Intraoperative image updating for brain shift following dural opening

Xiaoyao Fan Thayer School of Engineering, and

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David W. Roberts Geisel School of Medicine, Dartmouth College, Hanover;
Norris Cotton Cancer Center, and
Section of Neurosurgery, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire; and

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Timothy J. Schaewe Medtronic PLC, Surgical Technologies, Louisville, Colorado

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Songbai Ji Thayer School of Engineering, and
Section of Neurosurgery, Dartmouth-Hitchcock Medical Center, Lebanon, New Hampshire; and

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Leslie H. Holton Medtronic PLC, Surgical Technologies, Louisville, Colorado

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David A. Simon Medtronic PLC, Surgical Technologies, Louisville, Colorado

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Keith D. Paulsen Thayer School of Engineering, and
Geisel School of Medicine, Dartmouth College, Hanover;
Norris Cotton Cancer Center, and

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OBJECTIVE

Preoperative magnetic resonance images (pMR) are typically coregistered to provide intraoperative navigation, the accuracy of which can be significantly compromised by brain deformation. In this study, the authors generated updated MR images (uMR) in the operating room (OR) to compensate for brain shift due to dural opening, and evaluated the accuracy and computational efficiency of the process.

METHODS

In 20 open cranial neurosurgical cases, a pair of intraoperative stereovision (iSV) images was acquired after dural opening to reconstruct a 3D profile of the exposed cortical surface. The iSV surface was registered with pMR to detect cortical displacements that were assimilated by a biomechanical model to estimate whole-brain nonrigid deformation and produce uMR in the OR. The uMR views were displayed on a commercial navigation system and compared side by side with the corresponding coregistered pMR. A tracked stylus was used to acquire coordinate locations of features on the cortical surface that served as independent positions for calculating target registration errors (TREs) for the coregistered uMR and pMR image volumes.

RESULTS

The uMR views were visually more accurate and well aligned with the iSV surface in terms of both geometry and texture compared with pMR where misalignment was evident. The average misfit between model estimates and measured displacements was 1.80 ± 0.35 mm, compared with the average initial misfit of 7.10 ± 2.78 mm between iSV and pMR, and the average TRE was 1.60 ± 0.43 mm across the 20 patients in the uMR image volume, compared with 7.31 ± 2.82 mm on average in the pMR cases. The iSV also proved to be accurate with an average error of 1.20 ± 0.37 mm. The overall computational time required to generate the uMR views was 7–8 minutes.

CONCLUSIONS

This study compensated for brain deformation caused by intraoperative dural opening using computational model–based assimilation of iSV cortical surface displacements. The uMR proved to be more accurate in terms of model-data misfit and TRE in the 20 patient cases evaluated relative to pMR. The computational time was acceptable (7–8 minutes) and the process caused minimal interruption of surgical workflow.

ABBREVIATIONS

FRE = fiducial registration error; GPU = graphics processing unit; iMR = intraoperative magnetic resonance imaging scanner; iSV = intraoperative stereovision; iUS = intraoperative ultrasound; OR = operating room; pMR = preoperative magnetic resonance images; RMS = root mean square; TRE = target registration error; uMR = updated magnetic resonance images.
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  • 1

    Ammirati M, Gross JD, Ammirati G, Dugan S: Comparison of registration accuracy of skin- and bone-implanted fiducials for frameless stereotaxis of the brain: a prospective study. Skull Base 12:125130, 2002

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2

    Buckner JC: Factors influencing survival in high-grade gliomas. Semin Oncol 30:6 Suppl 19 1014, 2003

  • 3

    Carter TJ, Sermesant M, Cash DM, Barratt DC, Tanner C, Hawkes DJ: Application of soft tissue modelling to image-guided surgery. Med Eng Phys 27:893909, 2005

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 4

    Clatz O, Delingette H, Talos IF, Golby AJ, Kikinis R, Jolesz FA, et al.: Robust nonrigid registration to capture brain shift from intraoperative MRI. IEEE Trans Med Imaging 24:14171427, 2005

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5

    Ding S, Miga MI, Thompson RC, Dumpuri P, Cao A, Dawant BM: Estimation of intraoperative brain shift using a tracked laser range scanner. Conf Proc IEEE Eng Med Biol Soc 2007:848851, 2007

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6

    Fan X, Ji S, Fontaine K, Hartov A, Roberts D, Paulsen K: Simulation of brain tumor resection in image-guided neurosurgery. Proc. SPIE 7964:79640U, 2011

  • 7

    Fan X, Ji S, Hartov A, Roberts DW, Paulsen KD: Stereovision to MR image registration for cortical surface displacement mapping to enhance image-guided neurosurgery. Med Phys 41:102302, 2014

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8

    Ferrant M, Nabavi A, Macq B, Jolesz FA, Kikinis R, Warfield SK: Registration of 3-D intraoperative MR images of the brain using a finite-element biomechanical model. IEEE Trans Med Imaging 20:13841397, 2001

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9

    Garlapati RR, Roy A, Joldes GR, Wittek A, Mostayed A, Doyle B, et al.: More accurate neuronavigation data provided by biomechanical modeling instead of rigid registration. J Neurosurg 120:14771483, 2014

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10

    Helm PA, Eckel TS: Accuracy of registration methods in frameless stereotaxis. Comput Aided Surg 3:5156, 1998

  • 11

    Ji S, Fan X, Hartov A, Roberts D, Paulsen K, Estimation of intraoperative brain deformation. Payan Y: Soft Tissue Biomechanical Modeling for Computer Assisted Surgery Berlin, Springer, 2012. 11:97133

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 12

    Ji S, Fan X, Roberts DW, Hartov A, Paulsen KD: Cortical surface shift estimation using stereovision and optical flow motion tracking via projection image registration. Med Image Anal 18:11691183, 2014

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 13

    Ji S, Fan X, Roberts DW, Hartov A, Paulsen KD, Flow-based correspondence matching in stereovision. Wu G, Zhang D, Shen D, et al.: Machine Learning in Medical Imaging: 4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Nagoya, Japan, September 22, 2013. Proceedings Cham, Switzerland, Springer, 2013. 106113

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 14

    Ji S, Fan X, Roberts DW, Paulsen KD: Efficient stereo image geometrical reconstruction at arbitrary camera settings from a single calibration. Med Image Comput Comput Assist Interv 17:440447, 2014

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15

    Ji S, Roberts DW, Hartov A, Paulsen KD: Brain-skull contact boundary conditions in an inverse computational deformation model. Med Image Anal 13:659672, 2009

  • 16

    Joldes GR, Wittek A, Couton M, Warfield SK, Miller K: Real-time prediction of brain shift using nonlinear finite element algorithms. Med Image Comput Comput Assist Interv 12:300307, 2009

    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17

    Lacroix M, Abi-Said D, Fourney DR, Gokaslan ZL, Shi W, DeMonte F, et al.: A multivariate analysis of 416 patients with glioblastoma multiforme: prognosis, extent of resection, and survival. J Neurosurg 95:190198, 2001

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 18

    Lunn KE, Paulsen KD, Lynch DR, Roberts DW, Kennedy FE, Hartov A: Assimilating intraoperative data with brain shift modeling using the adjoint equations. Med Image Anal 9:281293, 2005

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19

    Nimsky C, Ganslandt O, Hastreiter P, Fahlbusch R: Intraoperative compensation for brain shift. Surg Neurol 56:357365, 2001

  • 20

    Paulsen KD, Miga MI, Kennedy FE, Hoopes PJ, Hartov A, Roberts DW: A computational model for tracking subsurface tissue deformation during stereotactic neurosurgery. IEEE Trans Biomed Eng 46:213225, 1999

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21

    Roberts DW, Miga MI, Hartov A, Eisner S, Lemery JM, Kennedy FE, et al.: Intraoperatively updated neuroimaging using brain modeling and sparse data. Neurosurgery 45:11991207, 1999

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 22

    Stummer W, Reulen HJ, Meinel T, Pichlmeier U, Schumacher W, Tonn JC, et al.: Extent of resection and survival in glioblastoma multiforme: identification of and adjustment for bias. Neurosurgery 62:564576, 2008

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23

    Sun H, Lunn KE, Farid H, Wu Z, Roberts DW, Hartov A, et al.: Stereopsis-guided brain shift compensation. IEEE Trans Med Imaging 24:10391052, 2005

  • 24

    Sun K, Pheiffer TS, Simpson AL, Weis JA, Thompson RC, Miga MI: Near real-time computer assisted surgery for brain shift correction using biomechanical models. IEEE J Transl Eng Health Med 2:2, 2014

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 25

    Tsai RY: A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE J Robot Autom 3:323344, 1987

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26

    Valdés PA, Fan X, Ji S, Harris BT, Paulsen KD, Roberts DW: Estimation of brain deformation for volumetric image updating in protoporphyrin IX fluorescence-guided resection. Stereotact Funct Neurosurg 88:110, 2010

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 27

    Wu Z, Paulsen KD, Sullivan JM Jr: Adaptive model initialization and deformation for automatic segmentation of T1-weighted brain MRI data. IEEE Trans Biomed Eng 52:11281131, 2005

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 28

    Zhuang DX, Liu YX, Wu JS, Yao CJ, Mao Y, Zhang CX, et al.: A sparse intraoperative data-driven biomechanical model to compensate for brain shift during neuronavigation. AJNR Am J Neuroradiol 32:395402, 2011

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation

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