Voxel significance mapping using local image variances in subtraction ictal spet

Subtraction ictal SPET co-registered to MRI (SISCOM) has been shown to aid epileptogenic localization and improve surgical outcome in partial epilepsy patients. This paper reports a method of identifying significant areas of epileptogenic activation in the SISCOM subtraction image, taking into account normal variation between sequential Tc^m-ethyl cysteinate diethylester SPET scans of single individuals, and attempts to assess the clinical value of statistical mapping in subtraction SPET. Non-linear inter-subject registration is used to combine a group of subtraction images into a common anatomical framework. A map of the pixel intensity standard deviation values in the subtraction images is created, and this map is non-linearly registered to a patient's SISCOM subtraction image. Pixels in the patient subtraction image were then evaluated based upon the statistical characteristics of corresponding pixels in the atlas. SISCOM images created with the voxel variance method were rated higher in quality than the conventional image variance method in 15 patients. No difference in localization rate was observed between the voxel variance mapping and image variance methods. The voxel significance mapping method was shown to improve the quality of clinical SISCOM images.