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Image-guIded surgery based on preoperative images and intraoperative navigation has become the stan-dard of care for many neurosurgical procedures. A

key step in image-guided surgery is the accurate intra-operative alignment, commonly called “registration,” be tween preoperative MR images or CT scans and the in tra operative patient situation.

Three registration approaches are currently available to achieve proper alignment: 1) FBR, 2) IBR, and 3) SBR. Fiducial-based registration relies on anatomical landmarks

and/or fiducial markers affixed to the patient’s skin or skull prior to scanning. Intraoperatively, the surgeon touches the fiducial markers and/or anatomical landmarks with a tracked probe and pairs these points with their coun-terparts in the preoperative image.3,12,18,21 Image-based registration uses intraoperatively acquired images, such as ultrasonographic or MR images, instead of landmarks to establish image-to-patient alignment.1,20 Surface-based reg istration uses intraoperative 3D surface points from the patient’s anatomy, such as the face and forehead, to com-pute the image-to-patient transformation.4,6,8

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

Clinical articleReuben R. ShamiR, m.Sc.,1 moti FReiman, m.Sc.,1 Leo JoSkowicz, Ph.D.,1 SeRgey SPektoR, m.D., Ph.D.,2 anD yigaL ShoShan, m.D.2

1School of Engineering and Computer Science; and 2Department of Neurosurgery, Hadassah Medical Center, Hebrew University, Jerusalem, Israel

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 im-age.

Methods. The authors randomly selected 12 patients (8 supine and 4 in a lateral position) who underwent neuro-surgery 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. (DOI: 10.3171/2009.3.JNS081457)

key woRDS      •      image-guided therapy      •      surface-based registration      •      registration error

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Abbreviations used in this paper: FBR = fiducial-based registra-tion; IBR = image-based registration; SBR = surface-based registra-tion; SRE = surface registration error; TRE = target registration error.

This article contains some figures that are displayed in color on line but in black and white in the print edition.

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Surface-based registration has the following advan-tages over FBR and IBR: 1) it is marker free; 2) it is fast, intuitive, and easy to use; 3) it is not subject to manual pairing and localization errors; and 4) its accuracy is sur-geon independent given that the surface is automatically generated by the scanner.

Authors of recent studies have compared the accu-racy of FBR and landmark-based registration in a clinical environment.4,7,9–11,13–16,21,22 Their conclusions are inconsis-tent: some authors reported that SBR is significantly less accurate than FBR,7,15,16,21,22 whereas others showed that SBR’s accuracy is similar to that of FBR.4,9–11,13,14

A possible explanation for the different results is the variation in the selected registration fiducial and target locations. As shown in clinical studies, the fiducial setup and target location do influence targeting accuracy.17–19 Therefore, any direct comparison of the FBR and SBR methods may be biased to the specific fiducial and target locations selected in the study. Furthermore, researchers in all of these studies have measured the targeting error for only a few targets located on the head surface, and thus they have made incomplete error assessments, especially for targets inside the brain. Since the actual target loca-tions inside the brain are unknown, an error estimation method must be used to evaluate the localization error.

In the study on FBR by West et al.,19 patient head images were augmented by overlaid contours that showed the estimated TREs. This visualization provided addi-tional clinically relevant information that could help a neurosurgeon in the decision-making process.

The goals in the present study on SBR were: 1) to quantify the SBR error in a clinical setup, 2) to estimate the targeting error for various locations inside the brain, and 3) to help neurosurgeons better assess the TRE by visualizing it on a patient’s head image.

MethodsWe randomly selected 12 patients who were sched-

uled to undergo image-guided neurosurgeries for various clinical indications. Patient consent was obtained in all cases.

Data Acquisition ProtocolAll patients underwent imaging the day before surgery.

Contrast-enhanced T1-weighted MR images with a 1-mm slice width (Signa, GE Medical Systems) were obtained in 11 patients (93%). Contrast CT head scans (helical Twin Flash scanner, Philips Medical Systems) with a 1.3-mm slice width were obtained in 1 patient (7%). Magnetic reso-nance images showed 256 × 256 × 200 voxels, with a voxel size of 0.93 × 0.93 × 1 mm; CT scans showed 512 × 512 × 100 voxels, with a voxel size similar to that used in the MR imaging. The imaging volume included areas of interest inside the brain and on the facial surface.

Before surgery, the head surface was automatically segmented and reconstructed, as reported in our previous study.8 Seventy-three thousand to 115,000 facial surface points were extracted from the MR imaging data sets, and 64,000 from the CT data set. After imaging and preop-erative planning, the patients were taken to the operating

room. After general anesthesia had been induced, 8 pa-tients were placed supine and 4 in a lateral position. Be-cause nasogastric tube fixation to the nose and eye drap-ing can significantly alter facial surface geometry around the eyes and nose, we performed surface scanning prior to this time.

One to 5 scans of each of the 12 patients’ faces were acquired with a root mean square point location accuracy of 0.3 ± 0.2 mm by using a commercial 3D optical surface scanner (faceSCAN II, Breuckmann) in the setup shown in Fig. 1. We acquired 34 different intraoperative scans in the 12 patients. For each patient, several scans were obtained using different scanner positions and environ-mental setups. We recorded all the data and brought it to our laboratory for further study.

Experimental ProtocolWe selected the anterior facial region from the pre-

operative surface scan and semiautomatically extracted points on the patient’s upper facial surface, including the forehead, eyes, and nose, by using custom software. The preoperative MR image or CT scan was then aligned to an intraoperative surface scan using a robust 2-step reg-istration algorithm (Fig. 2). The first step established a coarse correspondence based on 4 eye and nose bridge landmarks that are automatically extracted from both the MR image/CT scan and the facial surface scan data. The second step derived the registration transformation by aligning the point clouds from the MR image/CT scan and the facial surface with the robust iterative closest point registration method.2

Fig. 1. Photograph of the operating room setup showing the patient and surface scanner positions.

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To quantify the clinical accuracy of the SBR, we com-puted 2 error measurements: 1) the actual SRE, and 2) the estimated TRE. The actual SRE is the root-mean-square distance between facial surface points on the intraopera-tive facial surface scan and their closest corresponding points on the MR image/CT head surface after registra-tion. Note that when the registration is based on fiducials, a compatible error is known as the fiducial registration error. The actual TRE is the distance between the true lo-cation of a predefined target and its predicted location as computed with SBR. Since targets are located inside the brain, their true location and actual TRE cannot be mea-sured. Instead, we used the analytical formula described by Fitzpatrick et al.5 to estimate the TRE of various tar-get locations inside the brain. This method was originally designed for FBR under the assumption of independent, identically and normally distributed landmark localiza-tion errors. From a careful examination of this method we concluded that it could be used for SBR under similar assumptions.

Ten targets were selected on each patient image at increasing distances from the facial surface, and the cor-responding TRE estimations were computed. Based on these estimations, isoerror value contours were superim-posed on a representative MR image, providing an esti-mated TRE map of the brain. The results were tabulated separately for the supine and lateral positions.

We also investigated registration sensitivity to scan-ning conditions, including internal surface scanner param-eters, patient anatomical properties, and environmental parameters. Scanner parameters included the intensity of projected structured light and expected object brightness. Patient parameters included skin color and the presence/absence of facial hair. Environmental parameters included

operating room illumination, distance and orientation of the surface scanner with respect to the patient, and the tools, tubes, and other equipment around the patient’s head.

ResultsThe intraoperative surface scans were registered to the

MR imaging/CT data sets in < 1 minute on a standard per-sonal computer (2.4-GHz processor, Pentium 4, Intel).

Results, which were tabulated separately for the supine or lateral positions, are summarized in Table 1. There was no difference in the measured SREs and TREs in the 2 groups. The mean SRE over all 12 patients was 0.91 ± 0.35 mm, with a range of 0.69–1.31 mm. The mean estimated TRE increased as the target location lay further from the face surface scan (Fig. 3). For example, targets located at 60, 105, and 150 mm from the patient’s face had estimated TRE ranges of 1.53–2.84, 2.26–4.53, and 2.99–6.40 mm, respectively.

To better understand and visualize the clinical rel-evance of our results, we created estimated TRE maps by selecting a representative MR imaging slice and drawing on it the estimated TREs as isoerror value lines superim-posed on the image (Fig. 4).

An important finding in our study is the poor correla-tion between the SRE and the estimated TRE. In particu-lar, we observed that close SRE values do not necessarily predict close TRE values: Data Sets 9 and 11 have nearly identical SRE values (0.98 ± 0.38 mm and 0.99 ± 0.42 mm, respectively) but increasingly different TRE values (2.69 and 2.17 mm at 60 mm, 4.52 and 3.49 mm at 105 mm, and 6.40 and 4.88 mm at 150 mm, respectively). Our recent research corroborates these findings.17,18

We also noted that to obtain the best results from the surface scan data, the surface scanner should be placed at a distance of 0.9–1.2 m above the patient’s head, with the internal scanner parameters for light intensity set to the 75–100 U range and the expected scanned object param-eter set to “dark.” Factors such as patient skin color, facial hair, and adjacent surgical instruments did not affect reg-istration accuracy.

DiscussionOur results indicated that SBR has a low average SRE

of 0.91 mm in the facial zone. These results are excellent considering the intrinsic image resolution error and are comparable to those obtained using FBR based on com-parable MR imaging/CT data sets.3,12,18,19 The average es-timated TRE increases as the target location lies further away from the face, with errors of 4 mm in deep targets located 105 mm from the face.

Observed differences between the SRE and estimated TRE in SBR were also noteworthy. The SRE, which is the only accuracy measure provided by commercial neu-ronavigation software for both registration methods (SBR and FBR), underestimates the TRE for structures inside the brain and thus can be misleading. As our data show, the TRE of SBR for targets at a distance of > 60 mm can be relatively high and sometimes unacceptable, even in cases in which the SRE is low. The discrepancy may be crucial for small deep targets in, for example, the basal ganglia,

Fig. 2. Image demonstrating the face surface points from an intraop-erative surface scan (gray points) registered to forefront surface points extracted from a preoperative MR image (red/dark points).

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pineal region, and cerebral peduncle as well as the upper brainstem when it is approached from anterior to the coro-nal suture. Therefore, a surgeon using SBR should consider the SRE as an incomplete error assessment.

Our estimated TRE map (Fig. 4) shows the expected TRE at various zones of the brain image. This type of in-put can help the neurosurgeon select his or her approach (entry point and trajectory) using neuronavigation to a specific target while estimating the success rate (for ex-ample, correct diagnosis on stereotactic biopsy) and com-plication probability.

Our data also suggested that SBR is feasible in a busy operating room routine. The surface scanner is used before the skin incision, does not require direct contact or sterilization, and can be moved out of the operating field for the rest of the surgery. It requires < 5 minutes to set up, with the actual scanning taking only a few sec-onds. The surface scan-to-image data set registration process is fully automatic and relatively short, requiring < 2 minutes’ computation time. In comparison, standard neuronavigation systems with FBR require the surgeon to manually locate and touch the anatomical landmarks, which can be an inaccurate and time-consuming process, especially when performed by an inexperienced surgeon or when the patient is placed in the lateral position.

Another commercially available alternative is a tracked laser-beam line scanner (Z-touch, BrainLab). To acquire surface points, the surgeon points the laser beam to the pa-tient’s face and moves the beam in a predefined pattern to

record several hundred face surface points. This process is time consuming, user dependent, and error prone. In con-trast, the fast 3D surface scanner automatically acquires tens of thousands of points, which can increase registration

TABLE 1: Summary of SBR accuracy results in 12 patients*

Case No.†

No. of Intraop Surface

Scan Data Sets

Average No. of Face Surface Points (in thousands)

FRE Measured (mm)

TRE Estimated (mm)Extracted From Surface

ScansExtracted

From MRI/CT 60 mm‡ 105 mm‡ 150 mm‡

1 5 1.7 81 0.69 ± 0.23 1.58 2.59 3.632 4 2.8 90 1.08 ± 0.42 2.18 3.49 4.863 5 1.8 110 0.87 ± 0.33 1.85 3.10 4.394 2 2.1 100 0.76 ± 0.30 1.53 2.40 3.325 3 2.0 90 0.81 ± 0.30 1.88 3.04 4.256 3 1.5 94 0.83 ± 0.28 2.10 3.46 4.857§ 3 2.4 64 1.31 ± 0.55 2.84 4.53 6.298 2 2.1 115 0.85 ± 0.31 1.80 2.85 3.95

average for supine position — 2.1 93 0.91 ± 0.25 1.99 3.23 4.529 2 1.7 73 0.98 ± 0.38 2.69 4.52 6.40

10 2 2.1 96 0.90 ± 0.34 1.87 2.99 4.1611 2 2.2 100 0.99 ± 0.42 2.17 3.49 4.8812 1 2.9 104 1.09 ± 0.44 1.60 2.26 2.99

average for lat position — 2.1 92 0.95 ± 0.15 2.05 3.30 4.60overall average — 2.1 93 0.91 ± 0.35 2.00 ± 0.55 3.24 ± 0.95 4.53 ± 1.41

* Values represent the means ± SDs, unless indicated otherwise. Patients in the first 8 cases were supine, whereas those in the last 4 cases were placed in the lateral position. — = not applicable.† Also referred to as data set number.‡ Distance between the target and the patient’s face.§ Patient underwent preoperative CT scanning; all other patients underwent preoperative MR imaging.

Fig. 3. Graph revealing the mean (center line) and SD (interval) of the estimated TRE with respect to the location of the target. It is shows that as the target lies closer to the face, the SBR is more accurate and the TRE is decreasing.

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accuracy, and the surface is acquired in a few seconds. The registration result is surgeon independent as he or she has no effect on the generated surface.

The main drawback of our proposed method is the actual cost of a 3D surface scanner (~ €20,000, although the prices are dropping) with an accuracy of ± 0.3 mm or better. In addition, a custom stand to hold the scanner may be required in the operating room.

Another limitation of our method is that none of the existing TRE estimation methods are clinically validated for image-to-patient SBR. Current TRE estimations and appropriate visualization methods need further investiga-tion and development.

A key limitation of SBR is that it does not include location information from other parts of the head and thus can be of limited use for targets in the deep tem-poral, parietal, and occipital lobes, basal ganglia, pineal region, and upper brainstem. We envisage 2 possible so-lutions. The first is the addition of 1 or 2 skin fiducial markers in strategic head locations. Although the sur-geon will need to touch these fiducials with a tracked probe intraoperatively, the localization uncertainty will be much smaller than with anatomical landmarks.18 Fur-thermore, our and other studies indicated that adding a single fiducial marker in an optimal location can reduce the TRE by one-half.17,19 A second possible solution is to use additional uni- or bilateral ear scans. The outer ear surface provides lateral localization information that can be added to the frontal surface scan. Although the ear is flexible and deformable, we have observed that its outer surface and location with respect to the head is relatively stable. Thus, the outer ear surface can be extracted from the preoperative CT/MR image and matched with an in-traoperative side surface scan. The conjunction of both data sets may provide a better distribution of registration points and allow us to obtain a smaller TRE. For the su-pine position, bilateral external ear scanning may provide a combination of 2 opposite surface registration data sets that may enhance TREs. Further studies will be required to explore this idea.

We have integrated SBR into a system consisting of

a miniature robot for keyhole neurosurgery.8 We envisage using this method for other image-guided surgery appli-cations for which real-time tracking is not available.

ConclusionsA surface-based facial scanning–to–preoperative MR

imaging/CT data registration process is automatic, short, and feasible with good operating room integration. Our results on 12 patients showed that the surface registration error is ~ 1 mm in the facial zone for both supine and lat-eral patient positions. This level of accuracy is comparable to that obtained using FBR with skin fiducial markers in commercially available optical-based neuronavigation sys-tems. The SBR accuracy was better in the face and fron-tal zones, and the estimated registration error increased as the target location lay further away from the upper facial surface and deeper inside the brain. Visualization of the mean estimated TRE as isovalue lines superimposed on a patient’s MR image can assist the surgeon in decision making while planning and executing his neuronavigation procedures.

Disclosure

This research was supported in part by Magneton Grant No. 34377 from the Israeli Ministry of Industry and Trade.

Acknowledgment

The authors thank Mrs. Shifra Fraifeld at Hadassah-Hebrew University Medical Center for her editorial assistance in the prepara-tion of this manuscript.

References

1. Amstutz C, Caversaccio M, Kowal J, Bachler R, Nolte LP, Haus ler R, et al: A-mode ultrasound-based registration in com puter-aided surgery of the skull. Arch Otolaryngol Head Neck Surg 129:1310–1316, 2003

2. Besl PJ, McKay ND: A method for registration of 3D shapes. IEEE Trans Pattern Anal Mach Intell 14:239–256, 1992

3. Bjartmarz H, Rehncrona S: Comparison of accuracy and pre-cision between frame-based and frameless stereotactic navi-gation for deep brain stimulation electrode implantation. Ste-reotact Funct Neurosurg 85:235–242, 2007

4. Cao A, Thompson RC, Dumpuri P, Dawant BM, Galloway RL, Ding S, et al: Laser range scanning for image-guided neuro-surgery: investigation of image-to-physical space registrations. Med Phys 35:1593–1605, 2008

5. Fitzpatrick JM, West JB, Maurer CR Jr: Predicting error in rigid-body point-based registration. IEEE Trans Med Imag-ing 17:694–702, 1998

6. Grimson WL, Ettinger GJ, White SJ, Lozano-Perez T, Wells WM, Kikinis R: An automatic registration method for frame-less stereotaxy, image guided surgery, and enhanced reality visualization. IEEE Trans Med Imaging 15:129–140, 1996

7. Hoffmann J, Westendorff C, Leitner C, Bartz D, Reinert S: Validation of 3D-laser surface registration for image-guided cranio-maxillofacial surgery. J Craniomaxillofac Surg 33: 13–18, 2005

8. Joskowicz L, Shamir R, Freiman M, Shoham M, Zehavi E, Umansky F, et al: Image-guided system with miniature robot for precise positioning and targeting in keyhole neurosurgery. Comput Aided Surg 11:181–193, 2006

9. Marmulla R, Hassfeld S, Luth T, Mende U, Muhling J: Soft tis-

Fig. 4. Expected TRE map showing the mean estimated TRE as isovalue lines superimposed on a representative MR imaging slice.

R. R. Shamir et al.

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sue scanning for patient registration in image-guided surgery. Comput Aided Surg 8:70–81, 2003

10. Marmulla R, Muhling J, Wirtz CR, Hassfeld S: High-resolution laser surface scanning for patient registration in cranial com-puter-assisted surgery. Minim Invasive Neurosurg 47:72–78, 2004

11. Mascott CR, Sol JC, Bousquet P, Lagarrigue J, Lazorthes Y, Lauwers-Cances V: Quantification of true in vivo (applica-tion) accuracy in cranial image-guided surgery: influence of mode of patient registration. Neurosurgery 59 (1 Suppl 1): ONS146–ONS156, 2006

12. Maurer CR Jr, Fitzpatrick JM, Wang MY, Galloway RL Jr, Maciunas RJ, Allen GS: Registration of head volume images using implantable fiducial markers. IEEE Trans Med Imag-ing 16:447–462, 1997

13. Miga MI, Sinha TK, Cash DM, Galloway RL, Weil RJ: Corti-cal surface registration for image-guided neurosurgery using laser-range scanning. IEEE  Trans Med  Imaging 22:973–985, 2003

14. Raabe A, Krishnan R, Wolff R, Hermann E, Zimmermann M, Seifert V: Laser surface scanning for patient registration in in-tracranial image-guided surgery. Neurosurgery 50:797–803, 2002

15. Schicho K, Figl M, Seemann R, Donat M, Pretterklieber ML, Birkfellner W, et al: Comparison of laser surface scanning and fiducial marker-based registration in frameless stereo-taxy. Technical note. J Neurosurg 106:704–709, 2007

16. Schlaier J, Warnat J, Brawanski A: Registration accuracy and practicability of laser-directed surface matching. Comput Aid-ed Surg 7:284–290, 2002

17. Shamir RR, Joskowicz L, Spektor S, Shoshan Y: Localization and registration accuracy in image guided neurosurgery: a clinical study. Int J CARS 4:45–52, 2009

18. Shamir RR, Joskowicz L, Shoshan Y: Optimal landmarks selection and fiducial marker placement for minimal target registration error in image-guided neurosurgery. Proc SPIE 7261:[epub ahead of print], 2009

19. West JB, Fitzpatrick JM, Toms SA, Maurer CR Jr, Maciunas RJ: Fiducial point placement and the accuracy of point-based, rigid body registration. Neurosurgery 48:810–817, 2001

20. Wirtz CR, Tronnier VM, Bonsanto MM, Knauth M, Staubert A, Albert FK, et al: Image-guided neurosurgery with intra-operative MRI: update of frameless stereotaxy and radicality control. Stereotact Funct Neurosurg 68:39–43, 1997

21. Woerdeman PA, Willems PWA, Noordmans HJ, Berkelbach van der Sprenkel JW: Clinical accuracy of neuronavigation using registration methods based on point-pairs or surface matching. Int J CARS 1:297–299, 2006 (Abstract)

22. Woerdeman PA, Willems PW, Noordmans HJ, Tulleken CA, Berkelbach van der Sprenkel JW: Application accuracy in frameless image-guided neurosurgery: a comparison study of three patient-to-image registration methods. J Neurosurg 106:1012–1016, 2007

Manuscript submitted November 9, 2008.Accepted March 19, 2009.Please include this information when citing this paper: published

online April 24, 2009; DOI: 10.3171/2009.3.JNS081457.Portions of this work have been presented as an extended abstract

at the CARS 2007, Computer-Assisted Radiology and Surgery 21st International Congress in Berlin, Germany.

Address correspondence to: Reuben R. Shamir, M.Sc., School of Engineering and Computer Science, Hebrew University, Givat Ram Campus, Jerusalem, Israel 91904. email: rubke@cs.huji.ac.il.