An open‐source automated platform for three‐dimensional visualization of subdural electrodes using CT‐MRI coregistration

Abstract

Objective Visualizing implanted subdural electrodes in 3D space can greatly aid planning, executing, and validating resection in epilepsy surgery. Coregistration software is available, but cost, complexity, insufficient accuracy or validation limit adoption. We present a fully automated open-source application, based upon a novel method using post-implant CT and post-implant MR images, for accurately visualizing intracranial electrodes in 3D space. Methods CT-MR rigid brain coregistration, MR non-rigid registration, and prior-based segmentation were carried out on 7 subjects. Post-implant CT, post-implant MR, and an external labeled atlas were then aligned in the same space. The coregistration algorithm was validated by manually marking identical anatomical landmarks on the post-implant CT and post-implant MR images. Following coregistration, distances between the center of the landmark masks on the post-implant MR and the coregistered CT images were calculated for all subjects. Algorithms were implemented in open-source software and translated into a “drag and drop” desktop application for Apple Mac OS X. Results Despite post-operative brain deformation, the method was able to automatically align intra-subject multi-modal images and segment cortical subregions so that all electrodes could be visualized on the parcellated brain. Manual marking of anatomical landmarks validated the coregistration algorithm with a mean misalignment distance of 2.87 ± 0.58 mm between the landmarks. Software was easily used by operators without prior image processing experience. Significance We demonstrate an easy to use, novel platform for accurately visualizing subdural electrodes in 3D space on a parcellated brain. We rigorously validated this method using quantitative measures. The method is unique because it involves no pre-processing, is fully automated, and freely available worldwide. A desktop application, as well as the source code, are both available for download on the International Epilepsy Electrophysiology Portal (https://www.ieeg.org/) for use and interactive refinement.

Seizure localization using ictal phase-locked high gamma

“…To define the resection boundaries and the position of implanted electrodes, preoperative volumetric MRIs (1.5T or 3T) were coregistered to postimplantation CT scans and postresection MRIs using the Advanced Normalization Tools, 23 C3D, 24 FSL (Oxford, UK), and AMIRA (FEI Figure 1 Diagnostic algorithm using the SOZ and PLHG metrics, and resulting surgical outcome classification…”

Localization of Metal Electrodes in the Intact Rat Brain Using Registration of 3D Microcomputed Tomography Images to a Magnetic Resonance Histology Atlas

“…The method we used to isolate the electrode implant from the CT images in our preparation was based on the observation that voxels representing the electrode implant had higher intensity values than anything else in the image (including the skull) and appeared with a very low probability. This feature of electrodes has been found in CT images of human deep brain stimulators as well (Azarion et al, 2014). Taking advantage of these features, the most straight-forward way to mask out the electrode implant was to estimate a probability density function of the intensity values in each of the CT image volumes.…”

“…This process differs from previously published CT-MR registration procedures that register CT images of an individual to MR images of that same individual (Azarion et al, 2014; Maintz et al, 1996; Mirzadeh et al, 2014; Princich et al, 2013; Vaquero et al, 2001), or registration procedures that register MR images of one individual to a canonical MR atlas (Sergejeva et al, 2015). Since metal artifacts prevented the acquisition of MR images from implanted rats, the registration process for the present application had to both cross modalities (CT to MR) and individual specimens (an individual CT scan to an atlas representing the average of 5 other specimens).…”

Data integration: Combined imaging and electrophysiology data in the cloud

“…The patient MR image is non-rigidly registered to the NIREP atlas using cross-correlation as the similarity metric. The high level of accuracy of our co-registration method was quantitatively confirmed [18]. The images are all in the patient’s original T1 image coordinate frame.…”

“…In addition to these iEEG tools, the web console also contains a BrainMapper application, which is an easy to use “drag and drop” application that automatically co-registers CT and MRI electrode implantation images in order to provide an accurate and reliable 3D reconstruction of the electrodes on the surface of a patient’s brain [18]. Figure 3a shows a snapshot of the BrainMapper application.…”