Dynamic FDG-PET in localization of focal epilepsy: A pilot study

Abstract

Epilepsy surgery remains underutilized, in part because non-invasive methods of potential seizure foci localization are inadequate. We used high-resolution, parametric quantification from dynamic 2-[18F] fluoro-2-deoxy-d-glucose positron emission tomography (dFDG-PET) imaging to locate hypometabolic foci in patients whose standard clinical static PET images were normal. We obtained dFDG-PET brain images with simultaneous EEG in a one-hour acquisition on seven patients with no MRI evidence of focal epilepsy to record uptake and focal radiation decay. Images were attenuation- and motion-corrected and co-registered with high-resolution T1-weighted patient MRI and segmented into 18 regions of interest (ROI) per hemisphere. Tracer uptake was calibrated with a model corrected blood input function with partial volume (PV) corrections to generate tracer parametric maps compared between mean radiation values between hemispheres with z-scores. We identified ROI with the lowest negative z scores (<-1.65 SD) as hypometabolic. Dynamic 2-[18F] fluoro-2-deoxy-d-glucose positron emission tomography ( found focal regions of altered metabolism in all cases in which standard clinical FDG-PET found no abnormalities. This pilot study of dynamic FDG-PET suggests that further research is merited to evaluate whether glucose dynamics offer improved clinical utility for localization of epileptic foci over standard static techniques.

Keywords: Cerebral metabolism; Epilepsy surgery; Focal epilepsy; Neuroimaging.

Fig. 1.

Steps in dynamic FDG-PET image acquisition, pre-processing, and parametric map creation. Dynamic PET, high-resolution MR and Destrieux Atlas defined on the MR brain template form the raw inputs (A–D). The motion corrected dynamic PET data are Co-Registered with patient MR followed by co-registration of MR image with a high-resolution T1-weighted MR brain template provided by the Montreal Neurological Institute using nonrigid transform (E). Model corrected blood input function (MCIF) is then computed from the internal carotid arteries for all patients (F). Each voxel of dynamic PET data along with blood input is independently fed into a graphical Patlak model to compute parametric Ki maps using linear regression and subsequently z score maps (G). The co-registered template and atlas was used to bin individual voxels of the generated uptake maps for regional analyses (H).

Fig. 2.

Regional z score maps of dFDG-PET co-registered on MRI for all 7 patients indicating temporal lobe hypometabolism. Inclusion criteria required normal standard sFDG-PET.