[Neurological diseases and SPECT--analysis using easy Z-score imaging system (eZIS)].

We developed a method for automated diagnosis of brain perfusion SPECT and designated this method as an easy Z-score imaging system (eZIS). In this software program, voxel-by-voxel Z-score analysis after voxel normalization to global mean or cerebellar values; Z-score = ( [control mean] - [individual value] )/ (control SD) is performed. These Z-score maps are displayed by overlay on tomographic sections and by projection with averaged Z-score of 14mm thickness to surface rendering of the anatomically standardized MRI template. Anatomical standardization of SPECT images into a stereotactic space is performed using statistical parametric mapping (SPM) 2. This program has an advantage of capability of incorporation of SPM results into automated analysis of Z-score values as a volume of interest (VOI). A specific VOI can be determined by group comparison of SPECT images for patients with a neuropsychiatric disease with those for healthy volunteers using SPM. Even if a center can construct a normal database with good quality comprising a large number of healthy volunteers, other centers have not been able to use this normal database because of differences between the used gamma cameras, collimators and physical correction algorithms. Since SPECT exhibits greater variations in image quality among different centers than PET, conversion of SPECT images may be necessary for sharing a normal database. In this eZIS software, we incorporated a newly developed program for making it possible to share a normal database in SPECT studies. A Hoffman 3-dimensional brain phantom experiment was conducted to determine systematic differences between SPECT scanners. SPECT images for the brain phantom were obtained using two different scanners. Dividing these two phantom images after anatomical standardization by SPM created a 3-dimensional conversion map. The use of a conversion map obtained from SPECT images of the same phantom provided very similar SPECT data despite extreme differences between scanners. The present method may be useful for combining normal databases from different centers and greatly enhance the diagnostic value of brain SPECT imaging by standardization of data analysis using a common normal database.