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Development and Characterization of an Automatic Technique for the Alignment of fMRI Time Series

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Abstract

An automatic technique for the registration of fMRI time series has been developed, implemented and tested. The method assumes the human brain to be a rigid body and computes a head coordinate system on the basis of three reference points that lies on the directions corresponding to two of the principal axes of the volume at the intersections with the head boundary. Such directions are found computing the eigenvectors of the symmetric inertia matrix of the image. The inertia components were extracted weighting pixels' coordinates with their intensity values. The three reference points were found in the same position, relative to the head, in both the test and the reference images. The technique has been tested using T2*-weighted Magnetic Resonance (MR) images in which known rigid body transformations have been applied. The results obtained indicate that the method offers subvoxel accuracy in correcting misalignment among time points in fMRI time series

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References

  • Alpert, N.M., Brandshaw, J.F., Kennedy, D. and Correia, J.A. The principal axes transformation-a method for image registration. The Journal of Nuclear Medicine, 1990, 31: 1717–1722.

    Google Scholar 

  • Althof, R.J., Wind, M.G.J. and Dobbins, J.T. A rapid and auto-matic image registration algorithm with sub-pixel accu-racy. IEEE Transactions on Medical Imaging, 1997, 16: 308–316.

    Google Scholar 

  • Arendsen, R. and Bentum, M. Design and realization of computer system for high speed single exposure dual-energy image processing. Master Thesis, University of Twente, Netherlands, 1991.

    Google Scholar 

  • Bellers, E.B. and De Bruijn, F.J. On the automation of single exposure dual-energy computed-radiography. Master Thesis. University of Twente, Netherlands, 1993.

    Google Scholar 

  • Besl, P.J. and McKay, N.D. A method for registration of 3-D shapes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1992, 14: 239–256.

    Google Scholar 

  • Ciulla, C., Takeda, T. and Endo, H. MEG characterization of spontaneous alpha rhythm in the human brain. Brain Topography, 1999, 11(3): 211–222.

    Google Scholar 

  • Ciulla, C., Takeda, T., Morabito, M., Endo, H., Kumagai, T. and Xiao, R. MEG measurements of 40Hz auditory evoked re-sponse in human brain. Visualization of Information Processing in the Human Brain: Recent Advances in MEGand Functional MRI. EEG Suppl. 47, 1996: 121–127.

  • Collignon, A., Vandermeulen, D., Suetens, P. and Marchal, G. Automated multimodality image registration based on information theory. Computer Imaging and Vision, 1995, 3: 263–274.

    Google Scholar 

  • Cox, R.W. AFNI: Software for analysis and visualization of functional Magnetic Resonance Neuroimages. Computer and Biomedical Research, 1996, 29: 162–173.

    Google Scholar 

  • Demmel, J.W. Applied numerical linear algebra. SIAM, Society for Industrial and Applied Mathematics, 1997: 195–264.

  • Dobbins, J.T., Rice, J.J., Goodman, P.C., Patz, E.F. Jr. and Ravin, C.E. Variable compensation chest radiography performed with a computed radiography system: Design, consider-ations and initial clinical experience. Radiology, 1993, 187: 55–63.

    Google Scholar 

  • Faber, T.L. and Stokely, E.M. Orientation of 3-D structures in medical images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1988, 10(5): 626–633.

    Google Scholar 

  • Fitzpatrick, J.M., Hill, D.L.G., Shyr, Y., West, J., Studholme, C. and Maurer, C.R. Visual assessment of the accuracy of ret-rospective registration of MR and CT images of the brain. IEEE Transactions on Medical Imaging, 1998, 17(4): 571–585.

    Google Scholar 

  • Friston, K.J., Ashburner, J., Frith, C.D., Poline, J.B., Heather, J.D. and Frackowiak, R.S.J. Spatial registration and normaliza-tion of images. Human Brain Mapping, 1995, 2: 165–189.

    Google Scholar 

  • Friston, K.J., Williams, S., Howard, R., Frackowiak, R.S.J. and Turner, R. Movement-related effects in fMRI time series. MRM, 1996, 35: 346–355.

    Google Scholar 

  • Grootoonk, S., Hutton, C., Ashburner, J., Howseman, A.M., Josephs, O., Rees, G., Friston, K.J. and Turner, R. Characterization and correction of interpolation effects in the realignment of fMRI time series, Neuroimage, 2000, 11: 49–57.

    Google Scholar 

  • Holden, M., Hill, D.L.G., Denton, E.R.E., Jarosz, J.M., Cox, T.C.S., Rohlfing, T., Goodey, J. and Hawkes, D.J. Voxel Similarity measures for 3-D serial MRbrain image registration. IEEE Transactions on Medical Imaging, 2000, 19(2): 94–102.

    Google Scholar 

  • Lemieux, L., Wieshmann, U.C., Moran, N.F., Fish, D.R. and Shoovon, S.D. The detection and significance of subtle changes in mixed signal brain lesions by serial MRI scan matching and spatial normalization. Medical Image Analysis, 1998, 2(3): 227–242.

    Google Scholar 

  • Maes, F., Collignon, A., Vandermeulen, D., Marchal, G. and Suetens, P. Multimodality image registration by maximi-zation of mutual information. IEEE Transactions on Medical Imaging, 1997, 16(2): 187–198.

    Google Scholar 

  • Maurer, C.R., Fitzpatrick, J.M., Wang, M.Y., Galloway, R.L., Maciunas, R.J. and Allen, G.S. Registration of head volume images using implantable markers. IEEE Transactions on Biomedical Imaging, 1997, 16(4): 447–462.

    Google Scholar 

  • Meyer, C.R., Boes, J.L., Kim, B., Bland, P.H., Zasadny, K.R., Kison, P.V., Koral, K., Frey, K.A. and Wahl, R.L. Demonstration of accuracy and clinical versatility of mutual infor-mation for automatic multimodality image fusion using affine and thin-plate spline warped geometric deformations. Medical Image Analysis, 1997, 1(3): 195–206.

    Google Scholar 

  • Pellizzari, C.A., Chen, G.T.Y., Spelbring, D.R., Weichselbaum, R.R. and Chin-Tu, C. Accurate three-dimensional registra-tion of CT, PET, and/or MRimages of the brain. Journal of Computer Assisted Tomography, 1989, 13: 20–26.

    Google Scholar 

  • Studholme, C., Hill, D.L.G. and Hawkes, D.J. Automated 3D registration of MRand CT images of the head. Medical Image Analysis, 1996, 1: 163–175.

    Google Scholar 

  • Studholme, C., Hill, D.L.G. and Hawkes, D.J. Automated three-dimentional registration of magnetic resonance and positron emission tomography brain images by multiresolution optimization of voxel similarity measures. Medical Phys., 1997, 24(1): 25–35.

    Google Scholar 

  • Thevenaz, P. and Unser, M. An efficient mutual information optimizer for multiresolution image registration. Proc. of the 1998 IEEE Int. Conf. On Image processing, (ICIP 98), Chicago, 1998: 833–837.

  • Thevenaz, P. and Unser, M. Optimization of mutual informa-tion for multiresolution image registration. IEEE Transactions on Image Processing, 2000, 9(12): 2083–2099.

    Google Scholar 

  • Thompson, P. and Toga, W. Elastic image registration and pa-thology detection. In: I. Bankman (Ed.), Handbook of Medical Imaging Processing. Academic Press, 2000.

  • Toga, A. and Thompson, P. The role of image registration in brain mapping. Image and Vision Computing Journal, 2000.

  • Viola, P. and Wells III, W.M. Alignment by maximization of mutual information. Proc. IEEE Int. Conf. on Computer Vision, Boston, MA, 1995: 15–23.

  • Wang, M.Y., Maurer, C.R., Fitzpatrick, J.M. and Maciunas, R.J. An automatic technique for finding and localizing exter-nally attached markers in CT and MR volume images of the head. IEEE Transactions on Biomedical Engineering, 1996, 43(6): 627–637.

    Google Scholar 

  • Wells III, W. M., Viola, P., Atsumi, H., Nakajima, S. and Kikinis, R. Multimodal volume registration by maximization of mutual information. Medical Image Analysis, 1996, 1(1): 35–51.

    Google Scholar 

  • West, J. et al. Comparison and evaluation of retrospective intermodality brain image registration techniques. Journal of Computer Assisted Tomography, 1997, 21: 554–566.

    Google Scholar 

  • West, J., Fitzpatrick, J.M., Wang, M.Y., Dawant, B.M., Maurer, C.R., Kessler, R.M. and Maciunas, R.J. Retrospective intermodality registration techniques for images of the head: Surface-based versus Volume-based. IEEE Transactions on Medical Imaging, 1999, 18(2): 144–149.

    Google Scholar 

  • Woods, R.P., Cherry, S.R. and Mazziotta, J.C. Rapid automated algorithm for aligning and reslicing PET images. Journal of Computer Assisted Tomography, 1992, 16: 620–633.

    Google Scholar 

  • Woods, R.P., Mazziotta, J.C. and Cherry, S.R. MRI-PET regis-tration with automated algorithm. Journal of Computer Assisted Tomography, 1993, 17: 536–546.

    Google Scholar 

  • Woods, R.P. Grafton, S.T., Holmes, C.J., Cherry, S.R. and Mazziotta, J.C. Automated image registration: I. General methods and intrasubject, intramodality validation. J. Comput. Assist. Tomogr., 1998a, 22(1): 139–152.

    Google Scholar 

  • Woods, R.P., Grafton, S.T., Watson, J.D., Sicotte, N.L. and Mazziotta, J.C. Automated image registration: II. Intersubject validation of linear and nonlinear models. J. Comput. Assist. Tomogr., 1998b, 22(1): 153–165.

    Google Scholar 

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Authors and Affiliations

  1. Department of Computer and Information Science,, New Jersey Institute of Technology,, University Heights GITC Building,, Newark,, New Jersey,, USA

    Carlo Ciulla & Fadi P. Deek

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  1. Carlo Ciulla
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  2. Fadi P. Deek
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Ciulla, C., Deek, F.P. Development and Characterization of an Automatic Technique for the Alignment of fMRI Time Series. Brain Topogr 14, 41–56 (2001). https://doi.org/10.1023/A:1012515822675

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