PET quantification: strategies for partial volume correction

The quality of images reconstructed by statistical iterative methods depends on an accurate model of the relationship between image space and projection space through the system matrix The elements of the system matrix for the clinical Hi-Rez scanner were derived by processing the data measured for a point source at different positions in a portion of the field of view. These measured data included axial compression and azimuthal interleaving of adjacent projections. Measured data were corrected for crystal and geometrical efficiency. Then, a whole system matrix was derived by processing the responses in projection space. Such responses included both geometrical and detection physics components of the system matrix. The response was parameterized to correct for point source location and to smooth for projection noise. The model also accounts for axial compression (span) used on the scanner. The forward projector for iterative reconstruction was constructed using the estimated response parameters. This paper extends our previous work to fully three-dimensional. Experimental data were used to compare images reconstructed by the standard iterative reconstruction software and the one modeling the response function. The results showed that the modeling of the response function improves both spatial resolution and noise properties.

The aim of this study was to determine the impact of the main clinicopathological and biological prognostic factors of breast cancer on (18)F-fluorodeoxyglucose (FDG) uptake. Only women with tumours larger than 20 mm (T2-T4) were included in order to minimize bias of partial volume effect. In this prospective study, 132 consecutive women received FDG PET/CT imaging before starting neoadjuvant chemotherapy. Maximum standardized uptake values (SUV(max)) were compared to tumour characteristics as assessed on core biopsy. There was no influence of T and N stage on SUV. Invasive ductal carcinoma showed higher SUV than lobular carcinoma. However, the highest uptake was found for metaplastic tumours, representing 5% of patients in this series. Several biological features usually considered as bad prognostic factors were associated with an increase in FDG uptake: the median of SUV(max) was 9.7 for grade 3 tumours vs 4.8 for the lower grades (p < 0.0001); negativity for oestrogen receptors (ER) was associated with higher SUV (ER+ SUV = 5.5; ER- SUV = 7.6; p = 0.003); triple-negative tumours (oestrogen and progesterone receptor negative, no overexpression of c-erbB-2) had an SUV of 9.2 vs 5.8 for all others (p = 0005); p53 mutated tumours also had significantly higher SUV (7.8 vs 5.0; p < 0.0001). Overexpression of c-erbB-2 had no effect on the SUV value. Knowledge of the factors influencing uptake is important when interpreting FDG PET/CT scans. Also, findings that FDG uptake is highest in those patients with poor prognostic features (high grade, hormone receptor negativity, triple negativity, metaplastic tumours) is helpful to determine who are the best candidates for baseline staging.

In this paper, the authors review the field of resolution modeling in positron emission tomography (PET) image reconstruction, also referred to as point-spread-function modeling. The review includes theoretical analysis of the resolution modeling framework as well as an overview of various approaches in the literature. It also discusses potential advantages gained via this approach, as discussed with reference to various metrics and tasks, including lesion detection observer studies. Furthermore, attention is paid to issues arising from this approach including the pervasive problem of edge artifacts, as well as explanation and potential remedies for this phenomenon. Furthermore, the authors emphasize limitations encountered in the context of quantitative PET imaging, wherein increased intervoxel correlations due to resolution modeling can lead to significant loss of precision (reproducibility) for small regions of interest, which can be a considerable pitfall depending on the task of interest.