Voxel significance mapping in epilepsy studies using subtraction ictal SPECT

Subtraction ictal SPECT coregistered to MRI (SISCOM) has been shown to aid epileptogenic localization and improve surgical outcomes in partial epilepsy patients. This paper reports a new method of identifying significant areas of epileptogenic activation in the SISCOM subtraction image taking into account normal variation between sequential Tc-99m Ethyl Cysteinate Diethylester SPECT scans of single individuals. The method uses the AIR 3.0 nonlinear registration software 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 nonlinearly registered to a patient's SISCOM subtraction image. Pixels in the patient subtraction image may then be evaluated based upon the statistical characteristics of corresponding pixels in the atlas. Validation experiments were performed to verify that local image variances are not constant across the image and that nonlinear registration preserves local image variances. SISCOM images created with the voxel variance method were rated higher in quality than the conventional image variance method in images from fifteen 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 without removing localizing information.