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Medical Applications of NDT Data Fusion

  • Pierre Jannin
  • Christophe Grova
  • Bernard Gibaud

Abstract

The fusion of different sources of information has always been a component of medical practice. The complexity of biological phenomena is such that they cannot be explained with a single exploration. The complementary nature of the available exploration techniques (modalities) helps the physician in refining his diagnosis, preparing or performing therapeutic procedures. In this respect it is interesting to note that the development of new medical modalities has not led to the replacement of former ones, and that obviously there is no single modality providing the clinician with all possible sources of information. Before the development of computerised registration tools, data fusion involved pure mental matching of the data sets based on well-known common structures, the matching of the rest of the data being mentally interpolated from this initial step. The parallel emergence of new digital medical imaging devices, communication networks and powerful workstations has made it possible not only to display images but also to transfer and process them. Image processing methods have recently been developed to perform direct, automatic data matching. These methods define the multimodal data fusion topic. They have modified the way multimodal matching is performed as well as the way multimodal information is used; moving from a mental to a computer assisted fusion process. This evolution has led to a more accurate, more visual, more quantitative and therefore more objective fusion process.

Keywords

Single Photon Emission Compute Tomography Data Fusion Registration Method Geometrical Transformation Medical Image Computing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Maintz J.A., Viergever M.A., A review of medical image registration, Medical Image Analysis, 1998,2(l):l–36.Google Scholar
  2. 2.
    Maurer C.R., Fitzpatrick J.M., A Review of Medical Image Registration, Interactive Image-Guided Neurosurgery, Maciunas R.J. Ed., American Association of Neurological Surgeons, USA, 1993, chapter 3:17–44.Google Scholar
  3. 3.
    Van Den Elsen P.A., Pol E.J.D., Viergever M.A., Medical image matchinga review with classification, IEEE Eng. in Medicine and Biology Magazine, 1993,12(l):26–39.CrossRefGoogle Scholar
  4. 4.
    Hawkes D.J., Algorithms for radiological image registration and their clinical application, J. of Anatomy, 1998,193(3):347–361.CrossRefGoogle Scholar
  5. 5.
    Brown L.G., A survey of image registration techniques, ACM Computing Surveys, 1992, 24(4):325–376.CrossRefGoogle Scholar
  6. 6.
    Duncan R., SPECT Imaging in Focal Epilepsy, SPECT Imaging of the Brain, Kluwer Academic Publishers, 1997, chapter 2:43–68.CrossRefGoogle Scholar
  7. 7.
    Friston K.J., Jezzard P., Turner R., Analysis of functional MRI time-series, Human Brain Mapping, 1994,2(1):153–171.CrossRefGoogle Scholar
  8. 8.
    Worsley K.J., Friston K.J., Analysis of fMRI time-series revisited — again, Neuroimage, 1995, 2:173–181.CrossRefGoogle Scholar
  9. 9.
    Lacey A.J., Thacker N.A., Jackson A., Burton E., Locating motion artefacts in parametric fMRI analysis, Proc. 2nd Inter. Conf. on Medical Image Computing and Computer-Assisted Interventions, 1999, Cambridge, England, Lecture Notes in Computer Science, 1679:524–532.Google Scholar
  10. 10.
    Behrenbruch C., Marias K., Armitage P., Yam M., Moore N., English R., Brady M., MRI-mammography 2D/3D data fusion for breast pathology assessment, Proc. 3 Inter. Conf. on Medical Image Computing and Computer-Assisted Interventions, 2000, Pittsburgh, USA, Lecture Notes in Computer Science, Springer, 1935:307–316.Google Scholar
  11. 11.
    Boes, J.L., C.R. Meyer, Multi-variate mutual information for registration, Proc. 2nd Inter. Conf. on Medical Image Computing and Computer-Assisted Interventions, 1999, Cambridge, England, Lecture Notes in Computer Science, 1679:606–612.Google Scholar
  12. 12.
    Farrell E.J., Gorniak R.J.T., Kramer E.L., Noz M.E., Maguire Jr. G.Q., Reddy D.P., Graphical 3D medical image registration and quantification, J. of Medical Systems, 1997,21(3):155–172.CrossRefGoogle Scholar
  13. 13.
    Kalfas I.H., Kormos D.W., Murphy M.A., Mc Kenzie R.L., Baraett G.H., Bell G.R., Steiner C.P., Trimble M.B., Weisenberger J.P., Application offrameless stereotaxy to pedicle screw fixation of the spine, Journal of Neurosurgery, 1995,83:641–647.CrossRefGoogle Scholar
  14. 14.
    Zana F., Klein J.C., A multimodal registration algorithm of eye fundus images using vessels detection and Hough transform, IEEE Trans, on Medical Imaging, 1999,18(5):419–428.CrossRefGoogle Scholar
  15. 15.
    Lloret D., López A., Serrat J., Villanueva J.J., Creaseness-based computer tomography and magnetic resonance registration: comparison with the mutual information method, J. of Electronic Imaging, 1999,8(3):255–262.CrossRefGoogle Scholar
  16. 16.
    Stefan H., Schneider S., Feistel H., Pawlik G., Schüler P., Abraham-Fuchs K., Schelgel T., Neubauer U., Huk W.J., Ictal and interictal activity in partial epilepsy recorded with multichannel magnetoelectroencephalography: Correlation of Electroencephalography/Electrocorticography, magnetic resonance imaging, single photon emission computed tomography, and positron emission tomography findings, Epilepsia, 1992,33(5):874–887.CrossRefGoogle Scholar
  17. 17.
    Liu A.K., Belliveau J.W., Dale A.M., Spatiotemporal imaging of human brain activity using junctional MRI constrained magnetoencephalogrphy data: Monte Carlo simulations, Proc. Natl. Acad. Sci. USA, 1998,95:8945–8950.CrossRefGoogle Scholar
  18. 18.
    Maurer C.R. Jr., Hill D.L.G., Martin A.J., Liu H., McCue M., Rueckert D., Lloret D., Hall W.A., Maxwell R.E., Hawkes D.J., Truwit C.L., Investigation of intra-operative brain deformation using a L5T interventional MR system: Preliminary results, TREE Trans, on Medical Imaging, 1998, 17(5):817–825.CrossRefGoogle Scholar
  19. 19.
    Comeau R.M., Fenster A., Peters T.M., Intraoperative US imaging in image-guided neurosurgery, Radiographics, 1998,18(4): 1019–1027.Google Scholar
  20. 20.
    Olivier A., Germano I.M., Cukiert A., Peters T., Frameless stereotaxy for surgery of the epilepsies: Preliminary experience, J. of Neurosurgery, 1994, 81:629–633.CrossRefGoogle Scholar
  21. 21.
    Doyle W.K., Interactive Image-Directed Epilepsy Surgery: Rudimentary virtual reality in neurosurgery, Interactive Technology and the New Paradigm for Healthcare, IOS Press, 1995, chapter 16:91-100.Google Scholar
  22. 22.
    Tebo S.A., Leopold D.A., Long D.M., Kennedy D.W., Zinreich S.J., An optical 3D digitizer for frameless stereotactic surgery, IEEE Computer Graphics and Applications, 1996,16(1):55–64.CrossRefGoogle Scholar
  23. 23.
    Cuchet E., Knoplioch J., Dormont D., Marsault C., Registration in neurosurgery and neuroradiotherapy applications, J. of Image Guided Surgery, 1995,1:198–207.CrossRefGoogle Scholar
  24. 24.
    Jannin P., Scarabin J.M., Rolland Y., Schwartz D., A real 3d approach for the simulation of neurosurgical stereotactic act, Proc. SPIE Medical Imaging VHI: Image Capture, Formatting, and Display, Newport Beach, USA, 1994,2164:155–166.Google Scholar
  25. 25.
    Scarabin J.M., Croci S., Jannin P., Romeas R., Maurincomme E., Behague M., Carsin M., A new concept of stereotactic room with multimodal imaging, Proc. 13th Inter. Congress and Exhibition, Computer Assisted Radiology and Surgery, Paris, France, 1999,841–845.Google Scholar
  26. 26.
    Simon D.A., Herbert M., Kanade T., Techniques for fast and accurate intrasurgical registration, J. of Image Guided Surgery, 1995, l(l):17–29.CrossRefGoogle Scholar
  27. 27.
    Weese J., Penney G.P., Desmedt P., Buzug T.M., Hill D.L.G., Hawkes D.J.H., Voxel-based 2-D/3-D registration of fluoroscopy images and CT scans for image-guided surgery, IEEE Trans, on Information Technology in Biomedicine, 1997, l(4):284–293.CrossRefGoogle Scholar
  28. 28.
    Weese J., Buzug T. M., Lorenz C., Fassnacht C., An approach to 2D/3D registration of a vertebra in 2D X-ray fluoroscopies with 3D CT images, Lecture Notes in Computer Science, 1997, 1205:119–128.CrossRefGoogle Scholar
  29. 29.
    Betting F., Feldmar J., 3D-2D projective registration of anatomical surfaces with their projections, Proc. 14th Information Processing in Medical Imaging Conf., 1995, Ile de Berder, France, 3:275–286.Google Scholar
  30. 30.
    Evans A.C., Collins D.L., Mills S. R., Brown E. D., Kelly R.L., 3D statistical neuroanatomical models from 305 MRI volumes, Proc. IEEE Nuclear Science Symp. and Medical Imaging Conf., San Francisco, USA, 1993,1813–1817.Google Scholar
  31. 31.
    Collins D.L., Holmes C.J., Peters T.M., Evans A.C., Automatic 3D model-based neuroanatomical segmentation, Human Brain Mapping, 1995,3(3): 190–208.CrossRefGoogle Scholar
  32. 32.
    Ashburner J., Friston K.J., Multimodal image coregistration and partitioning — a unified framework, Neurolmage, 1997,6(3):209–217.CrossRefGoogle Scholar
  33. 33.
    Mazziotta J.C., Toga A.W., Evans A.C., Fox P., Lancaster J.L., A probabilistic atlas of human brain: Theory and rationales for its development, Neuroimage, 1995,2:89–101.CrossRefGoogle Scholar
  34. 34.
    Friston K.J., Ashburner J., Poline J.B., Frith C.D., Heather J.D., Frackowiak R.S.J., Spatial registration and normalization of images. Human Brain Mapping, 1995,2:165–189.CrossRefGoogle Scholar
  35. 35.
    Guimond A., Roche A., Ayache N., Meunier J., Multimodal brain warping using the demons algorithm and adaptive intensity corrections, INRIA, France, Report N°RR-396,1999.Google Scholar
  36. 36.
    Barillot C., Fusion de données et imagerie 3D en médecine, Thèse d’habilitauon à dinger des recherches, IRISA, Université de Rennes, France, 1999.Google Scholar
  37. 37.
    Talairach J., Tournoux P., Co-Planar stereotactic atlas of the human brain, Georg Thieme Verlag, 1988.Google Scholar
  38. 38.
    Collins D.L., Le Goualher G., Evans A.C., Non-linear cerebral registration with sulcal constraints, Proc. 1st Inter. Conf. on Medical Image Computing and Computer-Assisted Interventions, 1998, Cambridge, USA, Lecture Notes in Computer Science, 1496:974–984.Google Scholar
  39. 39.
    Van Den Elsen P., Maintz A., Pol E., Viergever M., Automatic registration of CT and MR brain images using correlation of geometrical features, IEEE Trans, on Medical Imaging, 1995, 14(2):384–396.CrossRefGoogle Scholar
  40. 40.
    Subsol G., Thirion J.P., Ayache N., Steps towards automatic building of anatomical atlases, SPIE Proc. 3rd Conf. on Visualization in Biomedical Computing, 1994, Rochester, USA, 2359:435–446.Google Scholar
  41. 41.
    Faugeras O., Three-Dimensional Computer Vision: A Geometric Viewpoint, MIT Press, 1993.Google Scholar
  42. 42.
    Bookstein F.L., Principal warps: Thin-plate splines and the decomposition of deformations, IEEE Trans, on Pattern Analysis and Machine Intelligence, 1989,11(6):567–585.CrossRefGoogle Scholar
  43. 43.
    Bookstein F.L., Thin-plate splines and the atlas problem for biomedical images, Proc. 12th Conf. on Information Processing in Medical Imaging, 1991, Wye, England, Lecture Notes in Computer Science, 511:326–342.Google Scholar
  44. 44.
    Sander S., Leahy R., Surface-based labelling of cortical anatomy using a deformable atlas, IEEE Trans, on Medical Imaging, 1997,16(l):41–54.CrossRefGoogle Scholar
  45. 45.
    Thompson P., Toga A., A surface-based technique for warping three-dimensional images of the brain, IEEE Trans, on Medical Imaging, 1996,15(4):402–417.CrossRefGoogle Scholar
  46. 46.
    Szeliski R., Lavallée S., Matching 3-D anatomical surfaces with non-rigid deformations using octree splines, Inter. J. of Computer Vision, 1996,18(2):176–186.Google Scholar
  47. 47.
    Collins D.L., Peters T.M., Evans A.C., An automated 3D non-linear deformation procedure for determination of gross morphometric variability in human brain, SPIE Proc. 3 Conf. on Visualization in Biomedical Computing, 1994, Rochester, USA, 2359:180–190.Google Scholar
  48. 48.
    Hellier P., Barillot C, Memin E„ Perez P., Medical image registration with robust multigrid techniques, Proc. 2nd Inter. Conf. on Medical Image Computing and Computer-Assisted Interventions, 1999, Cambridge, England, Lecture notes in Computer Science, 1679:680–687.Google Scholar
  49. 49.
    Thirion J.P., Non-rigid matching using demons, Proc. Conf. on Computer Vision and Pattern Recognition, Los Alamitos, USA, IEEE Computer Society Press, 1996,245–251.Google Scholar
  50. 50.
    Thirion J.P., Image matching as a diffusion process: An analogy with Maxwells demons, Medical Image Analysis, 1998,2(3):243–260.CrossRefGoogle Scholar
  51. 51.
    Bajcsy R., Kovacic S., Multiresolution elastic matching, Computer Vision, Graphics, and Image Processing, 1989,46(1):1–21.CrossRefGoogle Scholar
  52. 52.
    Gee J., Reivicj M., Bajcsy R., Elastically deforming 3D atlas to match anatomical brain images, J. of Computed Assisted Tomography, 1993,17(2):225–236.CrossRefGoogle Scholar
  53. 53.
    Christensen G.E., Rabbitt R.D., Miller M.I., Joshi S.C., Grenander U., Coogan T.A., Van Essen D.C., Topological properties of smooth anatomic maps, Proc. 14th Inter. Conf. on Information Processing in Medical Imaging, 1995, Dordrecht, Netherlands, 3:101–112.Google Scholar
  54. 54.
    Miller M.I., Christensen G.E., Amit Y.A., Grenander U., Mathematical textbook of deformable neuroanatomies, Medical Sciences, 1993,90:11944–11948.Google Scholar
  55. 55.
    Borgefors G., A new distance transformation approximating the Euclidean distance, Proc. 8th Inter. Conf. on Pattern Recognition, 1986, Paris, France, 336–338.Google Scholar
  56. 56.
    Borgefors G., Hierarchical chamfer matching: A parametric edge matching algorithm, IEEE Trans, on Pattern Analysis and Machine Intelligence, 1988,10(6):849–865.CrossRefGoogle Scholar
  57. 57.
    Pelizzari C.A., Chan G. T.Y., Spelhring D.R., Weichselbaum E. E., Chen C.T., Accurate three-dimensional registration of CT, PET and/or MR images of the brain, J. of Computer Assisted Tomography, 1989,13(l):20–26.CrossRefGoogle Scholar
  58. 58.
    Roche A., Malandain G., Ayache N., Pennec X., Multimodal image registration by maximization of the correlation ratio, INRIA, France, Report N°RR-3378,1998.Google Scholar
  59. 59.
    Roche A., Malandain G., Pennec X., Ayache N., The correlation ratio as a new similarity measure for multimodal image registration, Proc. 1st Inter. Conf. on Medical Image Computing and Computer-Assisted Intervention, 1998, Cambridge, USA, Lecture Notes in Computer Science, 1496:1115–1124.Google Scholar
  60. 60.
    Woods R.P., Mazziotta J.C., Cherry S.R., MRI-PET registration with automated algorithm, J. of Computer Assisted Tomography, 1993,17(4):536–546.CrossRefGoogle Scholar
  61. 61.
    Maes F., Collignon A., Vandermeulen D., Marchal G., Suetens P., Multimodality image registration by maximization of mutual information, IEEE Trans, on Medical Imaging, 1997, 16(2):187–198.CrossRefGoogle Scholar
  62. 62.
    Wells III W.M., Viola P., Atsumi H., Nakajima S., Kikinis R., Multi-modal volume registration by maximization of mutual information, Medical Image Analysis, 1996,1(1):35–51.CrossRefGoogle Scholar
  63. 63.
    Roche A., Malandain G., Ayache N., Unifying maximum likelihood approaches in medical image registration, INRIA, France, Report N°RR-3741,1999.Google Scholar
  64. 64.
    De Castro E., Morandi C., Registration of translated and rotated images using finite Fourier transforms, IEEE Trans. Pattern Analysis and Machine Intelligence, 1987,9:700–703.CrossRefGoogle Scholar
  65. 65.
    Pluim J.P.W., Maintz J.B.A., Viergever M.A., Interpolation artefacts in mutual information-based image registration, Computer Vision and Image Understanding, 2000,77(2):211–232.CrossRefGoogle Scholar
  66. 66.
    Maes F., Vendermeulen D., Suetens P., Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximisation of mutual information, Medical Image Analysis, 1999,3(4):373–386.CrossRefGoogle Scholar
  67. 67.
    Black M.J., Rangarajan A., On the unification of line processes, outlier rejection, and robust statistics with applications in early vision, Inter. J. of Computer Vision, 1996,19(1):57–91.CrossRefGoogle Scholar
  68. 68.
    Nikou C., Heitz F., Armspach J.P., Namer I.J., Grucker D., Registration of MR/MR and MR/SPECT brain images by fast stochastic optimization of robust voxel similarity measures, Neuroimage, 1998, 8:30–43.CrossRefGoogle Scholar
  69. 69.
    Press W.H., Teukolsky S.A., Vetterling W.T., Flannery B.P., Numerical Recipes in C, Cambridge University Press, 1992.Google Scholar
  70. 70.
    Jannin P., Bouliou A., Scarabin J.M., Barillot C., Lubert J., Visual matching between real and virtual images in image guided neurosurgery, SPIE Proc. on Medical Imaging: Image Display, 1997,3031:518–526.Google Scholar
  71. 71.
    Jannin P., Grova C., Schwartz D., Barillot C., Gibaud B., Visual qualitative comparison between functional neuro-imaging (MEG, fMRI, SPECT), Proc. 13th Computer Assisted Radiology and Surgery Conf., 1999, Paris, France, 238–243.Google Scholar
  72. 72.
    Viergever M.A., Maintz J.B.A., Stokking R., Integration of Junctional and anatomical brain images, Biophysical Chemistry, 1997,68(l–3):207–219.CrossRefGoogle Scholar
  73. 73.
    Socoloinsky D.A., Wolff L.B., Image fusion for enhanced visualization of brain imaging, SPIE Proc. on Medical Imaging: Image Display, 1999,3658:352–362.Google Scholar
  74. 74.
    Noordmans H.J., Van der Voort H.T.M., Rutten G.J.M., Viergever M.A., Physically realistic visualization of embedded volume structures for medical image data, SPIE Proc. on Medical Imaging: Image Display, 1999,3658:613–620.Google Scholar
  75. 75.
    Colin A., Boire J.-Y., MRI-SPECT fusion for the synthesis of high resolution 3d functional brain images: A preliminary study, Computer Methods and Programs in Biomedicine, 1999,60(2): 107–116.CrossRefGoogle Scholar
  76. 76.
    Hawkes D.J., Hill D.L.G., Lehmann E.D., Robinson G.P., Maisey M.N., Colchester A.C.F., Preliminary work on the interpretation of SPECT images with the aid of registered MR images and an MR derived 3D neuro-anatomical atlas, 3D Imaging in Medicine, Springer Verlag, 1990, 241–251.Google Scholar
  77. 77.
    O’Brien T.J., O’Connor M.K., Mullan B.P., Brinkmann B.H., Hanson D., Jack C.R., So E.L., Subtraction ictal SPECT co-registered to MRI in partial epilepsy: Description and technical validation of the method with phantom and patient studies, Nuclear Medicine Communications, 1998,19:31–45.CrossRefGoogle Scholar
  78. 78.
    Lundervold A., Storvik G., Segmentation of brain parenchyma and cerebrospinal fluid in multispectral magnetic resonance images, IEEE Trans, on Medical Imaging, 1995, 14(2):339–349.CrossRefGoogle Scholar
  79. 79.
    Brinkmann B.H., O’Brien T.J., Aharon S., O’Connor M.K., Mullan B.P., Hanson D.P., Robb R.A., Quantitative and clinical analysis of SPECT image registration for epilepsy studies. The J. of Nuclear Medecine, 1999,40(7):1098–1105.Google Scholar
  80. 80.
    Le Goualher G., Procyk E., Collins D.L., Venugopal R., Barillot C., Evans A.C., Automated extraction and variability analysis of sulcal neuroanatomy, IEEE Trans, on Medical Imaging, 1999,18(3):206–217.CrossRefGoogle Scholar
  81. 81.
    Friston K.J., Holmes A.P.,Worsley K.J., Poline J.B., Frith C.D., Frackowiak R.S.J., Statistical parametric maps in functional imaging: A general linear approach, Human Brain Mapping, 1995,2:189–210.CrossRefGoogle Scholar
  82. 82.
    Taylor C.A., Draney M.T., Ku J.P., Parker D., Steele B.N., Wang K., Zarins C.K., Predictive medicine: Computational techniques in therapeutic decision-making, Computer aided surgery, 1999,4:231–247.CrossRefGoogle Scholar
  83. 83.
    Sumanaweera T.S., Adler J.R., Napel S., Glover G.H., Characterization of spatial distortion in magnetic resonance imaging and its implications for stereotactic surgery, Neurosurgery, 1994, 35(4):696–704.CrossRefGoogle Scholar
  84. 84.
    Collins D.L., Zijdenbos A.P., Kollokian V., Sled J., Kabani N.J., Holmes C.J., Evans A.C., Design and construction of a realistic digital brain phantom, IEEE Trans, on Medical Imaging, 1998, 17(3):463–468.CrossRefGoogle Scholar
  85. 85.
    West J., Fitzpatrick J.M., Wang M.Y., Dawant B.M., Maurer C.R., Kessler R.M., Maciunas R.J., Barillot C., Lemoine D., Collignon A., Maes F., Suetens P., Vandermeulen D., Van Den Elsen P.A., Napel S., Sumanaweera T.S., Harkness B., Hemler P.F., Hill D.L.G., Hawkes D.J., Studholme C., Maintz J.B.A., Viergever M.A., Malandin G., Pennec X., Noz M.E., Maguire G.Q., Pollack M., Pellizzari C.A., Robb R.A., Hanson D., Woods R., Comparison and evaluation of retrospective intermodality image registration techniques, SPIE Proc. on Medical Imaging: Image Processing, 1996, 2710:332–347.Google Scholar
  86. 86.
    West J., Fitzpatrick J.M., Wang M.Y., Dawant B.M., Maurer C.R., Kessler R.M., Maciunas R.J., Barillot C., Lemoine D., Collignon A., Maes F., Suetens P., Vandermeulen D., Van Den Elsen P.A., Napel S., Sumanaweera T.S., Harkness B., Hemler P.F., Hill D.L.G., Hawkes D.J., Studholme C., Maintz J.B.A., Viergever M.A., Malandin G., Pennec X., Noz M.E., Maguire G.Q., Pollack M., Pellizzari C.A., Robb R.A., Hanson D., Woods R., Comparison and evaluation of retrospective intermodality brain image registration techniques, J. of Computer Assisted Tomography, 1997, 21(4):554–566,1997.CrossRefGoogle Scholar
  87. 87.
    Van Bemmel J.H., Musen M.A., Handbook of Medical Informatics, Springer Verlag, 1997.Google Scholar
  88. 88.
    Vieth J., Kober H., Weise E., Daun A., Moeger A., Friedrich, Pongratz H., Functional 3d localization of cerebrovascular accidents by magnetoencephalography (MEG), Neurological Research, 1992,14:132–134.Google Scholar
  89. 89.
    Schwartz D., Localisation Des Generateurs Intra-Cerebraux de L’activite MEG et EEG: Evaluation de la Precision Spatiale et Temporelle, PhD thesis, Universite de Rennes 1, France, 1997.Google Scholar
  90. 90.
    Schwartz D., Poiseau E., Lemoine D., Barillot C., Registration of MEG/EEG data with MRI: Methodology and precision issues, Brain Topography, 1996,9(2):101–116.CrossRefGoogle Scholar
  91. 91.
    Schwartz D., Badier J.M., Bihouee P., Bouliou A., Evaluation of a new MEG-EEG spatio-temporal approach using realistic sources, Brain Topography, 1999,11(4):279–289.CrossRefGoogle Scholar
  92. 92.
    Barillot C., Schwartz D.P., LeGoualher G., Gibaud B., Scarabin J.M., Representation of MEG/EEG data in a 3D morphological environment, Computer Assisted Radiology, Proc. of the Inter. Symp. on Computer and Communication Systems for Image Guided Diagnosis and Therapy, 1996, Paris, France, 249–254.Google Scholar
  93. 93.
    Yoo T.S., Ackerman M.J., Vannier M., Toward a common validation methodology for segmentation and registration algorithms, Proc. 3rd Inter. Conf. on Medical Image Computing and Computer-Assisted Interventions, 2000, Pittsburgh, USA, Lecture Notes in Computer Science, Springer, 1935:422–431.Google Scholar
  94. 94.
    DICOM Committee, Digital Imaging and Communications in Medecine (DICOM), ACR-NEMA Standard PS3.1-9,1993.Google Scholar
  95. 95.
    Jannin J., Reig O.J., Seigneuret E., Grova C., Morandi X., Scarabin J.M., A data fusion environment for multimodal and multi-informational neuro-navigation, J. of Computer Aided Surgery, 2000,5(1):1–10.CrossRefGoogle Scholar
  96. 96.
    Satava R.M., Jones S.B., Current and future applications of virtual reality for medicine; Proc. of IEEE, 1998,86(3):484–489.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • Pierre Jannin
    • 1
  • Christophe Grova
    • 1
  • Bernard Gibaud
    • 1
  1. 1.Université de RennesRennesFrance

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