Neuro Surgery

References

  • V. Arulesana, T. Kesavadasa, and K. R. Hoffmanna, “Computer Assisted Neurosurgery,” International Journal of Computer Assisted Radiology and Surgery, vol. 2, pp. 218-225, 2007.
    [Bibtex]
    @ARTICLE{Arulesana2007,
      author = {Arulesana, V. and Kesavadasa, T. and Hoffmanna, K.R.},
      title = {Computer Assisted Neurosurgery},
      journal = {International Journal of Computer Assisted Radiology and Surgery},
      year = {2007},
      volume = {2},
      pages = {218 - 225},
      file = {Arulesana2007.pdf:Arulesana2007.pdf:PDF},
      issn = {1861-6410},
      keywords = {TEC, NES},
      owner = {thomaskroes},
      publisher = {Springer},
      timestamp = {2011.01.11}
    }
  • A. C. F. Colchester, J. Zhao, K. S. Holton-Tainter, C. J. Henri, N. Maitland, P. T. E. Roberts, C. G. Harris, and R. J. Evans, “Development and preliminary evaluation of VISLAN, a surgical planning and guidance system using intra-operative video imaging,” Medical Image Analysis, vol. 1, iss. 1, pp. 73-90, 1996.
    [Bibtex]
    @ARTICLE{Colchester1996,
      author = {Alan C.F. Colchester and Jason Zhao and Kerrie S. Holton-Tainter
      and Christopher J. Henri and Neil Maitland and Patricia T.E. Roberts
      and Christopher G. Harris and Richard J. Evans},
      title = {Development and preliminary evaluation of VISLAN, a surgical planning
      and guidance system using intra-operative video imaging},
      journal = {Medical Image Analysis},
      year = {1996},
      volume = {1},
      pages = {73 - 90},
      number = {1},
      abstract = {VISLAN is an integrated neurosurgical planning and guidance system.
      New segmentation and rendering techniques have been incorporated.
      A stereo video system is used intra-operatively and fulfils four
      roles. First, the video display is overlaid with graphical outlines
      showing the position of the planned craniotomy or the target (enhanced
      reality displays). Second, a skin surface patch is reconstructed
      from the stereo video images using patterned light (mean errors of
      surface point location are <0.15 mm). Third, a freely mobile, hand-held
      localizer is tracked in real time (position errors are <0.5 mm and
      with improved calibration <0.2 mm), with its position superimposed
      on the pre-operative patient representation to assist surgical guidance.
      Fourth, markers fixed to the skull bone next to the cranial opening
      are used to detect intra-operative movement and to update registration.
      Initial results from phantom experiments show an overall system accuracy
      of better than 0.9 mm for intra-operative localization of features
      defined in pre-operative images. The prototype system has been tested
      during six neurosurgical operations with very good results.},
      file = {:Colchester1996.pdf:PDF},
      issn = {1361-8415},
      keywords = {enhanced reality, APP, PLA, GUI, NES, STV, SUR},
      owner = {thomaskroes},
      timestamp = {2010.11.02}
    }
  • J. Galloway R.L., R. J. Maciunas, and I. Edwards C.A., “Interactive image-guided neurosurgery,” Biomedical Engineering, IEEE Transactions on, vol. 39, iss. 12, pp. 1226-1231, 1992.
    [Bibtex]
    @ARTICLE{Galloway1992,
      author = {Galloway, R.L., Jr. and Maciunas, R.J. and Edwards, C.A., II},
      title = {Interactive image-guided neurosurgery},
      journal = {Biomedical Engineering, IEEE Transactions on},
      year = {1992},
      volume = {39},
      pages = {1226 - 1231},
      number = {12},
      month = {December},
      abstract = {Interactive image-guided (IIG) surgery involves the synchronal display
      of the tip of a surgical device on preoperative scans. This display
      allows the surgeon to locate the present surgical position relative
      to the final site of surgical interest. A technique for IIG surgery
      based on a 6-degrees-of-freedom articulated arm is presented. Design
      accuracy for the arm is less than 0.1 mm, and the present implementation
      has a submillimetric accuracy. The display can show the surgical
      position on any tomographic image set with simultaneous display on
      up to three image sets. Laboratory results and clinical applications
      are discussed.},
      file = {Galloway1992.pdf:Galloway1992.pdf:PDF},
      issn = {0018-9294},
      keywords = {6-degrees-of-freedom articulated arm;design accuracy;interactive image-guided
      neurosurgery;preoperative scans;submillimetric accuracy;synchronal
      display;tomographic image set;brain;surgery;Brain;Equipment Design;Humans;Models,
      Structural;Neurosurgery;Radiography, Interventional;Stereotaxic Techniques;Therapy,
      Computer-Assisted;Tomography, X-Ray Computed;},
      owner = {thomaskroes},
      timestamp = {2011.01.06}
    }
  • I. M. Germano, “The NeuroStation System for image-guided, frameless stereotaxy,” Neurosurgery, vol. 37, iss. 2, p. 348, 1995.
    [Bibtex]
    @ARTICLE{Germano1995,
      author = {Germano, I.M.},
      title = {The NeuroStation System for image-guided, frameless stereotaxy},
      journal = {Neurosurgery},
      year = {1995},
      volume = {37},
      pages = {348},
      number = {2},
      issn = {0148-396X},
      keywords = {APP, NES},
      owner = {Thomas},
      timestamp = {2011.02.03}
    }
  • W. L. Grimson, G. J. Ettinger, S. J. White, T. Lozano-Perez, W. M. Wells, and R. Kikinis, “An automatic registration method for frameless stereotaxy, image guided surgery, and enhanced reality visualization.,” IEEE transactions on medical imaging, vol. 15, iss. 2, pp. 129-40, 1996.
    [Bibtex]
    @ARTICLE{Grimson1996,
      author = {Grimson, W L and Ettinger, G J and White, S J and Lozano-Perez, T
      and Wells, W M and Kikinis, R},
      title = {An automatic registration method for frameless stereotaxy, image
      guided surgery, and enhanced reality visualization.},
      journal = {IEEE transactions on medical imaging},
      year = {1996},
      volume = {15},
      pages = {129-40},
      number = {2},
      month = {January},
      abstract = {There is a need for frameless guidance systems to help surgeons plan
      the exact location for incisions, to define the margins of tumors,
      and to precisely identify locations of neighboring critical structures.
      The authors have developed an automatic technique for registering
      clinical data, such as segmented magnetic resonance imaging (MRI)
      or computed tomography (CT) reconstructions, with any view of the
      patient on the operating table. The authors demonstrate on the specific
      example of neurosurgery. The method enables a visual mix of live
      video of the patient and the segmented three-dimensional (3-D) MRI
      or CT model. This supports enhanced reality techniques for planning
      and guiding neurosurgical procedures and allows us to interactively
      view extracranial or intracranial structures nonintrusively. Extensions
      of the method include image guided biopsies, focused therapeutic
      procedures, and clinical studies involving change detection over
      time sequences of images.},
      file = {Grimson1996.pdf:Grimson1996.pdf:PDF},
      issn = {0278-0062},
      owner = {thomaskroes},
      pmid = {18215896},
      timestamp = {2010.10.22}
    }
  • P. Grunert, K. Darabi, J. Espinosa, and R. Filippi, “Computer-aided navigation in neurosurgery,” Neurosurgical review, vol. 26, iss. 2, pp. 73-99, 2003.
    [Bibtex]
    @ARTICLE{Grunert2003,
      author = {Grunert, P. and Darabi, K. and Espinosa, J. and Filippi, R.},
      title = {Computer-aided navigation in neurosurgery},
      journal = {Neurosurgical review},
      year = {2003},
      volume = {26},
      pages = {73 - 99},
      number = {2},
      abstract = {The article comprises three main parts: a historical review on navigation,
      the mathematical basics for calculation and the clinical applications
      of navigation devices. Main historical steps are described from the
      first idea till the realisation of the frame-based and frameless
      
      navigation devices including robots. In particular the idea of robots
      can be traced back to the Iliad of Homer, the first testimony of
      European literature over 2500 years ago. In the second part the mathematical
      calculation of the mapping between the navigation and the image space
      is demonstrated, including different registration modalities and
      error estimations. The error of the navigation has to be divided
      into the technical error of the device calculating its own position
      in space, the registration error due to inaccuracies in the calculation
      of the transformation matrix between the navigation and the image
      space, and the application error caused additionally by anatomical
      shift of the brain structures during operation. In the third part
      the main clinical fields of application in modern neurosurgery are
      demonstrated, such as localisation of small intracranial lesions,
      skull-base surgery, intracerebral biopsies, intracranial endoscopy,
      functional neurosurgery and spinal navigation. At the end of the
      article some possible objections to navigation-aided surgery are
      discussed.},
      file = {Grunert2003.pdf:Grunert2003.pdf:PDF},
      issn = {0344-5607},
      keywords = {NES, REV},
      owner = {thomaskroes},
      publisher = {Springer},
      timestamp = {2010.11.19}
    }
  • T. Guo, K. W. FINNIS, A. G. PARRENT, and T. M. PETERS, “Visualization and navigation system development and application for stereotactic deep-brain neurosurgeries,” Computer Aided Surgery, vol. 11, iss. 5, pp. 231-239, 2006.
    [Bibtex]
    @ARTICLE{Guo2006,
      author = {Guo, T. and FINNIS, K.W. and PARRENT, A.G. and PETERS, T.M.},
      title = {Visualization and navigation system development and application for
      stereotactic deep-brain neurosurgeries},
      journal = {Computer Aided Surgery},
      year = {2006},
      volume = {11},
      pages = {231 - 239},
      number = {5},
      file = {Guo2006.pdf:Guo2006.pdf:PDF},
      keywords = {APP, GUI, PLA, SUR, SLR},
      owner = {thomaskroes},
      timestamp = {2011.01.06}
    }
  • W. a Hall and C. L. Truwit, “Intraoperative MR-guided neurosurgery.,” Journal of magnetic resonance imaging : JMRI, vol. 27, iss. 2, pp. 368-75, 2008.
    [Bibtex]
    @ARTICLE{Hall2008,
      author = {Hall, Walter a and Truwit, Charles L},
      title = {Intraoperative MR-guided neurosurgery.},
      journal = {Journal of magnetic resonance imaging : JMRI},
      year = {2008},
      volume = {27},
      pages = {368-75},
      number = {2},
      month = {February},
      abstract = {For more than a decade neurosurgeons have become increasingly dependent
      on image guidance to perform safe, efficient, and cost-effective
      surgery. Neuronavigation is frame-based or frameless and requires
      obtaining computed tomography or magnetic resonance imaging (MRI)
      scans several days or immediately before surgery. Unfortunately,
      these systems do not allow the neurosurgeon to adjust for the brain
      shift that occurs once the cranium is open. This technical inability
      has led to the development of intraoperative MRI (ioMRI) systems
      ranging from 0.12-3.0T in strength. The advantages of ioMRI are the
      excellent soft tissue discrimination and the ability to view the
      operative site in three dimensions. Enhanced visualization of the
      intracranial lesion enables the neurosurgeon to choose a safe surgical
      trajectory that avoids critical structures, to maximize the extent
      of the tumor resection, and to exclude an intraoperative hemorrhage.
      All ioMRI systems provide basic T1- and T2-weighted imaging capabilities
      but high-field (1.5T) systems can also perform MR spectroscopy (MRS),
      MR venography (MRV), MR angiography (MRA), brain activation studies,
      chemical shift imaging, and diffusion-weighted imaging. Identifying
      vascular structures by MRA or MRV may prevent injury during surgery.
      Demonstrating elevated phosphocholine within a tumor may improve
      the diagnostic yield of brain biopsy. Mapping out neurologic function
      may influence the surgical approach to a tumor. The optimal strength
      for MR-guided neurosurgery is currently under investigation.},
      file = {Hall2008.pdf:Hall2008.pdf:PDF},
      issn = {1053-1807},
      keywords = {Biopsy,Biopsy: instrumentation,Biopsy: methods,Brain,Brain Mapping,Brain
      Mapping: instrumentation,Brain Mapping: methods,Brain Neoplasms,Brain
      Neoplasms: surgery,Brain: pathology,Brain: surgery,Deep Brain Stimulation,Deep
      Brain Stimulation: methods,Humans,Imaging, Three-Dimensional,Imaging,
      Three-Dimensional: methods,Magnetic Resonance Imaging, Interventional,Magnetic
      Resonance Imaging, Interventional: econom,Magnetic Resonance Imaging,
      Interventional: instru,Magnetic Resonance Imaging, Interventional:
      method,Neurosurgical Procedures,Neurosurgical Procedures: adverse
      effects,Neurosurgical Procedures: methods},
      owner = {thomaskroes},
      pmid = {18183585},
      timestamp = {2010.10.22}
    }
  • W. A. Hall and C. L. Truwit, “Intraoperative MR imaging,” Magnetic resonance imaging clinics of North America, vol. 13, iss. 3, p. 533, 2005.
    [Bibtex]
    @ARTICLE{Hall2005,
      author = {Hall, W.A. and Truwit, C.L.},
      title = {Intraoperative MR imaging},
      journal = {Magnetic resonance imaging clinics of North America},
      year = {2005},
      volume = {13},
      pages = {533},
      number = {3},
      issn = {1064-9689},
      owner = {Thomas},
      timestamp = {2011.02.03}
    }
  • T. Hartkens, D. Hill, A. Castellano-Smith, D. Hawkes, C. Maurer Jr, A. Martin, W. Hall, H. Liu, and C. Truwit, “Measurement and analysis of brain deformation during neurosurgery,” Medical Imaging, IEEE Transactions on, vol. 22, iss. 1, pp. 82-92, 2003.
    [Bibtex]
    @ARTICLE{Hartkens2003,
      author = {Hartkens, T. and Hill, DLG and Castellano-Smith, AD and Hawkes, DJ
      and Maurer Jr, CR and Martin, AJ and Hall, WA and Liu, H. and Truwit,
      CL},
      title = {Measurement and analysis of brain deformation during neurosurgery},
      journal = {Medical Imaging, IEEE Transactions on},
      year = {2003},
      volume = {22},
      pages = {82 - 92},
      number = {1},
      file = {Hartkens2003.pdf:Hartkens2003.pdf:PDF},
      issn = {0278-0062},
      owner = {thomaskroes},
      publisher = {IEEE},
      timestamp = {2011.01.04}
    }
  • D. J. Hawkes, D. Barratt, J. M. Blackall, C. Chan, P. J. Edwards, K. Rhode, G. P. Penney, J. McClelland, and D. L. G. Hill, “Tissue deformation and shape models in image-guided interventions: a discussion paper.,” Medical image analysis, vol. 9, iss. 2, pp. 163-75, 2005.
    [Bibtex]
    @ARTICLE{Hawkes2005,
      author = {Hawkes, D J and Barratt, D and Blackall, J M and Chan, C and Edwards,
      P J and Rhode, K and Penney, G P and McClelland, J and Hill, D L
      G},
      title = {Tissue deformation and shape models in image-guided interventions:
      a discussion paper.},
      journal = {Medical image analysis},
      year = {2005},
      volume = {9},
      pages = {163-75},
      number = {2},
      month = {April},
      abstract = {This paper promotes the concept of active models in image-guided interventions.
      We outline the limitations of the rigid body assumption in image-guided
      interventions and describe how intraoperative imaging provides a
      rich source of information on spatial location of anatomical structures
      and therapy devices, allowing a preoperative plan to be updated during
      an intervention. Soft tissue deformation and variation from an atlas
      to a particular individual can both be determined using non-rigid
      registration. Established methods using free-form deformations have
      a very large number of degrees of freedom. Three examples of deformable
      models--motion models, biomechanical models and statistical shape
      models--are used to illustrate how prior information can be used
      to restrict the number of degrees of freedom of the registration
      algorithm and thus provide active models for image-guided interventions.
      We provide preliminary results from applications for each type of
      model.},
      file = {Hawkes2005.pdf:Hawkes2005.pdf:PDF},
      issn = {1361-8415},
      keywords = {Algorithms,Computer Simulation,Connective Tissue,Connective Tissue:
      pathology,Connective Tissue: physiopathology,Connective Tissue: surgery,Elasticity,Image
      Enhancement,Image Enhancement: methods,Image Interpretation, Computer-Assisted,Image
      Interpretation, Computer-Assisted: methods,Models, Biological,Movement,Subtraction
      Technique,Surgery, Computer-Assisted,Surgery, Computer-Assisted:
      methods, TEC},
      owner = {thomaskroes},
      pmid = {15721231},
      timestamp = {2010.10.22}
    }
  • C. J. Henri, A. C. F. Colchester, J. Zhao, D. J. Hawkes, D. L. G. Hill, and R. L. Evans, “Registration of 3-D Surface Data for Intra-Operative Guidance and Visualization in Frameless Stereotactic Neurosurgery,” Methods, 1995.
    [Bibtex]
    @ARTICLE{Henri1995,
      author = {Henri, Christopher J and Colchester, Alan C F and Zhao, Jason and
      Hawkes, David J and Hill, Derek L G and Evans, Richard L},
      title = {Registration of 3-D Surface Data for Intra-Operative Guidance and
      Visualization in Frameless Stereotactic Neurosurgery},
      journal = {Methods},
      year = {1995},
      abstract = {We describe a technique for registering 3-D multimodal im- age data,
      acquired preoperatively, with intraoperative surface data de- rived
      from stereo video during neurosurgery. Ultimately, our aim is to
      provide a system that supplants traditional frame-based stereotactic
      techniques while achieving comparable accuracy. For registration
      we em- ploy chamfer-matching in conjunction with a cost function
      that is robust to 'outliers'. To balance robustness and computation
      speed, we employ a quasi-stochastic search of parameter space that
      includes pursuing mul- tiple start points. This paper describes the
      registration problem as it pertains to our application. We discuss
      our approach to optimization and carry out a computational evaluation
      of the technique under various conditions.},
      file = {Henri1995.pdf:Henri1995.pdf:PDF},
      keywords = {TEC},
      owner = {thomaskroes},
      timestamp = {2010.10.22}
    }
  • H. Isekia, Y. Muragakia, R. Nakamuraa, T. Horib, and K. Takakuraa, “Computer Assisted Neurosurgery,” International Journal of Computer Assisted Radiology and Surgery, vol. 1, pp. 293-310, 2006.
    [Bibtex]
    @ARTICLE{Isekia2006,
      author = {Isekia, H. and Muragakia, Y. and Nakamuraa, R. and Horib, T. and
      Takakuraa, K.},
      title = {Computer Assisted Neurosurgery},
      journal = {International Journal of Computer Assisted Radiology and Surgery},
      year = {2006},
      volume = {1},
      pages = {293 - 310},
      file = {Isekia2006.pdf:Isekia2006.pdf:PDF},
      issn = {1861-6410},
      owner = {thomaskroes},
      publisher = {Springer},
      timestamp = {2011.01.11}
    }
  • G. R. Joldes, A. Wittek, and K. Miller, “Suite of finite element algorithms for accurate computation of soft tissue deformation for surgical simulation,” Medical Image Analysis, 2008.
    [Bibtex]
    @ARTICLE{Joldes2008,
      author = {Joldes, G.R. and Wittek, A. and Miller, K.},
      title = {Suite of finite element algorithms for accurate computation of soft
      tissue deformation for surgical simulation},
      journal = {Medical Image Analysis},
      year = {2008},
      file = {Joldes2008.pdf:Joldes2008.pdf:PDF},
      keywords = {TEC},
      owner = {Thomas},
      publisher = {Elsevier},
      timestamp = {2011.02.23}
    }
  • G. R. Joldes, A. Wittek, and K. Miller, “Real-time nonlinear finite element computations on GPU – Application to neurosurgical simulation,” Computer Methods in Applied Mechanics and Engineering, 2010.
    [Bibtex]
    @ARTICLE{Joldes2010,
      author = {Joldes, Grand Roman and Wittek, Adam and Miller, Karol},
      title = {Real-time nonlinear finite element computations on GPU - Application
      to neurosurgical simulation},
      journal = {Computer Methods in Applied Mechanics and Engineering},
      year = {2010},
      month = {July},
      abstract = {Application of biomechanical modeling techniques in the area of medical
      image analysis and surgical simulation implies two conflicting requirements:
      accurate results and high solution speeds. Accurate results can be
      obtained only by using appropriate models and solution algorithms.
      In our previous papers we have presented algorithms and solution
      methods for performing accurate nonlinear finite element analysis
      of brain shift (which includes mixed mesh, different non-linear material
      models, finite deformations and brain- skull contacts) in less than
      a minute on a personal computer for models having up to 50.000 degrees
      of freedom. In this paper we present an implementation of our algorithms
      on a Graphics Processing Unit (GPU) using the new NVIDIA Compute
      Unified Device Architecture (CUDA) which leads to more than 20 times
      increase in the computation speed. This makes possible the use of
      meshes with more elements, which better represent the geometry, are
      easier to generate, and provide more accurate results.},
      file = {Joldes2010.pdf:Joldes2010.pdf:PDF},
      issn = {00457825},
      keywords = {biomechanical models,non-rigid image registration, TEC, NES, GPU},
      owner = {thomaskroes},
      publisher = {Elsevier B.V.},
      timestamp = {2010.10.25}
    }
  • F. A. Jolesz, A. Nabavi, and R. Kikinis, “Integration of interventional MRI with computer-assisted surgery,” Journal of Magnetic Resonance Imaging, vol. 13, iss. 1, pp. 69-77, 2001.
    [Bibtex]
    @ARTICLE{Jolesz2001,
      author = {Jolesz, F.A. and Nabavi, A. and Kikinis, R.},
      title = {Integration of interventional MRI with computer-assisted surgery},
      journal = {Journal of Magnetic Resonance Imaging},
      year = {2001},
      volume = {13},
      pages = {69 - 77},
      number = {1},
      file = {Jolesz2001.pdf:Jolesz2001.pdf:PDF},
      issn = {1522-2586},
      owner = {thomaskroes},
      publisher = {Wiley Online Library},
      timestamp = {2011.01.11}
    }
  • A. Joshi, D. Scheinost, K. Vives, D. Spencer, L. Staib, and X. Papademetris, “Novel interaction techniques for neurosurgical planning and stereotactic navigation,” Visualization and Computer Graphics, IEEE Transactions on, vol. 14, iss. 6, pp. 1587-1594, 2008.
    [Bibtex]
    @ARTICLE{Joshi2008,
      author = {Joshi, A. and Scheinost, D. and Vives, K. and Spencer, D. and Staib,
      L. and Papademetris, X.},
      title = {Novel interaction techniques for neurosurgical planning and stereotactic
      navigation},
      journal = {Visualization and Computer Graphics, IEEE Transactions on},
      year = {2008},
      volume = {14},
      pages = {1587 -1594},
      number = {6},
      month = {November - December},
      abstract = {Neurosurgical planning and image guided neurosurgery require the visualization
      of multimodal data obtained from various functional and structural
      image modalities, such as magnetic resonance imaging (MRI), computed
      tomography (CT), functional MRI, Single photon emission computed
      tomography (SPECT) and so on. In the case of epilepsy neurosurgery
      for example, these images are used to identify brain regions to guide
      intracranial electrode implantation and resection. Generally, such
      data is visualized using 2D slices and in some cases using a 3D volume
      rendering along with the functional imaging results. Visualizing
      the activation region effectively by still preserving sufficient
      surrounding brain regions for context is exceedingly important to
      neurologists and surgeons. We present novel interaction techniques
      for visualization of multimodal data to facilitate improved exploration
      and planning for neurosurgery. We extended the line widget from VTK
      to allow surgeons to control the shape of the region of the brain
      that they can visually crop away during exploration and surgery.
      We allow simple spherical, cubical, ellipsoidal and cylindrical (probe
      aligned cuts) for exploration purposes. In addition we integrate
      the cropping tool with the image-guided navigation system used for
      epilepsy neurosurgery. We are currently investigating the use of
      these new tools in surgical planning and based on further feedback
      from our neurosurgeons we will integrate them into the setup used
      for image-guided neurosurgery.},
      file = {:Joshi2008.pdf:PDF},
      issn = {1077-2626},
      keywords = {3D volume rendering;functional MRI;image guided neurosurgery;image-guided
      neurosurgery;magnetic resonance imaging;multimodal data visualization;neurosurgeons;neurosurgical
      planning;single photon emission computed tomography;stereotactic
      navigation;structural image modalities;data visualisation;medical
      computing;rendering (computer graphics);surgery;Computer Graphics;Computer
      Simulation;Humans;Imaging, Three-Dimensional;Models, Neurological;Stereotaxic
      Techniques;Surgery, Computer-Assisted;User-Computer Interface;},
      owner = {thomaskroes},
      timestamp = {2010.11.02}
    }
  • R. Kikinis, P. L. Gleason, T. M. Moriarty, M. R. Moore, E. Alexander III, P. E. Stieg, M. Matsumae, W. E. Lorensen, H. E. Cline, P. M. L. Black, and others, “Computer-assisted interactive three-dimensional planning for neurosurgical procedures,” Neurosurgery, vol. 38, iss. 4, p. 640, 1996.
    [Bibtex]
    @ARTICLE{Kikinis1996,
      author = {Kikinis, R. and Gleason, P.L. and Moriarty, T.M. and Moore, M.R.
      and Alexander III, E. and Stieg, P.E. and Matsumae, M. and Lorensen,
      W.E. and Cline, H.E. and Black, P.M.L. and others},
      title = {Computer-assisted interactive three-dimensional planning for neurosurgical
      procedures},
      journal = {Neurosurgery},
      year = {1996},
      volume = {38},
      pages = {640},
      number = {4},
      issn = {0148-396X},
      keywords = {APP, PLA, NES},
      owner = {Thomas},
      timestamp = {2011.02.03}
    }
  • R. A. Kockro, L. Serra, Y. Tseng-Tsai, C. Chan, S. Yih-Yian, C. Gim-Guan, E. Lee, L. Y. Hoe, N. Hern, and W. L. Nowinski, “Planning and simulation of neurosurgery in a virtual reality environment,” Neurosurgery, vol. 46, iss. 1, p. 118, 2000.
    [Bibtex]
    @ARTICLE{Kockro2000,
      author = {Kockro, R.A. and Serra, L. and Tseng-Tsai, Y. and Chan, C. and Yih-Yian,
      S. and Gim-Guan, C. and Lee, E. and Hoe, L.Y. and Hern, N. and Nowinski,
      W.L.},
      title = {Planning and simulation of neurosurgery in a virtual reality environment},
      journal = {Neurosurgery},
      year = {2000},
      volume = {46},
      pages = {118},
      number = {1},
      issn = {0148-396X},
      owner = {Th},
      timestamp = {2011.03.04}
    }
  • Y. Kosugi, E. Watanabe, J. Goto, T. Watanabe, S. Yoshimoto, K. Takakura, and J. Ikebe, “An articulated neurosurgical navigation system using MRI and CT images,” IEEE Transactions on Biomedical Engineering, vol. 35, iss. 2, pp. 147-152, 1988.
    [Bibtex]
    @ARTICLE{Kosugi1988,
      author = {Kosugi, Y. and Watanabe, E. and Goto, J. and Watanabe, T. and Yoshimoto,
      S. and Takakura, K. and Ikebe, J.},
      title = {An articulated neurosurgical navigation system using MRI and CT images},
      journal = {IEEE Transactions on Biomedical Engineering},
      year = {1988},
      volume = {35},
      pages = {147 - 152},
      number = {2},
      file = {Kosugi1988.pdf:Kosugi1988.pdf:PDF},
      issn = {0018-9294},
      owner = {thomaskroes},
      timestamp = {2011.01.06}
    }
  • R. J. Maciunas, “Computer-assisted neurosurgery,” Clinical Neurosurgery, vol. 53, p. 267, 2006.
    [Bibtex]
    @ARTICLE{Maciunas2006,
      author = {Maciunas, R.J.},
      title = {Computer-assisted neurosurgery},
      journal = {Clinical Neurosurgery},
      year = {2006},
      volume = {53},
      pages = {267},
      file = {Maciunas2006.PDF:Maciunas2006.PDF:PDF},
      issn = {0069-4827},
      keywords = {REV, NES},
      owner = {thomaskroes},
      publisher = {LIPPINCOTT WILLIAMS \& WILKINS},
      timestamp = {2011.01.11}
    }
  • K. Miller, A. Wittek, G. Joldes, A. Horton, T. Dutta-Roy, J. Berger, and L. Morriss, “Modelling brain deformations for computer-integrated neurosurgery,” International Journal for Numerical Methods in Biomedical Engineering, vol. 26, iss. 1, pp. 117-138, 2010.
    [Bibtex]
    @ARTICLE{Miller2010,
      author = {Miller, K. and Wittek, A. and Joldes, G. and Horton, A. and Dutta-Roy,
      T. and Berger, J. and Morriss, L.},
      title = {Modelling brain deformations for computer-integrated neurosurgery},
      journal = {International Journal for Numerical Methods in Biomedical Engineering},
      year = {2010},
      volume = {26},
      pages = {117 - 138},
      number = {1},
      file = {Miller2010.pdf:Miller2010.pdf:PDF},
      issn = {2040-7947},
      keywords = {TEC, NES},
      owner = {thomaskroes},
      publisher = {John Wiley \& Sons},
      timestamp = {2011.01.11}
    }
  • J. Rexilius, S. Warfield, C. Guttmann, X. Wei, R. Benson, L. Wolfson, M. Shenton, H. Handels, and R. Kikinis, “A Novel Nonrigid Registration Algorithm and Applications,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2001, W. Niessen and M. Viergever, Eds., Springer Berlin / Heidelberg, 2001, vol. 2208, pp. 923-931.
    [Bibtex]
    @INCOLLECTION{Rexilius2001,
      author = {Rexilius, J. and Warfield, S. and Guttmann, C. and Wei, X. and Benson,
      R. and Wolfson, L. and Shenton, M. and Handels, H. and Kikinis, R.},
      title = {A Novel Nonrigid Registration Algorithm and Applications},
      booktitle = {Medical Image Computing and Computer-Assisted Intervention – MICCAI
      2001},
      publisher = {Springer Berlin / Heidelberg},
      year = {2001},
      editor = {Niessen, Wiro and Viergever, Max},
      volume = {2208},
      series = {Lecture Notes in Computer Science},
      pages = {923 - 931},
      abstract = {In this paper we describe a new algorithm for nonrigid registration
      of brain images based on an elastically deformable model. The use
      of registration methods has become an important tool for computer-assisted
      diagnosis and surgery. Our goal was to improve analysis in various
      applications of neurology and neurosurgery by improving nonrigid
      registration. A local gray level similarity measure is used to make
      an initial sparse displacement field estimate. The field is initially
      estimated at locations determined by local features, and then a linear
      elastic model is used to infer the volumetric deformation across
      the image. The associated partial differential equation is solved
      by a finite element approach. A model of empirically observed variability
      of the brain was created from a dataset of 154 young adults. Both
      homogeneous and inhomogeneous elasticity models were compared. The
      algorithm has been applied to medical applications including intraoperative
      images of neurosurgery showing brain shift and a study of gait and
      balance disorder.},
      affiliation = {Surgical Planning Laboratory, Harvard Medical School &amp; Brigham
      and Women’s Hospital, 75 Francis St., Boston, MA 02115, USA},
      file = {Rexilius2001.pdf:Rexilius2001.pdf:PDF},
      keywords = {TEC},
      owner = {thomaskroes},
      timestamp = {2011.01.11}
    }
  • A. T. Stadie, R. A. Kockro, R. Reisch, A. Tropine, S. Boor, P. Stoeter, and A. Perneczky, “Virtual reality system for planning minimally invasive neurosurgery. Technical note.,” Journal of neurosurgery, vol. 108, iss. 2, pp. 382-94, 2008.
    [Bibtex]
    @ARTICLE{Stadie2008,
      author = {Stadie, Axel Thomas and Kockro, Ralf Alfons and Reisch, Robert and
      Tropine, Andrei and Boor, Stephan and Stoeter, Peter and Perneczky,
      Axel},
      title = {Virtual reality system for planning minimally invasive neurosurgery.
      Technical note.},
      journal = {Journal of neurosurgery},
      year = {2008},
      volume = {108},
      pages = {382 - 94},
      number = {2},
      month = {February},
      abstract = {OBJECT: The authors report on their experience with a 3D virtual reality
      system for planning minimally invasive neurosurgical procedures.
      METHODS: Between October 2002 and April 2006, the authors used the
      Dextroscope (Volume Interactions, Ltd.) to plan neurosurgical procedures
      in 106 patients, including 100 with intracranial and 6 with spinal
      lesions. The planning was performed 1 to 3 days preoperatively, and
      in 12 cases, 3D prints of the planning procedure were taken into
      the operating room. A questionnaire was completed by the neurosurgeon
      after the planning procedure. RESULTS: After a short period of acclimatization,
      the system proved easy to operate and is currently used routinely
      for preoperative planning of difficult cases at the authors' institution.
      It was felt that working with a virtual reality multimodal model
      of the patient significantly improved surgical planning. The pathoanatomy
      in individual patients could easily be understood in great detail,
      enabling the authors to determine the surgical trajectory precisely
      and in the most minimally invasive way. CONCLUSIONS: The authors
      found the preoperative 3D model to be in high concordance with intraoperative
      conditions; the resulting intraoperative "d\'{e}j\`{a}-vu" feeling
      enhanced surgical confidence. In all procedures planned with the
      Dextroscope, the chosen surgical strategy proved to be the correct
      choice. Three-dimensional virtual reality models of a patient allow
      quick and easy understanding of complex intracranial lesions.},
      file = {Stadie2008.pdf:Stadie2008.pdf:PDF},
      issn = {0022-3085},
      keywords = {Adenoma,Adenoma: surgery,Adult,Aged,Angiography,Angiography: methods,Brain
      Neoplasms,Brain Neoplasms: surgery,Computer Simulation,Diffusion
      Magnetic Resonance Imaging,Female,Hemangioma, Cavernous, Central
      Nervous System,Hemangioma, Cavernous, Central Nervous System: sur,Humans,Image
      Processing, Computer-Assisted,Image Processing, Computer-Assisted:
      methods,Imaging, Three-Dimensional,Imaging, Three-Dimensional: methods,Intracranial
      Aneurysm,Intracranial Aneurysm: surgery,Magnetic Resonance Angiography,Magnetic
      Resonance Imaging,Male,Meningioma,Meningioma: surgery,Middle Aged,Neurosurgical
      Procedures,Neurosurgical Procedures: methods,Patient Care Planning,Surgery,
      Computer-Assisted,Surgery, Computer-Assisted: methods,Surgical Procedures,
      Minimally Invasive,Surgical Procedures, Minimally Invasive: methods,Tomography,
      X-Ray Computed,Tomography, X-Ray Computed: methods,User-Computer
      Interface, STV, APP, NES, AUR, SUR, VOR},
      owner = {thomaskroes},
      pmid = {18240940},
      timestamp = {2010.10.25}
    }
  • P. Tirelli, E. De Momi, N. Borghese, and G. Ferrigno, “Computer Assisted Neurosurgery,” International Journal of Computer Assisted Radiology and Surgery, vol. 4, pp. 85-91, 2009.
    [Bibtex]
    @ARTICLE{Tirelli2009,
      author = {Tirelli, P. and De Momi, E. and Borghese, NA and Ferrigno, G.},
      title = {Computer Assisted Neurosurgery},
      journal = {International Journal of Computer Assisted Radiology and Surgery},
      year = {2009},
      volume = {4},
      pages = {85 - 91},
      file = {Tirelli2009.pdf:Tirelli2009.pdf:PDF},
      issn = {1861-6410},
      keywords = {TEC},
      owner = {thomaskroes},
      publisher = {Springer},
      timestamp = {2011.01.11}
    }
  • A. Wang, A. Parrent, S. Mirsattari, and T. Peters, “Computer Assisted Neurosurgery,” International Journal of Computer Assisted Radiology and Surgery, vol. 5, pp. 106-113, 2010.
    [Bibtex]
    @ARTICLE{Wang2010,
      author = {Wang, A. and Parrent, A. and Mirsattari, S. and Peters, T.},
      title = {Computer Assisted Neurosurgery},
      journal = {International Journal of Computer Assisted Radiology and Surgery},
      year = {2010},
      volume = {5},
      pages = {106--113},
      file = {Wang2010.pdf:Wang2010.pdf:PDF},
      issn = {1861-6410},
      keywords = {APP, NES},
      owner = {thomaskroes},
      publisher = {Springer},
      timestamp = {2011.01.11}
    }

Reviews

  • C. R. Johnson and D. M. Weinstein, “Biomedical computing and visualization,” in Proceedings of the 29th Australasian Computer Science Conference – Volume 48, Darlinghurst, Australia, Australia, 2006, pp. 3-10.
    [Bibtex]
    @INPROCEEDINGS{Johnson2006,
      author = {Johnson, Chris R. and Weinstein, David M.},
      title = {Biomedical computing and visualization},
      booktitle = {Proceedings of the 29th Australasian Computer Science Conference
      - Volume 48},
      year = {2006},
      series = {ACSC '06},
      pages = {3 - 10},
      address = {Darlinghurst, Australia, Australia},
      publisher = {Australian Computer Society, Inc.},
      acmid = {1151700},
      file = {Johnson2006.pdf:Johnson2006.pdf:PDF},
      isbn = {1-920682-30-9},
      keywords = {biomedical computing, imaging, problem solving environment, visualization,
      REV, NES},
      location = {Hobart, Australia},
      numpages = {8},
      owner = {thomaskroes},
      timestamp = {2011.01.25}
    }
  • T. Peters, K. Finnis, T. Guo, and A. Parrent, “Neurosurgical Applications,” in Image-Guided Interventions, T. Peters and K. Cleary, Eds., Springer US, 2008, pp. 309-332.
    [Bibtex]
    @INCOLLECTION{Peters2008,
      author = {Peters, Terry and Finnis, Kirk and Guo, Ting and Parrent, Andrew},
      title = {Neurosurgical Applications},
      booktitle = {Image-Guided Interventions},
      publisher = {Springer US},
      year = {2008},
      editor = {Peters, Terry and Cleary, Kevin},
      pages = {309 - 332},
      note = {Chapter 11},
      abstract = {This chapter demonstrates a particular application of stereotactic
      neurosurgery, used in conjunction with deep brain atlases and an
      electrophysiological database, to guide the implantation of lesioning
      devices and stimulation electrodes to alleviate the symptoms of Parkinson’s
      disease and other diseases of the motor system. Central to this work
      is the nonrigid mapping of individual patients’ brains to a standard
      anatomical brain template. This operation not only maps the structure
      in the deep brain of individual patients to match the template, but
      also creates a warping matrix that allows the location of data collected
      from individual patients to be mapped to the database. This database
      may in turn be mapped to new patients to indicate the probable locations
      of stimuli and responses. This information can be employed to assist
      the surgeon in making an initial estimate of the electrode positioning,
      and reduce the exploration needed to finalize the target position
      in which to create a lesion or place a stimulator.},
      affiliation = {University of Western Ontario Robarts Research Institute 100 Perth
      Drive N6A 5K8 London ON Canada},
      file = {Peters2008.pdf:Peters2008.pdf:PDF},
      isbn = {978-0-387-73858-1},
      keyword = {Engineering},
      keywords = {REV, NES},
      owner = {Thomas},
      timestamp = {2011.02.24}
    }

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