Assessment of Orbital Computed Tomography (CT) Imaging Biomarkers in Patients with Thyroid Eye Disease.

To understand potential orbital biomarkers generated from computed tomography (CT) imaging in patients with thyroid eye disease. This is a retrospective cohort study. From a database of an ongoing thyroid eye disease research study at our institution, we identified 85 subjects who had both clinical examination and laboratory records supporting the diagnosis of thyroid eye disease and concurrent imaging prior to any medical or surgical intervention. Patients were excluded if imaging quality or type was not amenable to segmentation. The images of 170 orbits were analyzed with the developed automated segmentation tool. The main outcome measure was to cross 25 CT structural metrics for each eye with nine clinical markers using a Kendall rank correlation test to identify significant relationships. The Kendall rank correlation test between automatically calculated CT metrics and clinical data demonstrated numerous correlations. Extraocular rectus muscle metrics, such as the average diameter of the superior, medial, and lateral rectus muscles, showed a strong correlation (p < 0.05) with loss of visual acuity and presence of ocular motility defects. Hertel measurements demonstrated a strong correlation (p < 0.05) with volumetric measurements of the optic nerve and other orbital metrics such as the crowding index and proptosis. Optic neuropathy was strongly correlated (p < 0.05) with an increase in the maximum diameter of the superior muscle. This novel method of automated imaging metrics may provide objective, rapid clinical information. This data may be useful for appreciation of severity of thyroid eye disease and recognition of risk factors of visual impairment from dysthyroid optic neuropathy from CT imaging.

Disambiguating the optic nerve from the surrounding cerebrospinal fluid: Application to MS-related atrophy.

PURPOSE: Our goal is to develop an accurate, automated tool to characterize the optic nerve (ON) and cerebrospinal fluid (CSF) to better understand ON changes in disease. METHODS: Multi-atlas segmentation is used to localize the ON and sheath on T2-weighted MRI (0.6 mm(3) resolution). A sum of Gaussian distributions is fit to coronal slice-wise intensities to extract six descriptive parameters, and a regression forest is used to map the model space to radii. The model is validated for consistency using tenfold cross-validation and for accuracy using a high resolution (0.4 mm(2) reconstructed to 0.15 mm(2)) in vivo sequence. We evaluated this model on 6 controls and 6 patients with multiple sclerosis (MS) and a history of optic neuritis. RESULTS: In simulation, the model was found to have an explanatory R-squared for both ON and sheath radii greater than 0.95. The accuracy of the method was within the measurement error on the highest possible in vivo resolution. Comparing healthy controls and patients with MS, significant structural differences were found near the ON head and the chiasm, and structural trends agreed with the literature. CONCLUSION: This is a first demonstration that the ON can be exclusively, quantitatively measured and separated from the surrounding CSF using MRI.

Trans-meningeal drug delivery to optic nerve ganglion cell axons using a nanoparticle drug delivery system.

The purpose of this study was to investigate if neuroprotective drugs can cross the optic nerve sheath in vitro. Four optic nerves were used for this study. Two porcine nerves were harvested at the time of euthanasia and two human nerves were obtained at the time of therapeutic globe enucleation. The optic nerve sheaths were dissected and placed as a membrane in a two chamber diffusion cell to test meningeal penetration by both brimonidine alone and brimonidine encapsulated in nanoparticle (NP-brimonidine). Brimonidine concentration was assayed by UV-vis spectrometer measurement of absorbance at 389 nm. Increasing concentration of brimonidine on the receiver side of the chamber was measured in both the brimonidine alone and the brimonidine encapsulated experiments. The human data were fitted with a two parameter exponential regression analysis (brimonidine alone donor r(2) = 0.87 and receiver r(2) = 0.80, NP-brimonidine donor r(2) = 0.79 and receiver r(2) = 0.84). Time constant (τ) was 10.2 h (donor) and 13.1 h (receiver) in the brimonidine study, and 24.0 h (donor) and 15.9 h (receiver) in the NP-brimonidine study. Encapsulated brimonidine had a longer time to reach equilibrium. Passage of brimonidine through the optic nerve sheath was demonstrated in the experiments. Increase in time constants when comparing the NP-brimonidine with the brimonidine curves in the human studiesindicates that diffusion is delayed by the initial parameter of drug being loaded in NP. Direct treatment of injured optic nerve axons may be possible by trans-meningeal drug diffusion.

A Safe Framework for Quantitative In Vivo Human Evaluation of Image Guidance.

Goal: We present a new framework for in vivo image guidance evaluation and provide a case study on robotic partial nephrectomy. Methods: This framework (called the "bystander protocol") involves two surgeons, one who solely performs the therapeutic process without image guidance, and another who solely periodically collects data to evaluate image guidance. This isolates the evaluation from the therapy, so that in-development image guidance systems can be tested without risk of negatively impacting the standard of care. We provide a case study applying this protocol in clinical cases during robotic partial nephrectomy surgery. Results: The bystander protocol was performed successfully in 6 patient cases. We find average lesion centroid localization error with our IGS system to be 6.5 mm in vivo compared to our prior result of 3.0 mm in phantoms. Conclusions: The bystander protocol is a safe, effective method for testing in-development image guidance systems in human subjects.

Image-guided robotic surgery: update on research and potential applications in urologic surgery.

PURPOSE OF REVIEW: New methods of imaging and image-guidance technology have the potential to provide surgeons with spatially accurate three-dimensional information about the location and anatomical relationships of critical subsurface structures and instrument position updated and displayed during the performance of surgery. Robotic platforms and technology in various forms continues to revolutionize surgery and will soon incorporate image guidance. RECENT RESEARCH: Image-guided surgery (IGS) for abdominal and urologic interventions presents complex engineering and surgical challenges along with potential benefits to surgeons and patients. Key concepts such as registration, localization, accuracy, and targeting error are necessary for surgeons to understand and utilize the potential of IGS. Standard robotic surgeries, such as partial nephrectomy and radical prostatectomy may soon incorporate IGS. SUMMARY: Research continues to explore the potential for combining image guidance and robotics to augment and improve a variety of surgical interventions.

Comparison and assessment of semi-automatic image segmentation in computed tomography scans for image-guided kidney surgery.

PURPOSE: Image segmentation is integral to implementing intraoperative guidance for kidney tumor resection. Results seen in computed tomography (CT) data are affected by target organ physiology as well as by the segmentation algorithm used. This work studies variables involved in using level set methods found in the Insight Toolkit to segment kidneys from CT scans and applies the results to an image guidance setting. METHODS: A composite algorithm drawing on the strengths of multiple level set approaches was built using the Insight Toolkit. This algorithm requires image contrast state and seed points to be identified as input, and functions independently thereafter, selecting and altering method and variable choice as needed. RESULTS: Semi-automatic results were compared to expert hand segmentation results directly and by the use of the resultant surfaces for registration of intraoperative data. Direct comparison using the Dice metric showed average agreement of 0.93 between semi-automatic and hand segmentation results. Use of the segmented surfaces in closest point registration of intraoperative laser range scan data yielded average closest point distances of approximately 1 mm. Application of both inverse registration transforms from the previous step to all hand segmented image space points revealed that the distance variability introduced by registering to the semi-automatically segmented surface versus the hand segmented surface was typically less than 3 mm both near the tumor target and at distal points, including subsurface points. CONCLUSIONS: Use of the algorithm shortened user interaction time and provided results which were comparable to the gold standard of hand segmentation. Further, the use of the algorithm's resultant surfaces in image registration provided comparable transformations to surfaces produced by hand segmentation. These data support the applicability and utility of such an algorithm as part of an image guidance workflow.

Toward image guided robotic surgery: system validation.

PURPOSE: Navigation for current robotic assisted surgical techniques is primarily accomplished through a stereo pair of laparoscopic camera images. These images provide standard optical visualization of the surface but provide no subsurface information. Image guidance methods allow the visualization of subsurface information to determine the current position in relationship to that of tracked tools. MATERIALS AND METHODS: A robotic image guided surgical system was designed and implemented based on our previous laboratory studies. A series of experiments using tissue mimicking phantoms with injected target lesions was performed. The surgeon was asked to resect "tumor" tissue with and without the augmentation of image guidance using the da Vinci robotic surgical system. Resections were performed and compared to an ideal resection based on the radius of the tumor measured from preoperative computerized tomography. A quantity called the resection ratio, that is the ratio of resected tissue compared to the ideal resection, was calculated for each of 13 trials and compared. RESULTS: The mean +/- SD resection ratio of procedures augmented with image guidance was smaller than that of procedures without image guidance (3.26 +/- 1.38 vs 9.01 +/- 1.81, p <0.01). Additionally, procedures using image guidance were shorter (average 8 vs 13 minutes). CONCLUSIONS: It was demonstrated that there is a benefit from the augmentation of laparoscopic video with updated preoperative images. Incorporating our image guided system into the da Vinci robotic system improved overall tissue resection, as measured by our metric. Adding image guidance to the da Vinci robotic surgery system may result in the potential for improvements such as the decreased removal of benign tissue while maintaining an appropriate surgical margin.

Feasibility study for image-guided kidney surgery: assessment of required intraoperative surface for accurate physical to image space registrations.

A notable complication of applying current image-guided surgery techniques of soft tissue to kidney resections (nephrectomies) is the limited field of view of the intraoperative kidney surface. This limited view constrains the ability to obtain a sufficiently geometrically descriptive surface for accurate surface-based registrations. The authors examined the effects of the limited view by using two orientations of a kidney phantom to model typical laparoscopic and open partial nephrectomy views. Point-based registrations, using either rigidly attached markers or anatomical landmarks as fiducials, served as initial alignments for surface-based registrations. Laser range scanner (LRS) obtained surfaces were registered to the phantom's image surface using a rigid iterative closest point algorithm. Subsets of each orientation's LRS surface were used in a robustness test to determine which parts of the surface yield the most accurate registrations. Results suggest that obtaining accurate registrations is a function of the percentage of the total surface and of geometric surface properties, such as curvature. Approximately 28% of the total surface is required regardless of the location of that surface subset. However, that percentage decreases when the surface subset contains information from opposite ends of the surface and/or unique anatomical features, such as the renal artery and vein.

Robust surface registration using salient anatomical features for image-guided liver surgery: algorithm and validation.

A successful surface-based image-to-physical space registration in image-guided liver surgery (IGLS) is critical to provide reliable guidance information to surgeons and pertinent surface displacement data for use in deformation correction algorithms. The current protocol used to perform the image-to-physical space registration involves an initial pose estimation provided by a point based registration of anatomical landmarks identifiable in both the preoperative tomograms and the intraoperative presentation. The surface based registration is then performed via a traditional iterative closest point (ICP) algorithm between the preoperative liver surface, segmented from the tomographic image set, and an intraoperatively acquired point cloud of the liver surface provided by a laser range scanner. Using this more conventional method, the registration accuracy can be compromised by poor initial pose estimation as well as tissue deformation due to the laparotomy and liver mobilization performed prior to tumor resection. In order to increase the robustness of the current surface-based registration method used in IGLS, we propose the incorporation of salient anatomical features, identifiable in both the preoperative image sets and intraoperative liver surface data, to aid in the initial pose estimation and play a more significant role in the surface-based registration via a novel weighting scheme. Examples of such salient anatomical features are the falciform groove region as well as the inferior ridge of the liver surface. In order to validate the proposed weighted patch registration method, the alignment results provided by the proposed algorithm using both single and multiple patch regions were compared with the traditional ICP method using six clinical datasets. Robustness studies were also performed using both phantom and clinical data to compare the resulting registrations provided by the proposed algorithm and the traditional method under conditions of varying initial pose. The results provided by the robustness trials and clinical registration comparisons suggest that the proposed weighted patch registration algorithm provides a more robust method with which to perform the image-to-physical space registration in IGLS. Furthermore, the implementation of the proposed algorithm during surgical procedures does not impose significant increases in computation or data acquisition times.

Laparoscopic surface scanning and subsurface targeting: implications for image-guided laparoscopic liver surgery.

Segmental liver resection and locoregional ablative therapies are dependent upon accurate tumor localization to ensure safety as well as acceptable oncologic results. Because of the liver's limited external landmarks and complex internal anatomy, such tumor localization poses a technical challenge. Image guided therapies (IGT) address this problem by mapping the real-time, intraoperative position of surgical instruments onto preoperative tomographic imaging through a process called registration. Accuracy is critical to IGT and is a function of: 1) the registration technique, 2) the tissue characteristics, and 3) imaging techniques. The purpose of this study is to validate a novel method of registration using an endoscopic Laser Range Scanner (eLRS) and demonstrate its applicability to laparoscopic liver surgery. Six radiopaque targets were inserted into an ex-vivo bovine liver and a computed tomography (CT) scan was obtained. Using the eLRS, the liver surface was scanned and a surface-based registration was constructed to predict the position of the intraparenchymal targets. The target registration error (TRE) achieved using our surface-based registration was 2.4 +/- 1.0 mm. A comparable TRE using traditional fiducial-based registration was 2.6 +/- 1.7 mm. Compared to traditional fiducial-based registration, laparoscopic surface scanning is able to predict the location of intraparenchymal liver targets with similar accuracy and rate of data acquisition.

Characterization of tracked radiofrequency ablation in phantom.

In radiofrequency ablation (RFA), successful therapy requires accurate, image-guided placement of the ablation device in a location selected by a predictive treatment plan. Current planning methods rely on geometric models of ablations that are not sensitive to underlying physical processes in RFA. Implementing plans based on computational models of RFA with image-guided techniques, however, has not been well characterized. To study the use of computational models of RFA in planning needle placement, this work compared ablations performed with an optically tracked RFA device with corresponding models of the ablations. The calibration of the tracked device allowed the positions of distal features of the device, particularly the tips of the needle electrodes, to be determined to within 1.4 +/- 0.6 mm of uncertainty. Ablations were then performed using the tracked device in a phantom system based on an agarose-albumin mixture. Images of the sliced phantom obtained from the ablation experiments were then compared with the predictions of a bioheat transfer model of RFA, which used the positional data of the tracked device obtained during ablation. The model was demonstrated to predict 90% of imaged pixels classified as being ablated. The discrepancies between model predictions and observations were analyzed and attributed to needle tracking inaccuracy as well as to uncertainties in model parameters. The results suggest the feasibility of using finite element modeling to plan ablations with predictable outcomes when implemented using tracked RFA.

Concepts and preliminary data toward the realization of image-guided liver surgery.

Image-guided surgery provides navigational assistance to the surgeon by displaying the surgical probe position on a set of preoperative tomograms in real time. In this study, the feasibility of implementing image-guided surgery concepts into liver surgery was examined during eight hepatic resection procedures. Preoperative tomographic image data were acquired and processed. Accompanying intraoperative data on liver shape and position were obtained through optically tracked probes and laser range scanning technology. The preoperative and intraoperative representations of the liver surface were aligned using the iterative closest point surface matching algorithm. Surface registrations resulted in mean residual errors from 2 to 6 mm, with errors of target surface regions being below a stated goal of 1 cm. Issues affecting registration accuracy include liver motion due to respiration, the quality of the intraoperative surface data, and intraoperative organ deformation. Respiratory motion was quantified during the procedures as cyclical, primarily along the cranial-caudal direction. The resulting registrations were more robust and accurate when using laser range scanning to rapidly acquire thousands of points on the liver surface and when capturing unique geometric regions on the liver surface, such as the inferior edge. Finally, finite element models recovered much of the observed intraoperative deformation, further decreasing errors in the registration. Image-guided liver surgery has shown the potential to provide surgeons with important navigation aids that could increase the accuracy of targeting lesions and the number of patients eligible for surgical resection.

A novel method for texture-mapping conoscopic surfaces for minimally invasive image-guided kidney surgery.

PURPOSE: Organ-level registration is critical to image-guided therapy in soft tissue. This is especially important in organs such as the kidney which can freely move. We have developed a method for registration that combines three-dimensional locations from a holographic conoscope with an endoscopically obtained textured surface. By combining these data sources clear decisions as to the tissue from which the points arise can be made. METHODS: By localizing the conoscope's laser dot in the endoscopic space, we register the textured surface to the cloud of conoscopic points. This allows the cloud of points to be filtered for only those arising from the kidney surface. Once a valid cloud is obtained we can use standard surface registration techniques to perform the image-space to physical-space registration. Since our methods use two distinct data sources we test for spatial accuracy and characterize temporal effects in phantoms, ex vivo porcine and human kidneys. In addition we use an industrial robot to provide controlled motion and positioning for characterizing temporal effects. RESULTS: Our initial surface acquisitions are hand-held. This means that we take approximately 55 s to acquire a surface. At that rate we see no temporal effects due to acquisition synchronization or probe speed. Our surface registrations were able to find applied targets with submillimeter target registration errors. CONCLUSION: The results showed that the textured surfaces could be reconstructed with submillimetric mean registration errors. While this paper focuses on kidney applications, this method could be applied to any anatomical structures where a line of sight can be created via open or minimally invasive surgical techniques.

Dynamic, three-dimensional optical tracking of an ablative laser beam.

Surgical resection remains the treatment of choice for brain tumors with infiltrating margins but is currently limited by visual discrimination between normal and neoplastic marginal tissues during surgery. Imaging modalities such as computed tomography, magnetic resonance, positron emission tomography, and optical techniques can accurately localize tumor margins. We believe coupling the fine resolution of current imaging techniques with the precise cutting of midinfrared lasers through image-guided neurosurgery can greatly enhance tumor margin resection. This paper describes a feasibility study designed to optically track in three-dimensional space the articulated arm delivery of a noncontact ablative laser beam. To enable optical tracking of the laser beam focus, infrared-emitting diodes (IREDs) were attached to a handpiece machined for the distal end of the articulated arm of a surgical carbon dioxide laser. Crosstalk between the ablative laser beam and the tracking diodes was measured. The geometry of the adapted laser handpiece was characterized to track an externally attached passive tip and the laser beam focus. Target localization accuracies were assessed for both instrument points-of-interest and the sources of tracking errors were investigated. Stray infrared laser light did not affect optical tracking accuracy. The mean target registration errors while optically tracking the laser handpiece with a passive tip and the laser beam focus were 1.31+/-0.50 mm and 2.31+/-0.92 mm, respectively, and were equivalent to the errors tracking a 24-IRED pen probe from Northern Digital in a side-by-side comparison. The majority of error during ablation tracking derived from registration accuracy between physical space and the defined space of the ablation phantom and from an inability to freehand align the laser focus with the target in a consistent manner. While their magnitudes depend on spatial details of the tracking setup (e.g., number and distribution of fiducial points, working distance from the camera, etc.), these errors are inherent to any freehand laser surgery.

Development of a three-dimensional freehand endorectal ultrasound system for use in rectal cancer imaging.

The initial study reporting the accuracy of an optically tracked endorectal ultrasound (TERUS) probe for the purpose of improving the staging of rectal cancer is presented here. In this work we describe the need for a more accurate ERUS system and why the incorporation of image guidance makes this goal feasible. A rectal phantom was constructed with five targets placed in positions where tumors normally occur. The locations of these targets were found using two different imaging modalities, CT and ultrasound, and the target registration error (TRE) between these two image sets was calculated. The average TRE of 33 image captures of the five targets using TERUS was 2.1 mm. This is a promising outcome because the desired tumor margins for rectal cancer are on the order of centimeters. These preliminary results support the proof of concept for a TERUS system that should improve ultrasound imaging in rectal cancer.

Compensating for intraoperative soft-tissue deformations using incomplete surface data and finite elements.

Image-guided liver surgery requires the ability to identify and compensate for soft tissue deformation in the organ. The predeformed state is represented as a complete three-dimensional surface of the organ, while the intraoperative data is a range scan point cloud acquired from the exposed liver surface. The first step is to rigidly align the coordinate systems of the intraoperative and preoperative data. Most traditional rigid registration methods minimize an error metric over the entire data set. In this paper, a new deformation-identifying rigid registration (DIRR) is reported that identifies and aligns minimally deformed regions of the data using a modified closest point distance cost function. Once a rigid alignment has been established, deformation is accounted for using a linearly elastic finite element model (FEM) and implemented using an incremental framework to resolve geometric nonlinearities. Boundary conditions for the incremental formulation are generated from intraoperatively acquired range scan surfaces of the exposed liver surface. A series of phantom experiments is presented to assess the fidelity of the DIRR and the combined DIRR/FEM approaches separately. The DIRR approach identified deforming regions in 90% of cases under conditions of realistic surgical exposure. With respect to the DIRR/FEM algorithm, subsurface target errors were correctly located to within 4 mm in phantom experiments.

In vitro assessment of image-guided otologic surgery: submillimeter accuracy within the region of the temporal bone.

OBJECTIVES: Application of image-guided surgery to otology has been limited by the need for submillimeter accuracy via a fiducial system that is easily usable (noninvasive and nonobstructive). METHODS: A dental bite-block was fitted with a rigid frame with 7 fiducial markers surrounding each external ear. The temporal bones of 3 cadaveric skulls were removed and replaced with surgical targets arranged in a bull's-eye pattern about the centroid of each temporal bone. The surgical targets were identified both within CT scans and in physical space using an infrared optical tracking system. The difference between positions in CT space versus physical space was calculated as target registration error. RESULTS: A total of 234 independent target registration errors were calculated. Mean +/- standard deviation = 0.73 mm +/- 0.25 mm. CONCLUSIONS: These findings show that image-guided otologic surgery with submillimeter accuracy is achievable with a minimally invasive fiducial frame. Significance In vivo validation of the system is ongoing. With such validation, this system may facilitate clinically applicable image-guided otologic surgery. EBM RATING: A.

Transorbital target localization with augmented ophthalmologic surgical endoscopy.

PURPOSE: Access to the space behind the eyeball is limited by the position of the globe anteriorly, the neurovascular structures embedded in fat posteriorly, and the tight bony confine of the orbit. These anatomical relationships have impeded application of minimally invasive procedures to the region, such as foreign body removal, tumor biopsy, or the administration of medical therapy directly to the optic nerve. An image-guided system was developed using a magnetically tracked flexible endoscope to navigate behind the eye, with the aim of enabling accurate transorbital surgery to user-specified target locations. METHODS: Targets were defined by microspherical bulbs containing water or gadolinium contrast, with differing visible coloring agent. Six living pigs were anesthetized and two microspheres of differing color and contrast content were implanted in the fat tissue of each orbit. Preoperative T1-weighted MRI volumes were obtained and registered intraoperatively. The system capabilities were tested with a series of targeted surgical interventions. The surgeon was required to navigate the endoscope to each lucent microsphere and identify it by color. For three pigs, 3D/2D registration was performed such that the target's image volume coordinates were used to display its location on real-time endoscope video. RESULTS: The ophthalmologic surgeon was able to correctly identify every target by color, with average intervention time of 24.2 min without enhancement and 3.2 min with enhancement. This difference is highly statistically significant [Formula: see text] for reduction in localization time. CONCLUSIONS: Accurate transorbital target localization is possible in-vivo using image-guided transorbital endoscopy, while endoscopic enhancement through the use of video augmentation significantly reduces procedure time.

Constructing a statistical atlas of the radii of the optic nerve and cerebrospinal fluid sheath in young healthy adults.

Optic neuritis is a sudden inflammation of the optic nerve (ON) and is marked by pain on eye movement, and visual symptoms such as a decrease in visual acuity, color vision, contrast and visual field defects. The ON is closely linked with multiple sclerosis (MS) and patients have a 50% chance of developing MS within 15 years. Recent advances in multi-atlas segmentation methods have omitted volumetric assessment. In the past, measuring the size of the ON has been done by hand. We utilize a new method of automatically segmenting the ON to measure the radii of both the ON and surrounding cerebrospinal fluid (CSF) sheath to develop a normative distribution of healthy young adults. We examine this distribution for any trends and find that ON and CSF sheath radii do not vary between 20-35 years of age and between sexes. We evaluate how six patients suffering from optic neuropathy compare to this distribution of controls. We find that of these six patients, five of them qualitatively differ from the normative distribution which suggests this technique could be used in the future to distinguish between optic neuritis patients and healthy controls.

Incorporation of a laser range scanner into image-guided liver surgery: surface acquisition, registration, and tracking.

As image guided surgical procedures become increasingly diverse, there will be more scenarios where point-based fiducials cannot be accurately localized for registration and rigid body assumptions no longer hold. As a result, procedures will rely more frequently on anatomical surfaces for the basis of image alignment and will require intraoperative geometric data to measure and compensate for tissue deformation in the organ. In this paper we outline methods for which a laser range scanner may be used to accomplish these tasks intraoperatively. A laser range scanner based on the optical principle of triangulation acquires a dense set of three-dimensional point data in a very rapid, noncontact fashion. Phantom studies were performed to test the ability to link range scan data with traditional modes of image-guided surgery data through localization, registration, and tracking in physical space. The experiments demonstrate that the scanner is capable of localizing point-based fiducials to within 0.2 mm and capable of achieving point and surface based registrations with target registration error of less than 2.0 mm. Tracking points in physical space with the range scanning system yields an error of 1.4 +/- 0.8 mm. Surface deformation studies were performed with the range scanner in order to determine if this device was capable of acquiring enough information for compensation algorithms. In the surface deformation studies, the range scanner was able to detect changes in surface shape due to deformation comparable to those detected by tomographic image studies. Use of the range scanner has been approved for clinical trials, and an initial intraoperative range scan experiment is presented. In all of these studies, the primary source of error in range scan data is deterministically related to the position and orientation of the surface within the scanner's field of view. However, this systematic error can be corrected, allowing the range scanner to provide a rapid, robust method of acquiring anatomical surfaces intraoperatively.