Background PET imaging of 18F-fluorodeoxygucose (FDG) is used widely for tumour staging and assessment of treatment response, but the biology associated with FDG uptake is still not fully elucidated. We therefore carried out gene set enrichment analyses (GSEA) of RNA sequencing data to find KEGG pathways associated with FDG uptake in primary breast cancers. Methods Pre-treatment data were analysed from a window-of-opportunity study in which 30 patients underwent static and dynamic FDG-PET and tumour biopsy. Kinetic models were fitted to dynamic images, and GSEA was performed for enrichment scores reflecting Pearson and Spearman coefficients of correlations between gene expression and imaging. Results A total of 38 pathways were associated with kinetic model flux-constants or static measures of FDG uptake, all positively. The associated pathways included glycolysis/gluconeogenesis (‘GLYC-GLUC’) which mediates FDG uptake and was associated with model flux-constants but not with static uptake measures, and 28 pathways related to immune-response or inflammation. More pathways, 32, were associated with the flux-constant K of the simple Patlak model than with any other imaging index. Numbers of pathways categorised as being associated with individual micro-parameters of the kinetic models were substantially fewer than numbers associated with flux-constants, and lay around levels expected by chance. Conclusions In pre-treatment images GLYC-GLUC was associated with FDG kinetic flux-constants including Patlak K, but not with static uptake measures. Immune-related pathways were associated with flux-constants and static uptake. Patlak K was associated with more pathways than were the flux-constants of more complex kinetic models. On the basis of these results Patlak analysis of dynamic FDG-PET scans is advantageous, compared to other kinetic analyses or static imaging, in studies seeking to infer tumour-to-tumour differences in biology from differences in imaging. Trial registration NCT01266486, December 24th 2010.

4D-PET reconstruction has the potential to significantly increase the signal-to-noise ratio in dynamic PET by fitting smooth temporal functions during the reconstruction. However, the optimal choice of temporal function remains an open question. A 4D-PET reconstruction algorithm using adaptive-knot cubic B-splines is proposed. Using realistic Monte-Carlo simulated data from a digital patient phantom representing an [18-F]-FMISO-PET scan of a non-small cell lung cancer patient, this method was compared to a spectral model based 4D-PET reconstruction and the conventional MLEM and MAP algorithms. Within the entire patient region the proposed algorithm produced the best bias-noise trade-off, while within the tumor region the spline-and spectral model-based reconstructions gave comparable results.

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4D reconstruction of dynamic positron emission tomography (dPET) data can improve the signal-to-noise ratio in reconstructed image sequences by fitting smooth temporal functions to the voxel time-activity-curves (TACs) during the reconstruction, though the optimal choice of function remains an open question. We propose a spline-residue model, which describes TACs as weighted sums of convolutions of the arterial input function with cubic B-spline basis functions. Convolution with the input function constrains the spline-residue model at early time-points, potentially enhancing noise suppression in early time-frames, while still allowing a wide range of TAC descriptions over the entire imaged time-course, thus limiting bias.Spline-residue based 4D-reconstruction is compared to that of a conventional (non-4D) maximum a posteriori (MAP) algorithm, and to 4D-reconstructions based on adaptive-knot cubic B-splines, the spectral model and an irreversible two-tissue compartment (‘2C3K’) model. 4D reconstructions were carried out using a nested-MAP algorithm including spatial and temporal roughness penalties. The algorithms were tested using Monte-Carlo simulated scanner data, generated for a digital thoracic phantom with uptake kinetics based on a dynamic [18F]-Fluromisonidazole scan of a non-small cell lung cancer patient. For every algorithm, parametric maps were calculated by fitting each voxel TAC within a sub-region of the reconstructed images with the 2C3K model.Compared to conventional MAP reconstruction, spline-residue-based 4D reconstruction achieved >50% improvements for five of the eight combinations of the four kinetics parameters for which parametric maps were created with the bias and noise measures used to analyse them, and produced better results for 5/8 combinations than any of the other reconstruction algorithms studied, while spectral model-based 4D reconstruction produced the best results for 2/8. 2C3K model-based 4D reconstruction generated the most biased parametric maps. Inclusion of a temporal roughness penalty function improved the performance of 4D reconstruction based on the cubic B-spline, spectral and spline-residue models.

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