This study is a retrospective analysis of the diagnostic accuracy of FDOPA PET with MRI fusion to FDOPA PET without MRI fusion. Clinical FDOPA PET scans obtained between 2000 and 2008 at the University of Wisconsin Hospital and Clinics were assessed using measures derived from regions of interest (ROI) generated with fused MRI (fused group) and again with ROIs derived solely from PET data (non-fused groups). The ROIs were used to calculate ratios (Striatum/Occipital cortex, Striatum/Cerebellum) pertinent to Parkinson’s disease (PD) pathology. The clinical records were assessed for demographic data, follow-up length, and diagnosis. Receiver Operator Characteristics with area under the curve (AUC) measures were calculated and compared using confidence intervals and hypothesis testing. 27 patients had FDOPA PET with median clinical follow-up of 4 years. Of these, 17 patients had FDOPA PET with a fusible MR image. Seven of the 27 had a non-PD movement disorder. AUCs for the ratio measures ranged from 0.97-1.0 (fused), 0.73-0.83 (non-fused), and 0.63-0.82 (matched non-fused). The fused images had improved accuracy compared to the matched non-fused and all non-fused groups for the striatum to occipital group (p=0.04, p=0.03), while the striatum to cerebellum ratio had improvement over the non-fused all group (p=0.041). MR fusion to FDOPA PET improves the accuracy of at least some measures (Striatum/Occiput, Striatum/Cerebellum) in the diagnosis of PD.

Purpose

(1) Determine the effect of 18Fluorodeoxyglucose Positron Emission Tomography (FDG-PET), magnetic resonance imaging (MRI), and electroencephalogram (EEG) on the decision for temporal lobe epilepsy (TLE) surgery. (2) Determine if FDG-PET, MRI, or EEG predict surgical outcome.

Procedures

All PET scans ordered (2000–2010) for epilepsy or seizure were tabulated. Medical records were investigated to determine eligibility and collect data. Statistical analysis included odds ratios, kappa statistics, univariate analysis, and logistic regression.

Results

186 patients had an FDG-PET, 124 patients had TLE, 50 were surgical candidates, and 27 had operations with > 6 months follow-up. Median length of follow-up was 24 months. MRI, FDG-PET, and EEG were significant predictors of surgical candidacy (p<0.001) with odds ratio of 42.8, 20.4, and 6.3 respectively. PET was the only significant predictor of post-operative outcome. (p<0.01)

Conclusion

MRI had a trend toward most influence on surgical candidacy, but only FDG-PET predicted the surgical outcome.

There is a well known tradeoff between image noise and image sharpness that is dependent on the number of iterations performed in ordered subset expectation maximization (OSEM) reconstruction of PET data. We aim to evaluate the impact of this tradeoff on the sensitivity and specificity of 18F-FDG PET for the diagnosis of temporal lobe epilepsy. A retrospective blinded reader study was performed on two OSEM reconstructions, using either 2 or 5 iterations, of 32 18F-FDG PET studies acquired at our institution for the diagnosis of temporal lobe epilepsy. The sensitivity and specificity of each reconstruction for identifying patients who were ultimately determined to be surgical candidates was assessed using an ROC analysis. The sensitivity of each reconstruction for identifying patients who showed clinical improvement following surgery was also assessed. Our results showed no significant difference between the two reconstructions studied for either the sensitivity and specificity of 18F-FDG PET for predicting surgical candidacy, or its sensitivity for predicting positive surgical outcomes. This implies that the number of iterations performed during OSEM reconstruction will have little impact on a reader based interpretation of 18F-FDG PET scans acquired for the diagnosis of temporal lobe epilepsy, and can be determined by physician and institutional preference.