Gil J. Ettinger

An automatic registration method for frameless stereotaxy, image guided surgery, and enhanced reality visualization

There is a need for frameless guidance systems to help surgeons plan the exact location for incisions, to define the margins of tumors, and to precisely identify locations of neighboring critical structures. The authors have developed an automatic technique for registering clinical data, such as segmented magnetic resonance imaging (MRI) or computed tomography (CT) reconstructions, with any view of the patient on the operating table. The authors demonstrate on the specific example of neurosurgery. The method enables a visual mix of live video of the patient and the segmented three-dimensional (3-D) MRI or CT model. This supports enhanced reality techniques for planning and guiding neurosurgical procedures and allows us to interactively view extracranial or intracranial structures nonintrusively. Extensions of the method include image guided biopsies, focused therapeutic procedures, and clinical studies involving change detection over time sequences of images.

Automatic identification of gray matter structures from MRI to improve the segmentation of white matter lesions

The segmentation of MRI scans of patients with white matter lesions (WML) is difficult because the MRI characteristics of WML are similar to those of gray matter. Intensity-based statistical classification techniques misclassify some WML as gray matter and some gray matter as WML. We developed a fast elastic matching algorithm that warps a reference data set containing information about the location of the gray matter into the approximate shape of the patients brain. The region of white matter was segmented after segmenting the cortex and deep gray matter structures. The cortex was identified by using a three-dimensional, region-growing algorithm that was constrained by anatomical, intensity gradient, and tissue class parameters. White matter and WML were then segmented without interference from gray matter by using a two-class minimum-distance classifier. Analysis of double-echo spin-echo MRI scans of 16 patients with clinically determined multiple sclerosis (MS) was carried out. The segmentation of the cortex and deep gray matter structures provided anatomical context. This was found to improve the segmentation of MS lesions by allowing correct classification of the white matter region despite the overlapping tissue class distributions of gray matter and MS lesion.

Quantitative follow-up of patients with multiple sclerosis using MRI: technical aspects.

A highly reproducible automated procedure for quantitative analysis of serial brain magnetic resonance (MR) images was developed for use in patients with multiple sclerosis (MS). The intracranial cavity (ICC) was identified on standard dual‐echo spin‐echo brain MR images using a supervised automated procedure. MR images obtained from one MS patient at 24 time points in the course of a 1‐year follow‐up were aligned with the images of one of the time points. Next, the contents of the ICC in each MR exam were segmented into four tissues, using a self‐adaptive statistical algorithm. Misclassifications due to partial voluming were corrected using a combination of morphologic operators and connectivity criteria. Finally, a connectivity detection algorithm was used to separate the tissue classified as lesions into individual entities. Registration, classification of the contents of the ICC, and identification of individual lesions are fully automatic. Only identification of the ICC requires operator interaction. In each MR exam, the program estimated volumes for the ICC, gray matter (GM), white matter (WM), white matter lesions (WML), and cerebrospinal fluid (CSF). The reproducibility of the system was superior to that of supervised segmentation, as evidenced by the coefficient of variation: CSF supervised 45.9% vs. automated 7.7%, GM 16.0% vs. 1.4%, WM 15.7% vs. 1.3%, and WML 39.5% vs 52.0%. Our results demonstrate that this computerized procedure allows routine reproducible quantitative analysis of large serial MRI data sets.J. Magn. Reson. Imaging 1999;9:519–530.

Evaluating and Validating an Automated Registration System for Enhanced Reality Visualization in Surgery

Frameless guidance systems are needed to help surgeons plan exact locations for incisions, define margins of tumors and precisely locate critical structures. We describe an automatic method for registering clinical data, such as segmented MRI or CT, with any view of the patient, demonstrated on neurosurgery examples. The method enables mixing live video of the patient with the segmented 3D MRI or CT model, supporting enhanced reality techniques for planning and guiding procedures, and for interactively, non-intrusively viewing internal structures. We detail a computational evaluation of the method’s performance, and clinical experiments using the system in actual neurosurgical cases.

Utilizing segmented MRI data in image-guided surgery

While the role and utility of Magnetic Resonance Images as a diagnostic tool are well established in current clinical practice, there are a number of emerging medical arenas in which MRI can play an equally important role. In this article, we consider the problem of image-guided surgery, and provide an overview of a series of techniques that we have recently developed in order to automatically utilize MRI-based anatomical reconstructions for surgical guidance and navigation.

Clinical Experience with a Hich Precision Image-Guided Neurosurgery System

We describe an image-guided neurosurgery system which we have successfully used on 70 cases in the operating room. The system is designed to achieve high positional accuracy with a simple and efficient interface that interferes little with the operating room’s usual procedures, but is general enough to use on a wide range of cases. It uses data from a laser scanner or a trackable probe to register segmented MR imagery to the patient’s position in the operating room, and an optical tracking system to track head motion and localize medical instruments. Output visualizations for the surgeon consist of an “enhanced reality display,” showing location of hidden internal structures, and an instrument tracking display, showing the location of instruments in the context of the MR imagery. Initial assessment of the system in the operating room indicates a high degree of robustness and accuracy.

Experimentation with a transcranial magnetic stimulation system for functional brain mapping

We describe functional brain mapping experiments using a transcranial magnetic stimulation (TMS) device. This device, when placed on a subjects scalp, stimulates the underlying neurons by generating focused magnetic field pulses. A brain mapping is then generated by measuring responses of different motor and sensory functions to this stimulation. The key process in generating this mapping is the association of the 3-D positions and orientations of the TMS probe on the scalp to a 3-D brain reconstruction such as is feasible with a magnetic resonance image (MRI). We have developed a registration system which not only generates functional brain maps using such a device, but also provides real-time feedback to guide the technician in placing the probe at appropriate points on the head to achieve the desired map resolution. Functional areas we have mapped are the motor and visual cortex. Validation experiments focus on repeatability tests for mapping the same subjects several times. Applications of the technique include neuroanatomy research, surgical planning and guidance, treatment and disease monitoring, and therapeutic procedures.

Automatic registration for multiple sclerosis change detection

The authors are developing an automated 3D change detection system which accurately registers medical imagery (e.g., MRI or CT) of the same patient from different times for diagnosing pathologies, monitoring treatment, and tracking tissue changes. The system employs a combination of energy-minimization registration techniques to achieve accurate and robust alignment of 3D data sets. The bases for the registration are 3D surfaces extracted from the 3D imagery. Resultant structural changes in the data are identified by using an adaptive segmentation technique to automatically determine tissue morphology. The novel contributions of this work are its end-to-end automation of the change detection process and its high accuracy in monitoring and highlighting such physiological changes. The authors have applied this system to a multiple sclerosis study in which each patient had been imaged over 20 times for the purpose of tracking lesion evolution. This report describes preliminary registration performance analysis using this data.<<ETX>>

Visual hemifield mapping using transcranial magnetic stimulation coregistered with cortical surfaces derived from magnetic resonance images

The perception of a visual stimulus can be inhibited by occipital transcranial magnetic stimulation. This visual suppression effect has been attributed to disruption in the cortical gray matter of primary visual cortex or in the fiber tracts leading to V1 from the thalamus. However, others have suggested that the visual suppression effect is caused by disruption in secondary visual cortex. Here the authors used a figure-eight coil, which produces a focal magnetic field, and a Quadropulse stimulator to produce visual suppression contralateral to the stimulated hemisphere in five normal volunteer subjects. The authors coregistered the stimulation sites with magnetic resonance images in these same subjects using optical digitization. The stimulation sites were mapped onto the surface of the occipital lobes in three-dimensional reconstructions of the cortical surface to show the distribution of the visual suppression effect. The results were consistent with disruption of secondary visual cortical areas.

Non-invasive functional brain mapping using registered transcranial magnetic stimulation

The authors describe a method for mapping the functional regions of the brain using a transcranial magnetic stimulation (TMS) device. This device, when placed on a subjects scalp, stimulates the underlying neurons by generating focused magnetic field pulses. A brain mapping is then generated by measuring responses of different motor and sensory functions to this stimulation. The key process in generating this mapping is the association of the 3D positions and orientations of the TMS probe on the scalp to a 3D brain reconstruction such as is feasible with a magnetic resonance image (MRI). The authors perform this matching process by (1) registering the subjects head position to an a priori MRI scan, (2) tracking the 3D position/orientation of the TMS probe, (3) transforming the TMS probe position/orientation to the MRI coordinate frame, and (4) tracking movements in the subjects head position to factor out any head motion. The resultant process generates a high resolution, accurate brain mapping which supports surgical planning, surgical guidance, neuroanatomy research, and psychiatric therapy. When compared to other functional imaging modalities, this approach exhibits much lower cost, greater portability, and more direct active control over the functional areas being studied.