to both unregularised maximum likelihood expectation-maximisation and an unguided quadratically penalised method. These improvements were also observed for real data from a patient who had undergone scans with these two tracers, where noise reduction and greater anatomical detail were attained using the FDG-guided MET reconstruction. These results suggest that using one PET tracer to guide the reconstruction of another is both feasible and potentially beneficial. Future work will require hyperparameter optimisation and further application-specific validation.