Subdural electrode recording is commonly used to evaluate intractable epilepsy. In order to accurately record electrical activity responsible for seizure, electrodes must be positioned precisely near targets of interest, often indicated preoperatively through imaging studies. To achieve accurate placement, a large craniotomy is used to expose the brain surface. With the intent of limiting the size and improving the location of craniotomy for electrode placement, we examined magnetic tracking for localization of electrode strips. Commercially available electrode strips were attached to specialized magnetic tracking sensors developed by Medtronic plc. In a rigid phantom we evaluated the strips to determine the accuracy of electrode placement on targets. We further conducted an animal study to evaluate the impact of magnetic field interference during data collection. The measured distance between the physical fiducial and lead coil of the electrode strip was 1.32 ± 1.03mm in the phantom experiments. The tracking system induces a very strong signal in the electrodes in the Very Low Frequency, an International Telecommunication Union (ITU) designated frequency band, from 3 kHz to 30 kHz. The results of the animal experiment demonstrated both tracking feasibility and data collection.
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.
Advances in neuroimaging have enhanced the clinician's ability to localize the epileptogenic zone in focal epilepsy, but 20-50 percent of these cases still remain unlocalized. Many sophisticated modalities have been used to study epilepsy, but scalp electrode recorded electroencephalography is particularly useful due to its noninvasive nature and excellent temporal resolution. This study is aimed at specific locations of scalp electrode EEG information for correlation with anatomical structures in the brain. 3D position localizing devices commonly used in virtual reality systems are used to digitize the coordinates of scalp electrodes in a standard clinical configuration. The electrode coordinates are registered with a high- resolution MRI dataset using a robust surface matching algorithm. Volume rendering can then be used to visualize the electrodes and electrode potentials interpolated over the scalp. The accuracy of the coordinate registration is assessed quantitatively with a realistic head phantom.
Grayscale inhomogeneities in magnetic resonance (MR) images cause significant problems in automated quantitative image analysis. Removal of such inhomogeneities is a difficult task, but it has been investigated by a number of different authors recently. The most common methods used involve some type of homomorphic filtering to create a smoothed version of the original image, which is then used as an estimate of the bias field to be removed from the image. Many investigators have implemented variations of this technique and have demonstrated their usefulness for a wide range of applications, but no investigator has yet attempted a systematic, quantitative study to describe the effects these algorithms have on images. This study introduces a quantitative paradigm for evaluating inhomogeneity correction algorithms by their performance on a constructed simulation image with different bias fields applied. We find that mean filter algorithms are more successful than median filter algorithms, and that larger kernel sizes than what are currently reported in the literature offer significant improvements in post-correction image quality.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.