Automatic localization of the subthalamic nucleus on patient-specific clinical MRI by incorporating 7 T MRI and machine learning: Application in deep brain stimulation.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has shown clinical potential for relieving the motor symptoms of advanced Parkinson's disease. While accurate localization of the STN is critical for consistent across-patients effective DBS, clear visualization of the STN under standard clinical MR protocols is still challenging. Therefore, intraoperative microelectrode recordings (MER) are incorporated to accurately localize the STN. However, MER require significant neurosurgical expertise and lengthen the surgery time. Recent advances in 7 T MR technology facilitate the ability to clearly visualize the STN. The vast majority of centers, however, still do not have 7 T MRI systems, and fewer have the ability to collect and analyze the data. This work introduces an automatic STN localization framework based on standard clinical MRIs without additional cost in the current DBS planning protocol. Our approach benefits from a large database of 7 T MRI and its clinical MRI pairs. We first model in the 7 T database, using efficient machine learning algorithms, the spatial and geometric dependency between the STN and its adjacent structures (predictors). Given a standard clinical MRI, our method automatically computes the predictors and uses the learned information to predict the patient-specific STN. We validate our proposed method on clinical T2 W MRI of 80 subjects, comparing with experts-segmented STNs from the corresponding 7 T MRI pairs. The experimental results show that our framework provides more accurate and robust patient-specific STN localization than using state-of-the-art atlases. We also demonstrate the clinical feasibility of the proposed technique assessing the post-operative electrode active contact locations.

The influence of varying the number of characters per row on the accuracy and reproducibility of the ETDRS visual acuity chart.

BACKGROUND: As part of an effort to improve upon the Snellen chart, we provide a standardized version of the ETDRS chart utilizing five characters in each row. The choice of five characters contradicts the recommended ten characters per row determined by the NAS-NRC, a committee established to provide guidelines for testing visual acuity. We set out to quantify the influence of varying the number of characters per line on the ETDRS chart with respect to the accuracy and reproducibility of visual acuity measurement. METHODS: Eleven different ETDRS charts were created, each with a different number of characters appearing in each row. A computer simulation was programmed to run 10,000 virtual patients, each with a unique visual acuity, false-positive and false-negative error value. RESULTS: Accuracy and reproducibility were found to roughly correlate with the number of characters present in each row, such that charts with 1, 3, 5, 7, 9, and 11 characters per row provided accuracy of 0.164, 0.094, 0.078, 0.073, 0.071, and 0.070 logMAR, respectively. A non-linear relationship was observed, with little improvement found beyond seven characters per row. In addition, charts with an even number of characters per row provided higher accuracy than their greater-number odd counterparts. In certain instances, accuracy and reproducibility were not well correlated. CONCLUSIONS: Increasing the number of characters per row in the ETDRS chart provides a trade-off between accuracy and test duration. An optimized chart layout would take these findings into account, allowing for the use of different chart layouts for clinical versus research settings.

Reduced risk trajectory planning in image-guided keyhole neurosurgery.

PURPOSE: The authors present and evaluate a new preoperative planning method and computer software designed to reduce the risk of candidate trajectories for straight rigid tool insertion in image-guided keyhole neurosurgery. METHODS: Trajectories are computed based on the surgeon-defined target and a candidate entry point area on the outer head surface on preoperative CT/MRI scans. A multiparameter risk card provides an estimate of the risk of each trajectory according to its proximity to critical brain structures. Candidate entry points in the outer head surface areas are then color-coded and displayed in 3D to facilitate selection of the most adequate point. The surgeon then defines and/or revised the insertion trajectory using an interactive 3D visualization of surrounding structures. A safety zone around the selected trajectory is also computed to visualize the expected worst-case deviation from the planned insertion trajectory based on tool placement errors in previous surgeries. RESULTS: A retrospective comparative study for ten selected targets on MRI head scans for eight patients showed a significant reduction in insertion trajectory risk. Using the authors' method, trajectories longer than 30 mm were an average of 2.6 mm further from blood vessels compared to the conventional manual method. Average planning times were 8.4 and 5.9 min for the conventional technique and the authors' method, respectively. Neurosurgeons reported improved understanding of possible risks and spatial relations for the trajectory and patient anatomy. CONCLUSIONS: The suggested method may result in safer trajectories, shorter preoperative planning time, and improved understanding of risks and possible complications in keyhole neurosurgery.

Microelectrode recording duration and spatial density constraints for automatic targeting of the subthalamic nucleus.

BACKGROUND: Accurate detection of the boundaries of the subthalamic nucleus (STN) in deep brain stimulation (DBS) surgery using microelectrode recording (MER) is considered to refine localization and may therefore improve clinical outcome. However, MER tends to extend operation time and its cost-utility balance has been debated. OBJECTIVES: To quantify the tradeoff between accuracy of STN localization and the spatial and temporal parameters of MER that effect the operation time using an automated detection method. METHODS: We retrospectively estimated the accuracy of STN detection on data from 100 microelectrode trajectories. Our dense (average step = 0.12 mm) and long (average duration = 22.5 s) MER data was downsampled in the spatial and temporal domains. Then, the STN borders were detected automatically on both the downsampled and original data and compared to each other. RESULTS: With a recording duration of 16 s, average accuracy for detecting STN entry ranged from 0.06 mm for a 0.1-mm step to 0.51 mm for a 1.0-mm step. Smaller effects were found along the temporal axis. For example, a 0.1-mm recording step yielded an STN entry average accuracy ranging from 0.06 mm for a 16-second recording duration to 0.16 mm for 0.1 s. CONCLUSIONS: STN entry detection error was about half of the step size. Sampling duration of STN activity can be minimized to 1 s/record without compromising accuracy. We conclude that bilateral DBS surgery time utilizing MER may be significantly shortened without compromising targeting accuracy.

A Comparison of different scoring terminations rules for visual acuity testing: from a computer simulation to a clinical study.

Purpose: To compare four visual acuity (VA) scoring termination rules. Methods: A computer simulation generated 30,000 virtual patients who underwent 10 repetitions for each of four termination rules, on both the Snellen and ETDRS charts (2.4 million tests performed in total). Three termination rules focused on the smallest character row: all characters were correctly identified (100%), one character was incorrectly identified (one miss) and 50% or more of the characters were correctly identified (50%). The forth termination rule used a calculation in which each character, when correctly recognized, contributed a proportional increment (per-letter). Accuracy, test-retest variability (TRV) and test duration were measured. Next, a clinical study was conducted in which 254 subjects underwent three repetitions of the ETDRS VA test from 4 m, and VA scores for each of the four scoring termination rules were calculated. Results: In the Snellen simulation, the mean accuracy of the 100%, one miss, 50% and per-letter termination rules in decimal was 0.23 (-0.16 logMAR), 0.11 (-0.09 logMAR), 0.10 (-0.08 logMAR), and -0.08 (0.08 logMAR) respectively; while with the ETDRS simulation, the mean accuracy in decimal was 0.34 (-0.22 logMAR), 0.14 (-0.11 logMAR), 0.07 (-0.06 logMAR), and 0.07 (-0.05 logMAR), respectively. For the ETDRS simulation, the per-letter had the lowest TRV values and the longest test duration. In the clinical study (n = 254), the reproducibility of the 100%, one miss, 50% and per-letter was 0.50, 0.53, 0.17, 0.14, respectively. Conclusions: Clinical study and simulation data both suggest that the 100% and one-miss termination rules have higher TRVs, while the 50% and per-letter demonstrated much tighter, and rather close, TRV values.

Microelectrode Recordings Validate the Clinical Visualization of Subthalamic-Nucleus Based on 7T Magnetic Resonance Imaging and Machine Learning for Deep Brain Stimulation Surgery.

BACKGROUND: Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a proven and effective therapy for the management of the motor symptoms of Parkinson's disease (PD). While accurate positioning of the stimulating electrode is critical for success of this therapy, precise identification of the STN based on imaging can be challenging. We developed a method to accurately visualize the STN on a standard clinical magnetic resonance imaging (MRI). The method incorporates a database of 7-Tesla (T) MRIs of PD patients together with machine-learning methods (hereafter 7 T-ML). OBJECTIVE: To validate the clinical application accuracy of the 7 T-ML method by comparing it with identification of the STN based on intraoperative microelectrode recordings. METHODS: Sixteen PD patients who underwent microelectrode-recordings guided STN DBS were included in this study (30 implanted leads and electrode trajectories). The length of the STN along the electrode trajectory and the position of its contacts to dorsal, inside, or ventral to the STN were compared using microelectrode-recordings and the 7 T-ML method computed based on the patient's clinical 3T MRI. RESULTS: All 30 electrode trajectories that intersected the STN based on microelectrode-recordings, also intersected it when visualized with the 7 T-ML method. STN trajectory average length was 6.2 ± 0.7 mm based on microelectrode recordings and 5.8 ± 0.9 mm for the 7 T-ML method. We observed a 93% agreement regarding contact location between the microelectrode-recordings and the 7 T-ML method. CONCLUSION: The 7 T-ML method is highly consistent with microelectrode-recordings data. This method provides a reliable and accurate patient-specific prediction for targeting the STN.

Patient-specific anatomical model for deep brain stimulation based on 7 Tesla MRI.

OBJECTIVE: Deep brain stimulation (DBS) requires accurate localization of the anatomical target structure, and the precise placement of the DBS electrode within it. Ultra-high field 7 Tesla (T) MR images can be utilized to create patient-specific anatomical 3D models of the subthalamic nuclei (STN) to enhance pre-surgical DBS targeting as well as post-surgical visualization of the DBS lead position and orientation. We validated the accuracy of the 7T imaging-based patient-specific model of the STN and measured the variability of the location and dimensions across movement disorder patients. METHODS: 72 patients who underwent DBS surgery were scanned preoperatively on 7T MRI. Segmentations and 3D volume rendering of the STN were generated for all patients. For 21 STN-DBS cases, microelectrode recording (MER) was used to validate the segmentation. For 12 cases, we computed the correlation between the overlap of the STN and volume of tissue activated (VTA) and the monopolar review for a further validation of the model's accuracy and its clinical relevancy. RESULTS: We successfully reconstructed and visualized the STN in all patients. Significant variability was found across individuals regarding the location of the STN center of mass as well as its volume, length, depth and width. Significant correlations were found between MER and the 7T imaging-based model of the STN (r = 0.86) and VTA-STN overlap and the monopolar review outcome (r = 0.61). CONCLUSION: The results suggest that an accurate visualization and localization of a patient-specific 3D model of the STN can be generated based on 7T MRI. The imaging-based 7T MRI STN model was validated using MER and patient's clinical outcomes. The significant variability observed in the STN location and shape based on a large number of patients emphasizes the importance of an accurate direct visualization of the STN for DBS targeting. An accurate STN localization can facilitate postoperative stimulation parameters for optimized patient outcome.

Comparison of Snellen and Early Treatment Diabetic Retinopathy Study charts using a computer simulation.

AIM: To compare accuracy, reproducibility and test duration for the Snellen and the Early Treatment Diabetic Retinopathy Study (ETDRS) charts, two main tools used to measure visual acuity (VA). METHODS: A computer simulation was programmed to run multiple virtual patients, each with a unique set of assigned parameters, including VA, false-positive and false-negative error values. For each virtual patient, assigned VA was randomly chosen along a continuous scale spanning the range between 1.0 to 0.0 logMAR units (equivalent to 20/200 to 20/20). Each of 30 000 virtual patients were run ten times on each of the two VA charts. RESULTS: Average test duration (expressed as the total number of characters presented during the test ±SD) was 12.6±11.1 and 31.2±14.7 characters, for the Snellen and ETDRS, respectively. Accuracy, defined as the absolute difference (± SD) between the assigned VA and the measured VA, expressed in logMAR units, was superior in the ETDRS charts: 0.12±0.14 and 0.08±0.08, for the Snellen and ETDRS charts, respectively. Reproducibility, expressed as test-retest variability, was superior in the ETDRS charts: 0.23±0.17 and 0.11±0.09 logMAR units, for the Snellen and ETDRS charts, respectively. CONCLUSION: A comparison of true (assigned) VA to measured VA, demonstrated, on average, better accuracy and reproducibility of the ETDRS chart, but at the penalty of significantly longer test duration. These differences were most pronounced in the low VA range. The reproducibility using a simulation approach is in line with reproducibility values found in several clinical studies.

Engineering the next generation of clinical deep brain stimulation technology.

Deep brain stimulation (DBS) has evolved into a powerful clinical therapy for a range of neurological disorders, but even with impressive clinical growth, DBS technology has been relatively stagnant over its history. However, enhanced collaborations between neural engineers, neuroscientists, physicists, neurologists, and neurosurgeons are beginning to address some of the limitations of current DBS technology. These interactions have helped to develop novel ideas for the next generation of clinical DBS systems. This review attempts collate some of that progress with two goals in mind. First, provide a general description of current clinical DBS practices, geared toward educating biomedical engineers and computer scientists on a field that needs their expertise and attention. Second, describe some of the technological developments that are currently underway in surgical targeting, stimulation parameter selection, stimulation protocols, and stimulation hardware that are being directly evaluated for near term clinical application.

Machine Learning Approach to Optimizing Combined Stimulation and Medication Therapies for Parkinson's Disease.

BACKGROUND: Deep brain stimulation (DBS) of the subthalamic region is an established therapy for advanced Parkinson's disease (PD). However, patients often require time-intensive post-operative management to balance their coupled stimulation and medication treatments. Given the large and complex parameter space associated with this task, we propose that clinical decision support systems (CDSS) based on machine learning algorithms could assist in treatment optimization. OBJECTIVE: Develop a proof-of-concept implementation of a CDSS that incorporates patient-specific details on both stimulation and medication. METHODS: Clinical data from 10 patients, and 89 post-DBS surgery visits, were used to create a prototype CDSS. The system was designed to provide three key functions: (1) information retrieval; (2) visualization of treatment, and; (3) recommendation on expected effective stimulation and drug dosages, based on three machine learning methods that included support vector machines, Naïve Bayes, and random forest. RESULTS: Measures of medication dosages, time factors, and symptom-specific pre-operative response to levodopa were significantly correlated with post-operative outcomes (P < 0.05) and their effect on outcomes was of similar magnitude to that of DBS. Using those results, the combined machine learning algorithms were able to accurately predict 86% (12/14) of the motor improvement scores at one year after surgery. CONCLUSIONS: Using patient-specific details, an appropriately parameterized CDSS could help select theoretically optimal DBS parameter settings and medication dosages that have potential to improve the clinical management of PD patients.

A method for predicting the outcomes of combined pharmacologic and deep brain stimulation therapy for Parkinson's disease.

Deep brain stimulation (DBS) is an established therapy for the management of advanced Parkinson's disease (PD). However, the coupled adjustment of pharmacologic therapy and stimulation parameter settings is a time-consuming process and treatment outcomes are not always optimal. In this study, we develop a linear function that relates the DBS parameters, the levodopa dosage, and patient-specific preoperative clinical data with the actual treatment motor outcomes. To this end, we incorporate image-based patient-specific computer models of the volume of tissue activated by DBS in a multilinear regression analysis (6 PD patients; 60 follow up visits). The resulting predictor function was highly correlated with the actual motor outcomes (r = 0.76; p < 0.05). These results demonstrate that the outcomes of a combined pharmacologic-DBS therapy can be predicted and may facilitate patient-specific treatment optimization for maximal benefits and minimal adverse effects.

Subthalamic nucleus long-range synchronization-an independent hallmark of human Parkinson's disease.

Beta-band synchronous oscillations in the dorsolateral region of the subthalamic nucleus (STN) of human patients with Parkinson's disease (PD) have been frequently reported. However, the correlation between STN oscillations and synchronization has not been thoroughly explored. The simultaneous recordings of 2390 multi-unit pairs recorded by two parallel microelectrodes (separated by fixed distance of 2 mm, n = 72 trajectories with two electrode tracks >4 mm STN span) in 57 PD patients undergoing STN deep brain stimulation surgery were analyzed. Automatic procedures were utilized to divide the STN into dorsolateral oscillatory and ventromedial non-oscillatory regions, and to quantify the intensity of STN oscillations and synchronicity. Finally, the synchronicity of simultaneously vs. non-simultaneously recorded pairs were compared using a shuffling procedure. Synchronization was observed predominately in the beta range and only between multi-unit pairs in the dorsolateral oscillatory region (n = 615). In paired recordings between sites in the dorsolateral and ventromedial (n = 548) and ventromedial-ventromedial region pairs (n = 1227), no synchronization was observed. Oscillation and synchronicity intensity decline along the STN dorsolateral-ventromedial axis suggesting a fuzzy border between the STN regions. Synchronization strength was significantly correlated to the oscillation power, but synchronization was no longer observed following shuffling. We conclude that STN long-range beta oscillatory synchronization is due to increased neuronal coupling in the Parkinsonian brain and does not merely reflect the outcome of oscillations at similar frequency. The neural synchronization in the dorsolateral (probably the motor domain) STN probably augments the pathological changes in firing rate and patterns of subthalamic neurons in PD patients.

Asymmetric right/left encoding of emotions in the human subthalamic nucleus.

Emotional processing is lateralized to the non-dominant brain hemisphere. However, there is no clear spatial model for lateralization of emotional domains in the basal ganglia. The subthalamic nucleus (STN), an input structure in the basal ganglia network, plays a major role in the pathophysiology of Parkinson's disease (PD). This role is probably not limited only to the motor deficits of PD, but may also span the emotional and cognitive deficits commonly observed in PD patients. Beta oscillations (12-30 Hz), the electrophysiological signature of PD, are restricted to the dorsolateral part of the STN that corresponds to the anatomically defined sensorimotor STN. The more medial, more anterior and more ventral parts of the STN are thought to correspond to the anatomically defined limbic and associative territories of the STN. Surprisingly, little is known about the electrophysiological properties of the non-motor domains of the STN, nor about electrophysiological differences between right and left STNs. In this study, microelectrodes were utilized to record the STN spontaneous spiking activity and responses to vocal non-verbal emotional stimuli during deep brain stimulation (DBS) surgeries in human PD patients. The oscillation properties of the STN neurons were used to map the dorsal oscillatory and the ventral non-oscillatory regions of the STN. Emotive auditory stimulation evoked activity in the ventral non-oscillatory region of the right STN. These responses were not observed in the left ventral STN or in the dorsal regions of either the right or left STN. Therefore, our results suggest that the ventral non-oscillatory regions are asymmetrically associated with non-motor functions, with the right ventral STN associated with emotional processing. These results suggest that DBS of the right ventral STN may be associated with beneficial or adverse emotional effects observed in PD patients and may relieve mental symptoms in other neurological and psychiatric diseases.

Fiducial optimization for minimal target registration error in image-guided neurosurgery.

This paper presents new methods for the optimal selection of anatomical landmarks and optimal placement of fiducial markers in image-guided neurosurgery. These methods allow the surgeon to optimally plan fiducial marker locations on routine diagnostic images before preoperative imaging and to intraoperatively select the set of fiducial markers and anatomical landmarks that minimize the expected target registration error (TRE). The optimization relies on a novel empirical simulation-based TRE estimation method built on actual fiducial localization error (FLE) data. Our methods take the guesswork out of the registration process and can reduce localization error without additional imaging and hardware. Our clinical experiments on five patients who underwent brain surgery with a navigation system show that optimizing one marker location and the anatomical landmarks configuration reduced the TRE. The average TRE values using the usual fiducials setup and using the suggested method were 4.7 mm and 3.2 mm, respectively. We observed a maximum improvement of 4 mm. Reducing the target registration error has the potential to support safer and more accurate minimally invasive neurosurgical procedures.

Geometrical analysis of registration errors in point-based rigid-body registration using invariants.

Point-based rigid registration is the method of choice for aligning medical datasets in diagnostic and image-guided surgery systems. The most clinically relevant localization error measure is the Target Registration Error (TRE), which is the distance between the image-defined target and the corresponding target defined on another image or on the physical anatomy after registration. The TRE directly depends on the Fiducial Localization Error (FLE), which is the discrepancy between the selected and the actual (unknown) fiducial locations. Since the actual locations of targets usually cannot be measured after registration, the TRE is often estimated by the Fiducial Registration Error (FRE), which is the RMS distance between the fiducials in both datasets after registration, or with Fitzpatrick's TRE (FTRE) formula. However, low FRE-TRE and FTRE-TRE correlations have been reported in clinical practice and in theoretical studies. In this article, we show that for realistic FLE classes, the TRE and the FRE are uncorrelated, regardless of the target location and the number of fiducials and their configuration, and regardless of the FLE magnitude distribution. We use a geometrical approach and classical invariant theory to model the FLE and derive its relation to the TRE and FRE values. We show that, for these FLE classes, the FTRE and TRE are also uncorrelated. Finally, we show with simulations on clinical data that the FRE-TRE correlation is low also in the neighborhood of the FLE-FRE invariant classes. Consequently, and contrary to common practice, the FRE and FTRE may not always be used as surrogates for the TRE.

Target and trajectory clinical application accuracy in neuronavigation.

BACKGROUND: Catheter, needle, and electrode misplacement in navigated neurosurgery can result in ineffective treatment and severe complications. OBJECTIVE: To assess the Ommaya ventricular catheter localization accuracy both along the planned trajectory and at the target. METHODS: We measured the localization error along the ventricular catheter and on its tip for 15 consecutive patients who underwent insertion of the Ommaya catheter surgery with a commercial neuronavigation system. The preoperative computed tomography/magnetic resonance images and the planned trajectory were aligned with the postoperative computed tomography images showing the Ommaya catheter. The localization errors along the trajectory and at the target were then computed by comparing the preoperative planned trajectory with the actual postoperative catheter position. The measured localization errors were also compared with the error reported by the navigation system. RESULTS: The mean localization errors at the target and entry point locations were 5.9 ± 4.3 and 3.3 ± 1.9 mm, respectively. The mean shift and angle between planned and actual trajectories were 1.6 ± 1.9 mm and 3.9 ± 4.7°, respectively. The mean difference between the localization error at the target and entry point was 3.9 ± 3.7 mm. The mean difference between the target localization error and the reported navigation system error was 4.9 ± 4.8 mm. CONCLUSION: The catheter localization errors have significant variations at the target and along the insertion trajectory. Trajectory errors may differ significantly from the errors at the target. Moreover, the single registration error number reported by the navigation system does not appropriately reflect the trajectory and target errors and thus should be used with caution to assess the procedure risk.

A method for planning safe trajectories in image-guided keyhole neurosurgery.

We present a new preoperative planning method for reducing the risk associated with insertion of straight tools in image-guided keyhole neurosurgery. The method quantifies the risks of multiple candidate trajectories and presents them on the outer head surface to assist the neurosurgeon in selecting the safest path. The surgeon can then define and/or revise the trajectory, add a new one using interactive 3D visualization, and obtain a quantitative risk measures. The trajectory risk is evaluated based on the tool placement uncertainty, on the proximity of critical brain structures, and on a predefined table of quantitative geometric risk measures. Our results on five targets show a significant reduction in trajectory risk and a shortening of the preoperative planning time as compared to the current routine method.

Surface-based facial scan registration in neuronavigation procedures: a clinical study.

OBJECT: Surface-based registration (SBR) with facial surface scans has been proposed as an alternative for the commonly used fiducial-based registration in image-guided neurosurgery. Recent studies comparing the accuracy of SBR and fiducial-based registration have been based on a few targets located on the head surface rather than inside the brain and have yielded contradictory conclusions. Moreover, no visual feedback is provided with either method to inform the surgeon about the estimated target registration error (TRE) at various target locations. The goals in the present study were: 1) to quantify the SBR error in a clinical setup, 2) to estimate the targeting error for many target locations inside the brain, and 3) to create a map of the estimated TRE values superimposed on a patient's head image. METHODS: The authors randomly selected 12 patients (8 supine and 4 in a lateral position) who underwent neurosurgery with a commercial navigation system. Intraoperatively, scans of the patients' faces were acquired using a fast 3D surface scanner and aligned with their preoperative MR or CT head image. In the laboratory, the SBR accuracy was measured on the facial zone and estimated at various intracranial target locations. Contours related to different TREs were superimposed on the patient's head image and informed the surgeon about the expected anisotropic error distribution. RESULTS: The mean surface registration error in the face zone was 0.9 +/- 0.35 mm. The mean estimated TREs for targets located 60, 105, and 150 mm from the facial surface were 2.0, 3.2, and 4.5 mm, respectively. There was no difference in the estimated TRE between the lateral and supine positions. The entire registration procedure, including positioning of the scanner, surface data acquisition, and the registration computation usually required < 5 minutes. CONCLUSIONS: Surface-based registration accuracy is better in the face and frontal zones, and error increases as the target location lies further from the face. Visualization of the anisotropic TRE distribution may help the surgeon to make clinical decisions. The observed and estimated accuracies and the intraoperative registration time show that SBR using the fast surface scanner is practical and feasible in a clinical setup.