Purpose: This study aimed to investigate the results of compartmental modeling (CM) and spectral analysis (SA) generated with dynamic 18F-FMISO tumor images. Besides, the regular tissue-to-blood ratio (TBR) images were derived and compared with the dynamic models.
Methods: Nine subjects with glioblastoma underwent PET/CT imaging with the 18F-FMISO tracer. The protocol for PET imaging began with 15 min in dynamic mode and two 10-min duration static images at 120 min and 180 min post-injection. We used the two-tissue compartmental model for CM at the voxel basis, and we conducted SA to estimate the 18F-FMISO accumulation within each voxel. We also investigated the usual tumor-to-blood ratio (TBR) for comparison.
Results: The images of the tumor showed different patterns of hypoxia and necrosis as a function of PET scanning times, while CM and SA methods based on dynamic PET imaging equally located tumor hypoxia. The mean correlation of Ki images of all subjects between CM and SA was 0.63 ± 0.19 (0.24-0.86). CM produced less noisy K i images than SA, and, in the contrary, SA produced accumulation component images more clear than with CM. CM-K i and SA-K i images were correlated with TBR images (r = 0.72 ± 0.20 and 0.56 ± 0.26, respectively). In the only subject having a continuously increasing tumor time-activity curve, the k 3 image showed a high uptake in the necrosis region which was not apparent in TBR or K i images.
Conclusion: Based on these results, the combination of CM and SA approaches was found more appropriate in generating voxel-based hypoxia images.
Keywords: Compartmental modeling; FMISO; Hypoxia; Spectral analysis.
© Korean Society of Nuclear Medicine 2021.