UNLABELLED: The present study aimed to quantify which image reconstruction conditions for normal databases and patients affect statistical brain function image analysis using an easy z score imaging system (eZIS) and 3-dimensional stereotactic surface projections (3D-SSP). METHODS: We constructed normal databases based on cerebral perfusion SPECT images obtained from 15 healthy individuals. Each normal database was created with the following unique conditions: a variable Butterworth filter cutoff frequency (fc) with and without scatter and attenuation corrections. To simulate patient data, we selected 1 dataset from among those created from the 15 healthy individuals. The simulated patient data were designed to include hypoperfused regions with prespecified volumes. Using 3D-SSP and eZIS, we compared how the above processing conditions affect the distribution of SD in normal database images and the accuracy of detecting specific regions. RESULTS: The SD for the SPECT images increased with the fc of the Butterworth filter. The z score decreased by 30% for 3D-SSP and by 14% for eZIS, indicating that the prefilter significantly affected z scores. The accuracy of detecting the hypoperfused regions was significantly influenced by the fc; 3D-SSP decreased by 7.51%, and eZIS decreased by 55.34%. The detection accuracy with eZIS, which involves a smoothing process, was significantly decreased. The error of the area of hypoperfused regions was minimized when normal database and patient data were both corrected for scatter and attenuation. CONCLUSION: When the reconstruction conditions (fc, scatter correction, and attenuation correction) at normal database creation differed from those at patient data processing, the z scores widely underestimated the analytic results because the SD varied according to the reconstruction conditions. The accuracy of brain function image analysis can be improved by considering the reconstruction conditions and correcting for scatter and attenuation on both normal databases and patient data.
UNLABELLED: The present study aimed to quantify which image reconstruction conditions for normal databases and patients affect statistical brain function image analysis using an easy z score imaging system (eZIS) and 3-dimensional stereotactic surface projections (3D-SSP). METHODS: We constructed normal databases based on cerebral perfusion SPECT images obtained from 15 healthy individuals. Each normal database was created with the following unique conditions: a variable Butterworth filter cutoff frequency (fc) with and without scatter and attenuation corrections. To simulate patient data, we selected 1 dataset from among those created from the 15 healthy individuals. The simulated patient data were designed to include hypoperfused regions with prespecified volumes. Using 3D-SSP and eZIS, we compared how the above processing conditions affect the distribution of SD in normal database images and the accuracy of detecting specific regions. RESULTS: The SD for the SPECT images increased with the fc of the Butterworth filter. The z score decreased by 30% for 3D-SSP and by 14% for eZIS, indicating that the prefilter significantly affected z scores. The accuracy of detecting the hypoperfused regions was significantly influenced by the fc; 3D-SSP decreased by 7.51%, and eZIS decreased by 55.34%. The detection accuracy with eZIS, which involves a smoothing process, was significantly decreased. The error of the area of hypoperfused regions was minimized when normal database and patient data were both corrected for scatter and attenuation. CONCLUSION: When the reconstruction conditions (fc, scatter correction, and attenuation correction) at normal database creation differed from those at patient data processing, the z scores widely underestimated the analytic results because the SD varied according to the reconstruction conditions. The accuracy of brain function image analysis can be improved by considering the reconstruction conditions and correcting for scatter and attenuation on both normal databases and patient data.