PT - JOURNAL ARTICLE AU - A Ram Yu AU - Jin Su Kim AU - Jisook Moon AU - Kyeong Min Kim AU - JongGuk Kim AU - Ji-Ae Park AU - Sang-Keun Woo AU - Hee Joung Kim AU - Sang Moo Lim TI - Development of a voxel-based statistical analysis method for FP-CIT PET in a Parkinson’s disease rat model DP - 2011 May 01 TA - Journal of Nuclear Medicine PG - 2056--2056 VI - 52 IP - supplement 1 4099 - http://jnm.snmjournals.org/content/52/supplement_1/2056.short 4100 - http://jnm.snmjournals.org/content/52/supplement_1/2056.full SO - J Nucl Med2011 May 01; 52 AB - 2056 Objectives Because of the lack of anatomical information, it is difficult to apply spatial normalization parameters to statistical parametric mapping (SPM) analysis in FP-CIT PET. Therefore, for efficient spatial normalization, additional anatomical images such as X-ray CT or MRI scans are needed. In this study, we developed an SPM method that enables us to perform spatial normalization without acquisition of additional anatomical data. Methods F-18 FP-CIT PET data was acquired from 8 normal rats. Emission PET data was collected for 1 h after injection of 1 mCi FP-CIT. Attenuation and scatter corrections were performed. List-mode data were sorted into sinograms and then reconstructed into 3-frame images using 3DRP and MAP reconstruction methods. Because initial uptake corresponds to the blood pool, we used the initial uptake image (~1200 s) as anatomical information of the brain and the second frame image was used as FP-CIT binding data. For efficient spatial normalization, the brain was extracted. A rat brain template was created by using the blood pool data. Individual PET data from the first frame was spatially normalized onto the template and the spatial normalization parameter was reapplied onto the FP-CIT binding data. To increase the signal-to-noise ratio, a 3-mm Gaussian kernel was used for smoothing. To validate the developed SPM method, PET data of a Parkinson's disease (PD) model was acquired. Two sample t-tests were performed to determine the difference in the binding potential (BP) between normal rats and PD models. Results An FP-CIT PET template for rat brain was constructed by using blood pool data without acquisition of additional anatomical data. For SPM validation, the difference in the BP between the normal and PD was determined (P < 0.005, K > 0). This result was in concord with the ROI analysis results. Conclusions The SPM method for FP-CIT PET data provides high localization accuracy in PD analysis. This method may be useful for assessment of PD and may augment the therapeutic effect of stem cells in PD