Abstract
This paper reports on performance assessment of an algorithm developed to align functional Magnetic Resonance Image (fMRI) time series. The algorithm is based on the assumption that the human brain is subject to rigid-body motion and has been devised by pipelining fiducial markers and tensor based registration methodologies. Feature extraction is performed on each fMRI volume to determine tensors of inertia and gradient image of the brain. A head coordinate system is determined on the basis of three fiducial markers found automatically at the head boundary by means of the tensors and is used to compute a point-based rigid matching transformation. Intensity correction is performed with sub-voxel accuracy by trilinear interpolation. Performance of the algorithm was preliminarily assessed by fMR brain images in which controlled motion has been simulated. Further experimentation has been conducted with real fMRI time series. Rigid-body transformations were retrieved automatically and the value of motion parameters compared to those obtained with the Statistical Parametric Mapping (SPM99) and the Automatic Image Registration (AIR 3.08). Results indicate that the algorithm offers sub-voxel accuracy in performing both misalignment and intensity correction of fMRI time series.
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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.
Andersson, J.L.R., Hutton, C., Ashburner, J., Turner, R. and Friston, K. Modeling geometric deformations in EPI time series. Neuroimage, 2001, 13: 903–919.
Arun, K.S., Huang, T.S. and Blostein, S.D. Least-square fitting of two 3-D point sets. IEEE Trans. Pattern Anal. Machine Intell., 1987, PAMI-9: 698–700.
Audette, M.A., Ferrie, F.P. and Peters, T.M. An algorithmic overview of surface registration techniques for medical imaging. Medical Image Analysis, 2000, 4: 201–217.
Biswal, B.B. and Hyde, J.S. Contour-based registration technique to differentiate between task-activated and head motion-induced signal variations in fMRI. MRM, 1997, 38: 470–476.
Buckner, R.L., Snyder, A.Z., Sanders, A.L., Raichle, M.E. and Morris, J.C. Functional bra in imaging of young, nondemented and demented older adults. Journal of Cognitive Neuroscience, 2000, 12Supplement 2: 24–34.
Castleman, K.R. Digital Image Processing. Prentice Hall, Englewood Cliffs, New Jersey, 1996, 116–119.
Ciulla, C., Takeda, T. and Endo, H. MEG characterization of spontaneous alpha rhythm in the human brain. Brain Topography, 1999, 11(3): 211–222.
Ciulla, C., Takeda, T., Endo, H., Kumagai, T., Morabito, M. and Xiao, R. MEG measurements of 40Hz auditory evoked response in human brain. In: C. Aine, Y. Okada, G. Stroink, S. Swithenby and C. Wood (Eds), Biomag96: Proceedings of the Tenth International Conference on Biomagnetism, Springer-Verlag, 2000.
Ciulla, C. and Deek, F.P. Development and characterization of an automatic technique for the alignment of fMRI time series. Brain Topography, 2001, 14(1): 41–56.
Ciulla, C. Development and characterization of methodology and technology for the alignment of fMRI time series. PhD Dissertation, NJIT-New Jersey Institute of Technology, Newark, NJ, USA, May 2002.
Collignon, A., Maes, F., Dalaere, D., Vandermeulen, D., Suetens, P. and Marchal, G. Automated multi-modality image registration based on information theory. In: Y. Bizais, C. Barillot and R. Di Paola (Eds.), Information Processing in Medical Imaging. Kluwer Academic, Dordrecht, The Netherlands, 1995a: 263–274.
Collignon, A., Vandermeulen, D., Suetens, P. and Marchal, G. Automated multimodality image registration based on information theory. Computer Imaging and Vision, 1995b, 3: 263–274.
Cox, R.W. AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computer and Biomedical Research, 1996, 29: 162–173.
Demmel, J.W. Applied numerical linear algebra. SIAM. Society for Industrial and Applied Mathematics. 1997: 195–264.
Eddy, W.E., Fitzgerald, M. and Noll, D.C. Improved image registration by using fourier interpolation. MRM, 1996, 36: 923–931.
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.
Fitzpatrick, J.M, West, J.B. and Maurer, C.R. Jr. Predicting error in rigid-body point-based registration. Special Issue of IEEE Trans. Med. Imaging on Image Registration, 1998, 17: 694–702.
Fitzpatrick, J.M., Hill, D.L.G. and Maurer, C.R. Image registration. In: M. Sonka and J.M. Fitzpatrick (Eds.), Handbook of Medical Imaging, Vol. 2: Medical Imaging Processing and Analysis. SPIE Vol. PM80, 2000.
Fitzpatrick, J.M. and West, J.B. The distribution of terget registration error in rigid-body point-based registration. IEEE Transactions on Medical Imaging, 2001, 20(9): 917–927.
Freire, L. and Mangin, J.F. Motion correction algorithms may create spurious brain activations in the absence of subject motion. Neuroimage, 2001, 14: 709–722.
Friston, K.J., Ashburner, J., Frith, C.D., Poline, J.B., Heather, J.D. and Frackowiak, R.S.J. Spatial registration and normalization of images. Hum. Brain Mapp., 1995, 2: 165–189.
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.
Grachev, I.D., Berdichevsky, D., Raunch, S.L., Heckers, S., Kennedy, D.N., Cavines, V.S. and Alpert, N.M. A method for assessing the accuracy of intersubject registration of the human brain using anatomic landmarks. Neuroimage, 1999, 9: 250–268.
Grootoonk, S., Hutton, C., Ashburner, J., Howseman, A.M., Josephs, O., Rees, G., Friston, K.J. and Turner, R. Characterizationandcorrection of interpolation effects in the realignment of fMRI time series. Neuroimage, 2000, 11: 49–57.
Hill, D.L.G., Hawkes, D., Crossman, J.E., Gleeson, M.J., Cox, T.C.S., Bracey, E.C.M.L., Strong, A.J. and Graves, P. Registration of MR and CT images for skull base surgery using point-like anatomical features. British Journal of Radiology, 1991, 64: 1030–1035.
Hill, D.L.G., Hawkes, D.J., Harrison, N. and Ruff, C.F. A strategy for automated multimodal registration incorporating anatomical knowledge and imagery characteristics. In: H.H. Barret and A.F. Gmitro (Eds.), Information Processing in Medical Imaging. Springer-Verlag, Berlin, 1993: 182–196.
Hill, D.L.G. and Hawkes, D.J. Voxel similarity measures for automated image registration. Visualization in Biomedical Computing. vol. Proc. SPIE 2359, 1994: 205–216.
Hogan, R.E., Cook, M.J., Kilpatrick, C.J., Binns, D.W., Desmond, P.M. and Morris, K. Accuracy of coregistration of single-photon emission CT with MR via a brain surface matching technique. A. J. Neuroradiol., 1995, 17: 793–797.
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 serialMRbrain image registration. IEEE Transactions on Medical Imaging, 2000, 19: 94–102.
Jiang, H., Holton, K.S. and Robb, R.A. Image registration of multimodality 3-D medical images by chamfer matching. Biomedical Image Processing and Three-Dimensional Microscopy. vol. Proc. SPIE 1660, 1992a: 356–366.
Jiang, H., Robb, R.A. and Holton, K.S. A new approach to 3-D registration of multimodality medical images by surface matching. Visualization in Biomedical Computing. vol. Proc. SPIE 1808, 1992b: 196–213.
Kruggel, F. and Yves von Cramen, D. Alignment of magnetic resonance brain datasets with the stereotactical coordinate system. Medical Image Analysis, 1999, 3(2): 175–185.
Kybic, J., Thevenaz, P. and Unser, M. Multiresolution spline warping for EPI registration. Proc. of the SPIE Conference on Mathematical Imaging: Wavelet Applications in Signal and Image Processing. VII, Denver, CO, USA, 1999, 3813: 571–579.
Lee, C.C., Jack, C.R. Jr., Grimm, R.C., Rossman, P.J., Felmlee, J.P., Ehman, R.L. and Riederer, S.J. Real-time adaptive motion correction in functional MRI. MRM, 1996, 36: 436–444.
Lee, C.C., Grimm, R.C., Manduca, A., Felmlee, J.P., Ehman, R.L., Riederer, S.J. and. Jack, C.R. Jr. A prospective approach to correct for inter-image head rotation in fMRI. MRM, 1998, 39: 234–243.
Lemoine, D., Liegeard, D., Lussot, E. and Barillot, C. Multimodal registration system for the fusion of MRI, CT, MEG, and 3D or stereotactic angiography data. Medical Imaging. Image Capture Formatting and Display. vol. Proc. SPIE, 1994: 46–56.
Maas, L.C., Frederick Blaise, deB. and Renshaw, P.F. Decoupled automated rotational and translational registration for functional MRI time series data: the DART registration algorithm. MRM, 1997, 37: 131–139.
Maes, F., Collignon, A., Vandermeulen, D., Marchal, G. and Suetens, P. Multimodality image registration by maximization of mutual information. IEEE Trans. Medical Imaging, 1997, 16(2): 187–198.
Maes, F., Vandermeulen, D. and Suetens, P. Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information. Medical Image Analysis, 1999, 3(4): 373–386.
Maguire, G.Q. Jr., Noz, M.E., Rusinek, H., Jaeger, J., Kramer, E.L., Sanger, J.J. and Smith, G. Graphics applied to medical image registration. IEEE Comput. Graph. Appl., 1991, 11: 20–29.
Maintz, J.B.A., van den Elsen, P.A. and Viergever, M.A. Comparison of feature-based matching of CT andMRbra in images. In: N. Ayache (Ed.), Computer Vision, Virtual Reality and Robotics in Medicine. Springer-Verlag, Berlin, 1995: 219–228.
Maintz, J.B.A., van den Elsen, P.A. and Viergever, M.A. Comparison of edge-based and ridge-based registration of CT andMRbrain images. Medical Image Analysis, 1996a, 1(2): 151–161.
Maintz, J.B.A., van den Elsen, P.A. and Viergever, M.A. Evaluation of ridge seeking operators for multimodality medical image matching. IEEE Trans. Pattern Anal. Mach. Intell., 1996b, 18: 353–365.
Maintz, J.B., van den Elsen, P.A. and Viergever, M.A. Registration of 3D medical images using simple morphological tools. In: J. Duncan and G. Gindi (Eds.), IPMI 97, Volume 1230 of Lecture Notes in Computer Science. Springer Verlag, 1997: 204–217.
Maintz, J.B. and Viergever, M.A. A survey of medical registration. Medical Image Analysis, 1998, 2(1): 1–36.
Malandain, G., Fernandez-Vidal, S. and Rocchisani, J.M. Rigid registration of 3-D objects by motion analysis. Proc. 12th Int. Conf. Pattern Recognition, 1994a: 579–581.
Malandain, G., Fernandez-Vidal, S. and Rocchisani, J.M. Improving registration of 3-D medical images using a mechnical based method. 3rd European Conference on Computer Vision (ECCV 94), 1994b: 131–136.
Malandain, G., Fernandez-Vidal, S. and Rocchisani, J.M. Physically based rigid registration of 3-D form objects:Application to medical imaging. Tech. Rep. 2453, INRIA, Sophia Antipolis Cedex France, 1995.
Maurer, C.R., Fitzpatrick, J.M., Wang, M.Y. and Maciunas, R.J. Estimation of localization accuracy for markers in multimodal volume images. Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., 1993, 15: 124–125.
Maurer, C.R., Aboutanos, G.B., Dawant, B.M., Gadamsetty, S., Margolin, R.A., Maciunas, R.J. and Fitzpatrick, J.M. Effect of geometrical distortion correction in MR on image registration accuracy. Medical Imaging. Image Processing. vol. Proc. SPIE 2167, 1994: 200–213.
Maurer, C.R., Aboutanos, G.B., Dawant, B.M., Margolin, R.A., Maciunas, R.J. and Fitzpatrick, J.M. Registration of CT and MR brain images using a combination of points and surfaces. Medical Imaging: Image Processing. vol. Proc. SPIE 2434, 1995: 109–123.
Maurer, C.R., Aboutanos, G.B., Dawant, B.M., Gadamsetty, S., Margolin, R.A., Maciunas, R.J. and Fitzpatrick, J.M. Effect of geometrical distortion correction in MR on image registration accuracy. J. Computer Assist. Tomogr., 1996, 20: 666–679.
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.
Ostuni, J.L., Santha, A.K.S., Ventaka, S.M., Weinberger, D.R., Levin, L.R. and Frank, J.A. Analysis of interpolation effects in the reslicing of functional MR images. Journal of Computer Assisted Tomography, 1997, 21(5): 803–810.
Pellizzari, C.A., Chen, G.T.Y., Spelbring, D.R., Weichselbaum, R.R. and Chin-Tu, C. Accurate three-dimensional registration of CT, PET, and/orMRimages of the brain. Journal of Computer Assisted Tomography, 1989, 13: 20–26.
Pennec, X., Ayache, N. and Thirion, J.P. Landmark-based registration using features identified through differential geometry. In: I.N. Bandman (Ed.), Handbook of Medical Imaging: Processing and Analysis, 2000: 499–513.
Rouet, J.M., Jacq, J.J. and Roux, C. Genetic algorithms for a robust 3-D MR-CT registration. IEEE Transactions on Information Technology in Biomedicine, 2000, 4: 126–136.
Studholme, C., Hill, D.L.G. and Hawkes, D.J. Automated 3D registration ofMRand CT images of the head. Medical Image Analysis, 1996, 1: 163–175.
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.
Toga, A.W. and Thompson, P. The role of image registration in brain mapping. Image and Vision Computing Journal, 2000.
Thompson, P. and Toga, A.W. Warping strategies for intersubject registration. In: I.N. Bankman (Ed.), Handbook of Medical Imaging: Processing and Analysis, 2000: 569–601.
Thevenaz, P., Ruttimann, U.E. and Unser, M. A pyramid approach to subpixel registration based on intensity. IEEE Transactions on Image Processing, 1998, 7(1): 27–41.
Thevenaz, P. and Unser, M. Optimization of mutual information for multiresolution image registration. IEEE Trans. on Image Processing, 2000, 9(12): 2083–2099.
van den Elsen, P.A., Pol, E.J.D., Sumanaweera, T.S., Hemler, P.F., Napel, S. and Adler, J.R. Grey value correlation techniques used for automatic matching of CT andMRbrain and spine images. Visualization in Biomedical Computing. vol. Proc. SPIE 2359, 1994: 227–237.
Wang, M.Y., Maurer, C.R., Fitzpatrick, J.M. and Maciunas, R.J. An automatic technique for finding and localizing externally attached markers in CT and MR volume images of the head. IEEE Transactions on Biomedical Engineering, 1996, 43(6): 627–637.
Wang, Y. and Staib, L.H. Physical model-based non-rigid registration incorporating statistical shape information. Medical Image Analysis, 2000, 4: 7–20.
Wells III, W.M., Viola, P. and Kikinis, R. Multi-modal volume registration by maximization of mutual information. Medical Robotics and Computer Assisted Surgery. Wiley-Liss, New York, 1995: 55–62.
Wells III, W. M., Viola, P., Atsumi, H., Nakajima, S. and Kikinis, R. Multimodal volume registration by maximization ofmutual information. Medical Image Analysis, 1996, 1(1): 35–51.
West, J. et al. Comparison and evaluation of retrospective intermodality bra in image registration techniques. Journal of Computer Assisted Tomography, 1997, 21: 554–566.
West, J. et al. Fiducial point placement and the accuracy of point-based, rigid body registration. Neurosurgery, 2001, 48: 810–817.
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.
Woods, R.P., Mazziotta, J.C. and Cherry, S.R. MRI-PET Registration with Automated Algorithm. Journal of Computer Assisted Tomography, 1993, 17: 536–546.
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.
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.
Woods, R.P. Validation of registration accuracy. In: I.N. Bankman (Eds.), Handbook of Medical Imaging: Processing and Analysis, 2000: 491–497.
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Ciulla, C., Deek, F.P. Performance Assessment of an Algorithm for the Alignment of fMRI Time Series. Brain Topogr 14, 313–332 (2002). https://doi.org/10.1023/A:1015756812054
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DOI: https://doi.org/10.1023/A:1015756812054