More accurate neuronavigation data provided by biomechanical modeling instead of rigid registration

Technical note

Full access

It is possible to improve neuronavigation during image-guided surgery by warping the high-quality preoperative brain images so that they correspond with the current intraoperative configuration of the brain. In this paper, the accuracy of registration results obtained using comprehensive biomechanical models is compared with the accuracy of rigid registration, the technology currently available to patients. This comparison allows investigation into whether biomechanical modeling provides good-quality image data for neuronavigation for a larger proportion of patients than rigid registration. Preoperative images for 33 neurosurgery cases were warped onto their respective intraoperative configurations using both the biomechanics-based method and rigid registration. The Hausdorff distance–based evaluation process, which measures the difference between images, was used to quantify the performance of both registration methods. A statistical test for difference in proportions was conducted to evaluate the null hypothesis that the proportion of patients for whom improved neuronavigation can be achieved is the same for rigid and biomechanics-based registration. The null hypothesis was confidently rejected (p < 10−4). Even the modified hypothesis that fewer than 25% of patients would benefit from the use of biomechanics-based registration was rejected at a significance level of 5% (p = 0.02). The biomechanics-based method proved particularly effective in cases demonstrating large craniotomy-induced brain deformations. The outcome of this analysis suggests that nonlinear biomechanics-based methods are beneficial to a large proportion of patients and can be considered for use in the operating theater as a possible means of improving neuronavigation and surgical outcomes.

Abbreviations used in this paper:H0 = null hypothesis; HD = Hausdorff distance.

Complete surgical removal of a cerebral tumor is highly desirable, yet often difficult to achieve. One of the factors that complicate near-complete tumor resection is the craniotomy-induced brain shift that occurs during neurosurgery. It has been established that during craniotomy the brain surface can deform by more than 20 mm23 and by more than 10 mm in approximately 30% of patients.10 Figure 1 shows the brain deformation that occurs upon opening the skull. As brain shift distorts the anatomy, it diminishes the utility of preoperatively acquired imaging data.16,21 Therefore, the efficiency of intraoperative neuronavigation can be significantly improved by fusing high-resolution preoperative imaging data with the intraoperative configuration of the patient's brain. This can be achieved by updating the preoperative image to the current intraoperative configuration through a technique known as registration. Although current commercial image-guided navigation systems use rigid registration for this purpose, we are starting to see a shift toward nonrigid registration, which is necessary to capture the nonrigid movement of brain tissue caused by brain shift.1,4,6,17,18,25

Fig. 1.
Fig. 1.

An example of craniotomy-induced brain shift. In the preoperative MR image (left), the perimeter of the brain is identified with the spline (green line). Registration of this spline to the intraoperative image, obtained using MRI (right), clearly shows the brain shift induced by the surgical procedure. Therefore, surgical planning using preoperative images may result in ineffective procedures.

Rigid registration is generally performed in the operating room using neuronavigation systems such as the ExacTrac system available in Brainlab12 (www.brainlab.com) or the StealthStation neuronavigation system from Medtronic9 (www.medtronic.com). In the Brainlab system, the patient is registered to the preoperative image by matching the positions of fiducial markers on the patient and the image. In the Stealth system, registration is performed by matching landmarks identified by a tracked pointer. Although nonrigid registration is believed to provide better data, most current methods4,6,17,18,25 are inefficient; that is, they cannot provide results in real time and do not account for the large brain deformations often observed during surgery.14,20 Our biomechanics-based nonrigid registration method overcomes these limitations.14,20

The potential of nonrigid registration has been demonstrated and is well documented.11,13,14,19,31,33 In brief, we use a biomechanics-based computational model to predict deformation of the brain and use this predicted deformation to warp (deform) the preoperative MRI data to the current intraoperative position of the brain. We have developed, refined, and rigorously tested our methods13,14,19,31 to now apply them in real time by using a desktop computer during neurosurgery.14,15 We are confident that our methods allow the accurate estimation of brain shift and thus lead to a safer and more efficient surgical approach.

A key advantage of our approach is that it does not require intraoperative MR images and it complements existing neuronavigation equipment. The measurement of the position of a number of points on the exposed surface of the brain is sufficient and can be conducted in the operating room using existing neuronavigation technology. The tracking pointer tool available within Medtronic's StealthStation neuronavigation system enables the surgeon to select (by touching) a number of points on the brain surface (technical details regarding the software application are available online at http://www.na-mic.org/Wiki/index.php/Stealthlink_Protocol) and determine their positions in the images by using software tools implemented in 3D Slicer.5,26–28 These data then serve as inputs to our biomechanics-based approach.

In this paper, the advantages of our biomechanics-based approach relative to traditional rigid registration are demonstrated for 33 neurosurgery cases. To establish the efficacy of our method, we performed a test for difference in proportions3 to evaluate the null hypothesis (H0) that the proportion of patients for whom improved neuronavigation can be achieved is the same for rigid and biomechanics-based registration.

Methods

Medical Image Data

Preoperative and intraoperative medical image data sets for 33 patients with cerebral gliomas were randomly selected from a retrospective database of 859 intracranial tumor cases available at the Boston Children's Hospital.29 The types, locations, and sizes of these tumors are listed in Table 1. Imaging was performed using a 0.5-T open MR system in the neurosurgical suite. The resolution of the images was 0.85 × 0.85 × 2.5 mm3. Consent was obtained for the use of the anonymized retrospective image database, in accordance with the institutional review board of the Boston Children's Hospital.

TABLE 1:

Sizes, types, and locations of 33 cerebral low-grade gliomas

Case No.Tumor Size (mm)*Tumor TypeTumor Location
123diffuse astrocytomaposterior
226diffuse astrocytomalateral
311diffuse astrocytomalateral
431diffuse astrocytomalateral
549focal astrocytomalateral
648diffuse astrocytomalateral
716focal astrocytomalateral
835focal astrocytomaposterior-lateral
922diffuse astrocytomaanterior-lateral
1031focal astrocytomaanterior-lateral
1125focal astrocytomalateral
1227oligodendrogliomaanterior
1328diffuse astrocytomaanterior
1418focal astrocytomaanterior
1511diffuse astrocytomalateral
1620diffuse astrocytomaposterior
1722diffuse astrocytomaposterior
1823diffuse astrocytomaanterior
1917diffuse astrocytomaposterior-lateral
2013focal astrocytomaanterior-lateral
2119diffuse astrocytomalateral
2215diffuse astrocytomalateral
2318diffuse astrocytomalateral
2413diffuse astrocytomaposterior-lateral
259diffuse astrocytomaposterior-lateral
2614diffuse astrocytomaanterior
2718diffuse astrocytomaanterior
2826diffuse astrocytomaposterior
299focal astrocytomalateral
3018diffuse astrocytomalateral
3118diffuse astrocytomaanterior-lateral
3247diffuse astrocytomaposterior
3320diffuse astrocytomaposterior

Tumor size, which was rounded to the nearest millimeter, was defined as the length of the longest diagonal of the cuboid that enveloped the tumor.

The location of the tumor was defined in the axial plane.

Biomechanical Modeling-Based Registration

We begin by computing the deformation fields of the brain using a computational model and subsequently utilize this information to warp (deform) the preoperative images onto their intraoperative configuration (Fig. 2). For a detailed description of the numerical modeling approach to predict deformation, the reader is referred to our earlier work.20,32 The workflow in clinical situations can be divided into preoperative and intraoperative steps. The preoperative steps are as follows: 1) The preoperative image is segmented (divided) into the desired structures, such as parenchyma, ventricles, and tumor (Fig. 3); and 2) based on this segmentation, which can be performed days before surgery, a patient-specific brain computational model is generated. The intraoperative steps are as follows: 1) Using only sparse intraoperative data—for example, positional information of the exposed brain, acquired using the pointer tool of the StealthStation (Medtronic Inc.)—we apply loading conditions to our computational model; and 2) once the model is completely defined, we compute the deformation and then warp the preoperative image so that it now shows the intraoperative configuration of the brain (Fig. 2). This final step is performed in real time during neurosurgery.

Fig. 2.
Fig. 2.

Registration process according to our biomechanics-based methods. The flowchart (beginning with the preoperative image) illustrates the various steps used in registering the preoperative images onto their intraoperative configuration. M = the moving image (preoperative image); T = the transform that registers the preoperative image onto the intraoperative configuration of the brain; T(M) = the transformed moving image (warped preoperative image).

Fig. 3.
Fig. 3.

Case 7. An example of a segmented geometry (left) from a preoperative MR image and the resulting patient-specific brain mesh (right). An essential engineering-specific detail of the brain mesh in this case is that it consists of 99,974 elements and 32,023 nodes.

Current Technique Available to Patients: Rigid Registration

Rigid registration is the standard registration method currently available to patients in MRI-equipped operating theaters. In this approach, the preoperative image is aligned with the intraoperative image such that the rigid transform minimizes the mutual information between both images. This technique was described in detail by Wells et al.30 and has been widely adopted by leading intraoperative navigation companies.

Evaluation of Registration Accuracy Using Hausdorff Distance

Intraoperative MR images acquired using the 0.5-T open system were used as the actual configuration (ground truth) to which we compared the results of both the rigid and the biomechanics-based registrations. We used the Hausdorff distance (HD) metric to calculate the spatial differences (in mm) between 2 overlaid images. The HD was measured by comparing automatically detected feature edges, known as Canny edges.2 We subsequently evaluated the HD results for both rigid and biomechanics-based registrations by using intraoperative image data as ground truth, as described in our earlier work.7 The Canny edges used in the evaluation process are shown in Fig. 4.

Fig. 4.
Fig. 4.

Evaluation of registration accuracy using Canny edges. Canny edges of a preoperative image warped using our biomechanics-based approach (cropped to the region of interest) and the corresponding intraoperative image. A: Biomechanics-based warped preoperative image. B: Canny edges of biomechanics-based warped preoperative image. C: Corresponding intraoperative image. D: Canny edges of the intraoperative image. By comparing these Canny edges using the HD method, it was possible to quantify the registration error and the accuracy of each technique.

Almost all Canny edges in the warped preoperative image have a corresponding edge in the intraoperative image; that is, they apparently represent the same anatomical feature in both MR images (Fig. 4). However, outliers (unusually large HD values) arise when some Canny edges that do not represent the same anatomical feature (hence, farther apart) are compared. These outliers, confirmed by subsequent visual inspection of the images, were excluded from the final analysis. By reporting the complete HD results over the full percentile range (0–100), instead of a single quantity at a certain percentile, it is easier to determine the entire range of alignment errors and to identify potential outliers. To report HD values over the full percentile range, we used the nth percentile HD metric that is defined as the HD value that is greater than n percent of the total number of HD values belonging to edges of either image.

Statistical Evaluation Using Test for Difference in Proportions

To statistically ascertain whether our biomechanics-based approach demonstrates improvements over rigid registration, the test for difference in proportions was conducted.3 Our H0 was defined as follows: There will be no statistically significant increase in the proportion of neurosurgery patients for whom accurate data for intraoperative navigation is obtained using our biomechanics-based method, as compared with data obtained using rigid registration (the ability to confidently reject this hypothesis will demonstrate the superiority of our biomechanics-based approach).

The test statistic is a numerical summary of a data set that reduces the data to 1 value, which can be used to perform a hypothesis test. For the test for difference in proportions, the test statistic follows a normal distribution and depends on the proportion of “yes” responses (false [H0]) for both registration methods. In the current study, “yes” is the response when the evaluation results show that our biomechanics-based method has at least as good accuracy as rigid registration (therefore rejects H0); otherwise the response is “no.” The p value, which is defined as the estimated probability of rejecting the H0 when that hypothesis is true,8,24 is used to decide the test for difference in proportions between the populations of each group.3,8,24 Results with p < 0.05 were considered statistically significant.

Results

Demonstrating the Inadequacy of Rigid Registration Using an Example Case

The overlaid Canny edges for our biomechanics-based method and rigid registration are shown in Fig. 5 in both the axial and coronal planes for Case 7. This case demonstrated large brain shift (10 mm). Here, the misalignments using our biomechanics-based method are less than those found using rigid registration. This observation is also supported by the results from the percentile HD analysis for Case 7, shown in Fig. 6. The misalignment (HD metric) values for rigid registration are higher than those for the biomechanics-based method for all percentiles between 0 and 100 in both planes. As the accuracy of Canny edge detection is limited by the resolution of the original medical image, an alignment error < 2 times the in-plane resolution of the intraoperative image is difficult to avoid29 (1.7 mm in this study). Therefore, edges with misalignment values < 1.7 mm were considered successfully registered. This choice is consistent with the accuracy of manual neurosurgery, which is reported to be not better than 1.5 mm.22,29 Figures 5 and 6 clearly demonstrate the insufficiency of rigid registration for cases with large deformations.

Fig. 5.
Fig. 5.

Case 7. Overlaid Canny edges for both registration techniques in 2 different planes for an example large deformation case. A: Biomechanics-based method in axial plane. B: Rigid registration in axial plane. C: Biomechanics-based method in coronal plane. D: Rigid registration in coronal plane. Green represents overlapping edges, blue identifies nonoverlapping edges of the warped preoperative image, and red indicates nonoverlapping edges of the intraoperative image.

Fig. 6.
Fig. 6.

Case 7. Percentile HD metric curves for axial (left) and coronal (right) planes for an example case of large deformation. The horizontal line in each plot represents the minimum expected registration error (1.7 mm).

Biomechanics-Based Method Versus Rigid Registration: Statistical Results

Small craniotomy-induced deformation, defined as deformation < 3.3 mm, was observed in 19 cases. For these small deformation cases, there was an insignificant difference between the percentile HD metric curves, implying that both registration techniques perform similarly. Figure 7 shows this comparable performance using the percentile HD results for a typical small deformation case (Case 3). However, in the remaining 14 cases in which brain shift exceeded 3.3 mm, our biomechanics-based method proved more accurate.

Fig. 7.
Fig. 7.

Case 3. Percentile HD metric curves for axial (left) and coronal (right) planes for an example case of small deformation. The horizontal line in each plot represents the minimum expected registration error (1.7 mm).

Test for Difference in Proportions

The number of “yes” responses for our biomechanics-based method and rigid registration were 33 and 19, respectively. The p values for difference in proportions of 0%, 20%, and 25% were 0.0000125, 0.00457, and 0.02, respectively. Thus, there is strong evidence that more than 25% of patients undergoing glioma resection could benefit from the application of our biomechanics-based methods.

Discussion

The results demonstrate that our biomechanics-based method, as compared with the commonly used rigid registration method, provides improved neuronavigation data for a larger proportion of patients. Our method proved particularly effective in cases in which the patient experienced a large craniotomy-induced brain shift (> 3.3 mm). The probability that fewer than 25% of patients would benefit from the intraoperative use of computational biomechanics-based brain shift compensation is only 2%; in other words, the probability that more than 25% of patients would benefit from our approach is 98%. On the basis of our findings, larger-scale efficacy testing of our methods, with a view to future clinical implementation, is warranted. Clinical application of this method is further facilitated by the distinct advantage of this new approach, that is, the redundancy of intraoperative MRI data. Only the displacements of a limited number of points on the exposed surface of the brain need to be measured using typical neuronavigation systems.

Experience at Brigham and Women's Hospital29 has demonstrated that intraoperative MRI is immensely useful in ensuring near-complete resection, particularly of low-grade tumors. However, this imaging usually comes at the expense of significantly longer operating times and is resource intensive.

Conclusions

The use of real-time comprehensive biomechanical computations in the operating theater could present a viable and economical alternative to an intraoperative MR image. Thus, the results presented in this report have the potential to significantly advance the way medical imaging, combined with biomechanical modeling, is used to guide the successful resection of brain tumors.

Acknowledgment

Medical image data related to 33 cases of neurosurgery were obtained from the retrospective database at the Boston Children's Hospital, affiliated with the Harvard Medical School.

Disclosure

Misters Garlapati and Mostayed are recipients of a Scholarship for International Research. Ms. Roy is the recipient of a University Postgraduate Award. Misters Garlapati, Mostayed, and Doyle and Ms. Roy gratefully acknowledge the financial support of The University of Western Australia. The financial support of the National Health and Medical Research Council (Grant No. APP1006031) is acknowledged by Drs. Miller, Warfield, Wittek, and Knuckey. Additionally, the support of the NIH (Grants Nos. R01 EB008015 and R01 LM010033) and Boston Children's Hospital Translational Research Program is acknowledged by Dr. Warfield. The financial support of the Neuroimage Analysis Center (NIH P41 EB015902), National Center for Image-Guided Therapy (NIH U41 RR019703), and the National Alliance for Medical Image Computing (NAMIC), funded by the NIH through the NIH Roadmap for Medical Research (Grant U54 EB005149) is gratefully acknowledged by Dr. Kikinis. Information on the National Centers for Biomedical Computing can be obtained from http://nihroadmap.nih.gov/bioinformatics. The authors report no conflict of interest concerning the materials or methods used in this study or the findings specified in this paper.

Author contributions to the study and manuscript preparation include the following. Conception and design: Miller. Acquisition of data: Warfield. Analysis and interpretation of data: Garlapati, Roy, Joldes, Wittek, Mostayed. Drafting the article: Garlapati, Roy. Critically revising the article: all authors. Reviewed submitted version of manuscript: all authors. Approved the final version of the manuscript on behalf of all authors: Miller. Statistical analysis: Garlapati, Roy, Joldes. Administrative/technical/material support: Miller, Wittek, Warfield, Kikinis, Knuckey. Study supervision: Miller.

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

References

  • 1

    Archip NClatz OWhalen SKacher DFedorov AKot A: Non-rigid alignment of pre-operative MRI, fMRI, and DT-MRI with intra-operative MRI for enhanced visualization and navigation in image-guided neurosurgery. Neuroimage 35:6096242007

  • 2

    Canny J: A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8:6796981986

  • 3

    Connor RJ: Sample size for testing differences in proportions for the paired-sample design. Biometrics 43:2072111987

  • 4

    Dumpuri PThompson RCDawant BMCao AMiga MI: An atlas-based method to compensate for brain shift: preliminary results. Med Image Anal 11:1281452007

  • 5

    Fedorov ABeichel RKalpathy-Cramer JFinet JFillion-Robin JCPujol S: 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 30:132313412012

  • 6

    Ferrant MNabavi AMacq BBlack PMJolesz FAKikinis R: Serial registration of intraoperative MR images of the brain. Med Image Anal 6:3373592002

  • 7

    Garlapati RRJoldes GRWittek ALam JWeisenfeld NHans A: Objective evaluation of accuracy of intra-operative neuroimage registration. Wittek AMiller KNielsen PMF: New YorkSpringer2013. 8799

  • 8

    Goodman SN: Toward evidence-based medical statistics. 1: The P value fallacy. Ann Intern Med 130:99510041999

  • 9

    Henderson JMHolloway KLGaede SERosenow JM: The application accuracy of a skull-mounted trajectory guide system for image-guided functional neurosurgery. Comput Aided Surg 9:1551602004

  • 10

    Hill DLMaurer CR JrMaciunas RJBarwise JAFitzpatrick JMWang MY: Measurement of intraoperative brain surface deformation under a craniotomy. Neurosurgery 43:5145281998

  • 11

    Hu JJin XLee JBZhang LChaudhary VGuthikonda M: Intraoperative brain shift prediction using a 3D inhomogeneous patient-specific finite element model. J Neurosurg 106:1641692007

  • 12

    Jin JYYin FFTenn SEMedin PMSolberg TD: Use of the BrainLAB ExacTrac X-Ray 6D system in image-guided radiotherapy. Med Dosim 33:1241342008

  • 13

    Joldes GRWittek AMiller K: Computation of intra-operative brain shift using dynamic relaxation. Comput Methods Appl Mech Eng 198:331333202009

  • 14

    Joldes GRWittek AMiller K: Real-time nonlinear finite element computations on GPU—application to neurosurgical simulation. Comput Methods Appl Mech Eng 199:330533142010

  • 15

    Joldes GRWittek AMiller K: Suite of finite element algorithms for accurate computation of soft tissue deformation for surgical simulation. Med Image Anal 13:9129192009

  • 16

    Miga MIPaulsen KDLemery JMEisner SDHartov AKennedy FE: Model-updated image guidance: initial clinical experiences with gravity-induced brain deformation. IEEE Trans Med Imaging 18:8668741999

  • 17

    Miga MIRoberts DWKennedy FEPlatenik LAHartov ALunn KE: Modeling of retraction and resection for intraoperative updating of images. Neurosurgery 49:75852001

  • 18

    Miga MISinha TKCash DMGalloway RLWeil RJ: Cortical surface registration for image-guided neurosurgery using laser-range scanning. IEEE Trans Med Imaging 22:9739852003

  • 19

    Miller KWittek AJoldes GHorton ADutta-Roy TBerger J: Modelling brain deformations for computer-integrated neurosurgery. Int J Numer Method Biomed Eng 26:1171382010

  • 20

    Miller KWittek AJoldes GRBiomechanical modeling of the brain for computer-assisted neurosurgery. Miller K: Biomechanics of the Brain New YorkSpringer2011. 111136

  • 21

    Nabavi ABlack PMGering DTWestin CFMehta VPergolizzi RS Jr: Serial intraoperative magnetic resonance imaging of brain shift. Neurosurgery 48:7877982001

  • 22

    Nakaji PSpetzler RF: Innovations in surgical approach: the marriage of technique, technology, and judgment. Clin Neurosurg 51:1771852004

  • 23

    Roberts DWHartov AKennedy FEMiga MIPaulsen KD: Intraoperative brain shift and deformation: a quantitative analysis of cortical displacement in 28 cases. Neurosurgery 43:7497601998

  • 24

    Shaffer JP: Multiple hypothesis testing. Annu Rev Psychol 46:5615841995

  • 25

    Škrinjar ONabavi ADuncan JA stereo-guided biomechanical model for volumetric deformation analysis. Staib L: IEEE Workshop on Mathematical Methods in Biomedical Image Analysis Washington, DCIEEE Computer Society2001. 95102

  • 26

    Tokuda JFischer GSPapademetris XYaniv ZIbanez LCheng P: OpenIGTLink: an open network protocol for image-guided therapy environment. Int J Med Robot 5:4234342009

  • 27

    Ungi TAbolmaesumi PJalal RWelch MAyukawa INagpal S: Spinal needle navigation by tracked ultrasound snapshots. IEEE Trans Biomed Eng 59:276627722012

  • 28

    Ungi TSargent DMoult ELasso APinter CMcGraw RC: Perk Tutor: an open-source training platform for ultrasound-guided needle insertions. IEEE Trans Biomed Eng 59:347534812012

  • 29

    Warfield SKHaker SJTalos IFKemper CAWeisenfeld NMewes AU: Capturing intraoperative deformations: research experience at Brigham and Women's Hospital. Med Image Anal 9:1451622005

  • 30

    Wells WM IIIViola PAtsumi HNakajima SKikinis R: Multi-modal volume registration by maximization of mutual information. Med Image Anal 1:35511996

  • 31

    Wittek AJoldes GCouton MWarfield SKMiller K: Patient-specific non-linear finite element modelling for predicting soft organ deformation in real-time: application to non-rigid neuroimage registration. Prog Biophys Mol Biol 103:2923032010

  • 32

    Wittek AJoldes GRMiller KAlgorithms for computational biomechanics of the brain. Miller K: Biomechanics of the Brain New YorkSpringer2011. 189220

  • 33

    Wittek AMiller KKikinis RWarfield SK: Patient-specific model of brain deformation: application to medical image registration. J Biomech 40:9199292007

Article Information

Address correspondence to: Karol Miller, D.Sc., School of Mechanical and Chemical Engineering, The University of Western Australia (M050), 35 Stirling Highway, Crawley, Perth, WA 6009, Australia. email: karol.miller@uwa.edu.au.

Please include this information when citing this paper: published online January 24, 2014; DOI: 10.3171/2013.12.JNS131165.

© AANS, except where prohibited by US copyright law."

Headings

Figures

  • View in gallery

    An example of craniotomy-induced brain shift. In the preoperative MR image (left), the perimeter of the brain is identified with the spline (green line). Registration of this spline to the intraoperative image, obtained using MRI (right), clearly shows the brain shift induced by the surgical procedure. Therefore, surgical planning using preoperative images may result in ineffective procedures.

  • View in gallery

    Registration process according to our biomechanics-based methods. The flowchart (beginning with the preoperative image) illustrates the various steps used in registering the preoperative images onto their intraoperative configuration. M = the moving image (preoperative image); T = the transform that registers the preoperative image onto the intraoperative configuration of the brain; T(M) = the transformed moving image (warped preoperative image).

  • View in gallery

    Case 7. An example of a segmented geometry (left) from a preoperative MR image and the resulting patient-specific brain mesh (right). An essential engineering-specific detail of the brain mesh in this case is that it consists of 99,974 elements and 32,023 nodes.

  • View in gallery

    Evaluation of registration accuracy using Canny edges. Canny edges of a preoperative image warped using our biomechanics-based approach (cropped to the region of interest) and the corresponding intraoperative image. A: Biomechanics-based warped preoperative image. B: Canny edges of biomechanics-based warped preoperative image. C: Corresponding intraoperative image. D: Canny edges of the intraoperative image. By comparing these Canny edges using the HD method, it was possible to quantify the registration error and the accuracy of each technique.

  • View in gallery

    Case 7. Overlaid Canny edges for both registration techniques in 2 different planes for an example large deformation case. A: Biomechanics-based method in axial plane. B: Rigid registration in axial plane. C: Biomechanics-based method in coronal plane. D: Rigid registration in coronal plane. Green represents overlapping edges, blue identifies nonoverlapping edges of the warped preoperative image, and red indicates nonoverlapping edges of the intraoperative image.

  • View in gallery

    Case 7. Percentile HD metric curves for axial (left) and coronal (right) planes for an example case of large deformation. The horizontal line in each plot represents the minimum expected registration error (1.7 mm).

  • View in gallery

    Case 3. Percentile HD metric curves for axial (left) and coronal (right) planes for an example case of small deformation. The horizontal line in each plot represents the minimum expected registration error (1.7 mm).

References

1

Archip NClatz OWhalen SKacher DFedorov AKot A: Non-rigid alignment of pre-operative MRI, fMRI, and DT-MRI with intra-operative MRI for enhanced visualization and navigation in image-guided neurosurgery. Neuroimage 35:6096242007

2

Canny J: A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8:6796981986

3

Connor RJ: Sample size for testing differences in proportions for the paired-sample design. Biometrics 43:2072111987

4

Dumpuri PThompson RCDawant BMCao AMiga MI: An atlas-based method to compensate for brain shift: preliminary results. Med Image Anal 11:1281452007

5

Fedorov ABeichel RKalpathy-Cramer JFinet JFillion-Robin JCPujol S: 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 30:132313412012

6

Ferrant MNabavi AMacq BBlack PMJolesz FAKikinis R: Serial registration of intraoperative MR images of the brain. Med Image Anal 6:3373592002

7

Garlapati RRJoldes GRWittek ALam JWeisenfeld NHans A: Objective evaluation of accuracy of intra-operative neuroimage registration. Wittek AMiller KNielsen PMF: New YorkSpringer2013. 8799

8

Goodman SN: Toward evidence-based medical statistics. 1: The P value fallacy. Ann Intern Med 130:99510041999

9

Henderson JMHolloway KLGaede SERosenow JM: The application accuracy of a skull-mounted trajectory guide system for image-guided functional neurosurgery. Comput Aided Surg 9:1551602004

10

Hill DLMaurer CR JrMaciunas RJBarwise JAFitzpatrick JMWang MY: Measurement of intraoperative brain surface deformation under a craniotomy. Neurosurgery 43:5145281998

11

Hu JJin XLee JBZhang LChaudhary VGuthikonda M: Intraoperative brain shift prediction using a 3D inhomogeneous patient-specific finite element model. J Neurosurg 106:1641692007

12

Jin JYYin FFTenn SEMedin PMSolberg TD: Use of the BrainLAB ExacTrac X-Ray 6D system in image-guided radiotherapy. Med Dosim 33:1241342008

13

Joldes GRWittek AMiller K: Computation of intra-operative brain shift using dynamic relaxation. Comput Methods Appl Mech Eng 198:331333202009

14

Joldes GRWittek AMiller K: Real-time nonlinear finite element computations on GPU—application to neurosurgical simulation. Comput Methods Appl Mech Eng 199:330533142010

15

Joldes GRWittek AMiller K: Suite of finite element algorithms for accurate computation of soft tissue deformation for surgical simulation. Med Image Anal 13:9129192009

16

Miga MIPaulsen KDLemery JMEisner SDHartov AKennedy FE: Model-updated image guidance: initial clinical experiences with gravity-induced brain deformation. IEEE Trans Med Imaging 18:8668741999

17

Miga MIRoberts DWKennedy FEPlatenik LAHartov ALunn KE: Modeling of retraction and resection for intraoperative updating of images. Neurosurgery 49:75852001

18

Miga MISinha TKCash DMGalloway RLWeil RJ: Cortical surface registration for image-guided neurosurgery using laser-range scanning. IEEE Trans Med Imaging 22:9739852003

19

Miller KWittek AJoldes GHorton ADutta-Roy TBerger J: Modelling brain deformations for computer-integrated neurosurgery. Int J Numer Method Biomed Eng 26:1171382010

20

Miller KWittek AJoldes GRBiomechanical modeling of the brain for computer-assisted neurosurgery. Miller K: Biomechanics of the Brain New YorkSpringer2011. 111136

21

Nabavi ABlack PMGering DTWestin CFMehta VPergolizzi RS Jr: Serial intraoperative magnetic resonance imaging of brain shift. Neurosurgery 48:7877982001

22

Nakaji PSpetzler RF: Innovations in surgical approach: the marriage of technique, technology, and judgment. Clin Neurosurg 51:1771852004

23

Roberts DWHartov AKennedy FEMiga MIPaulsen KD: Intraoperative brain shift and deformation: a quantitative analysis of cortical displacement in 28 cases. Neurosurgery 43:7497601998

24

Shaffer JP: Multiple hypothesis testing. Annu Rev Psychol 46:5615841995

25

Škrinjar ONabavi ADuncan JA stereo-guided biomechanical model for volumetric deformation analysis. Staib L: IEEE Workshop on Mathematical Methods in Biomedical Image Analysis Washington, DCIEEE Computer Society2001. 95102

26

Tokuda JFischer GSPapademetris XYaniv ZIbanez LCheng P: OpenIGTLink: an open network protocol for image-guided therapy environment. Int J Med Robot 5:4234342009

27

Ungi TAbolmaesumi PJalal RWelch MAyukawa INagpal S: Spinal needle navigation by tracked ultrasound snapshots. IEEE Trans Biomed Eng 59:276627722012

28

Ungi TSargent DMoult ELasso APinter CMcGraw RC: Perk Tutor: an open-source training platform for ultrasound-guided needle insertions. IEEE Trans Biomed Eng 59:347534812012

29

Warfield SKHaker SJTalos IFKemper CAWeisenfeld NMewes AU: Capturing intraoperative deformations: research experience at Brigham and Women's Hospital. Med Image Anal 9:1451622005

30

Wells WM IIIViola PAtsumi HNakajima SKikinis R: Multi-modal volume registration by maximization of mutual information. Med Image Anal 1:35511996

31

Wittek AJoldes GCouton MWarfield SKMiller K: Patient-specific non-linear finite element modelling for predicting soft organ deformation in real-time: application to non-rigid neuroimage registration. Prog Biophys Mol Biol 103:2923032010

32

Wittek AJoldes GRMiller KAlgorithms for computational biomechanics of the brain. Miller K: Biomechanics of the Brain New YorkSpringer2011. 189220

33

Wittek AMiller KKikinis RWarfield SK: Patient-specific model of brain deformation: application to medical image registration. J Biomech 40:9199292007

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