Impact of different parametric Patlak imaging approaches and comparison with a 2-tissue compartment pharmacokinetic model with a long axial field-of-view (LAFOV) PET/CT in oncological patients

Abstract

Aim

The recently introduced Long-Axial-Field-of-View (LAFOV) PET-CT scanners allow for the first-time whole-body dynamic- and parametric imaging. Primary aim of this study was the comparison of direct and indirect Patlak imaging as well as the comparison of different time frames for Patlak calculation with the LAFOV PET-CT in oncological patients. Secondary aims of the study were lesion detectability and comparison of Patlak analysis with a two-tissue-compartment model (2TCM).

Methodology

50 oncological patients with 346 tumor lesions were enrolled in the study. All patients underwent [¹⁸F]FDG PET/CT (skull to upper thigh). Here, the Image-Derived-Input-Function) (IDIF) from the descending aorta was used as the exclusive input function. Four sets of images have been reviewed visually and evaluated quantitatively using the target-to-background (TBR) and contrast-to-noise ratio (CNR): short-time (30 min)-direct (STD) Patlak K_i, short-time (30 min)-indirect (STI) Patlak K_i, long-time (59.25 min)-indirect (LTI) Patlak K_i, and 50–60 min SUV (sumSUV). VOI-based 2TCM was used for the evaluation of tumor lesions and normal tissues and compared with the results of Patlak model.

Results

No significant differences were observed between the four approaches regarding the number of tumor lesions. However, we found three discordant results: a true positive liver lesion in all Patlak K_i images, a false positive liver lesion delineated only in LTI K_i which was a hemangioma according to MRI and a true negative example in a patient with an atelectasis next to a lung tumor. STD, STI and LTI K_i images had superior TBR in comparison with sumSUV images (2.9-, 3.3- and 4.3-fold higher respectively). TBR of LTI K_i were significantly higher than STD K_i. VOI-based k₃ showed a 21-fold higher TBR than sumSUV. Parameters of different models vary in their differential capability between tumor lesions and normal tissue like Patlak K_i which was better in normal lung and 2TCM k₃ which was better in normal liver. 2TCM K_i revealed the highest correlation (r = 0.95) with the LTI Patlak K_i in tumor lesions group and demonstrated the highest correlation with the STD Patlak K_i in all tissues group and normal tissues group (r = 0.93 and r = 0.74 respectively).

Conclusions

Dynamic [¹⁸F]-FDG with the new LAFOV PET/CT scanner produces Patlak K_i images with better lesion contrast than SUV images, but does not increase the lesion detection rate. The time window used for Patlak imaging plays a more important role than the direct or indirect method. A combination of different models, like Patlak and 2TCM may be helpful in parametric imaging to obtain the best TBR in the whole body in future.