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Groupwise Registration of Brain Images for Establishing Accurate Spatial Correspondence of Brain Structures

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Shape Analysis in Medical Image Analysis

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 14))

Abstract

For establishing accurate spatial correspondence of brain structures among different subjects, many groupwise image registration methods have been proposed to register brain images taken from different subjects onto a common space. Except the congealing method, most groupwise image registration methods achieve the image registration by registering images to a template image using pairwise image registration algorithms. For these groupwise image registration methods built upon pairwise image registration, the key points are template determination, registration path identification, and pairwise image registration. Focusing on the graph-based groupwise image registration methods due to their high computation efficiency and accuracy, this chapter introduces briefly the congealing method and groupwise image registration methods with different strategies for template determination and registration path identification. To demonstrate the strength of state-of-the-art groupwise image registration methods, a quantitative comparison study has also been presented for representative graph-based groupwise image registration methods based on two publicly available 3D MR brain image datasets.

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References

  1. Chau W, McIntosh AR (2005) The Talairach coordinate of a point in the MNI space: how to interpret it. Neuroimage 25:408–416

    Article  Google Scholar 

  2. Ashburner J (2007) A fast diffeomorphic image registration algorithm. Neuroimage 38:95–113

    Article  Google Scholar 

  3. Jia H, Wu G, Wang Q, Wang Y, Kim M, Shen D (2012) Directed graph based image registration. Comput Med Imaging Graph 36:139–151

    Article  Google Scholar 

  4. Jia H, Yap PT, Wu G, Wang Q, Shen D (2011) Intermediate templates guided groupwise registration of diffusion tensor images. Neuroimage 54:928–939

    Article  Google Scholar 

  5. Park H, Bland PH, Hero AO 3rd, Meyer CR (2005) Least biased target selection in probabilistic atlas construction. MICCAI 8:419–426

    Google Scholar 

  6. Joshi S, Davis B, Jomier M, Gerig G (2004) Unbiased diffeomorphic atlas construction for computational anatomy. Neuroimage 23:S151–S160

    Article  Google Scholar 

  7. Wu GR, Jia HJ, Wang Q, Shen DG (2011) SharpMean: groupwise registration guided by sharp mean image and tree-based registration. Neuroimage 56:1968–1981

    Article  Google Scholar 

  8. Hamm J, Davatzikos C, Verma R (2009) Efficient large deformation registration via geodesics on a learned manifold of images. MICCAI 12:680–687

    Google Scholar 

  9. Ye D, Hamm J, Kwon D, Davatzikos C, Pohl K (2012) Regional manifold learning for deformable registration of brain MR images. In: MICCAI 2012, vol. 7512, Springer, Berlin Heidelberg, pp 131–138

    Google Scholar 

  10. Wang Q, Chen L, Yap PT, Wu G, Shen D (2010) Groupwise registration based on hierarchical image clustering and atlas synthesis. Hum Brain Mapp 31:1128–1140

    Article  Google Scholar 

  11. Tang Z, Jiang D, Fan Y (2013) Image registration based on dynamic directed graphs with group-wise image similarity. In: 2013 international symposium on biomedical imaging: from nano to macro, San Francisco, CA, USA

    Google Scholar 

  12. Balci SK, Golland P, Shenton M, Wells WM (2007) Free-form B-spline deformation model for groupwise registration. MICCAI 10:23–30

    Google Scholar 

  13. Donoghue CR, Rao A, Pizarro L, Bull AMJ, Rueckert D (2012) Fast and accurate global geodesic registrations using knee MRI from the Osteoarthritis Initiative. In: 2012 IEEE computer society conference on computer vision and pattern recognition workshops, Providence, RI, USA, pp 50–57

    Google Scholar 

  14. Tang SY, Fan Y, Wu GR, Kim M, Shen DG (2009) RABBIT: rapid alignment of brains by building intermediate templates. Neuroimage 47:1277–1287

    Article  Google Scholar 

  15. Zitova B, Flusser J (2003) Image registration methods: a survey. Image Vis Comput 21:977–1000

    Article  Google Scholar 

  16. Stockman G, Kopstein S, Benett S (1982) Matching images to models for registration and object detection via clustering. IEEE Trans Pattern Anal Mach Intell 4:229–241

    Article  Google Scholar 

  17. Bhattacharya D, Sinha S (1997) Invariance of stereo images via the theory of complex moments. Pattern Recogn 30:1373–1386

    Article  Google Scholar 

  18. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60:91–110

    Article  Google Scholar 

  19. Zhuang XH, Arridge S, Hawkes DJ, Ourselin S (2011) A nonrigid registration framework using spatially encoded mutual information and free-form deformations. IEEE Trans Med Imaging 30:1819–1828

    Article  Google Scholar 

  20. Rueckert D, Sonoda LI, Hayes C, Hill DLG, Leach MO, Hawkes DJ (1999) Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans Med Imaging 18:712–721

    Article  Google Scholar 

  21. Klein A, Andersson J, Ardekani BA, Ashburner J, Avants B, Chiang MC, Christensen GE, Collins DL, Gee J, Hellier P, Song JH, Jenkinson M, Lepage C, Rueckert D, Thompson P, Vercauteren T, Woods RP, Mann JJ, Parsey RV (2009) Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage 46:786–802

    Article  Google Scholar 

  22. Thirion JP (1998) Image matching as a diffusion process: an analogy with Maxwell’s demons. Med Image Anal 2:243–260

    Article  Google Scholar 

  23. Barron JL, Fleet DJ, Beauchemin SS (1994) Performance of optical-flow techniques. Int J Comput Vision 12:43–77

    Article  Google Scholar 

  24. Bloy L, Verma R (2010) Demons registration of high angular resolution diffusion images. In: 2010 7th IEEE international symposium on biomedical imaging: from nano to macro, pp 1013–1016

    Google Scholar 

  25. Cahill ND (2012) Motion coherent image registration and demons: practical handling of deformation boundaries. Medical imaging 2012: image processing, vol 8314

    Google Scholar 

  26. Cahill ND, Noble JA, Hawkes DJ (2009) Demons algorithms for fluid and curvature registration. In: 2009 IEEE international symposium on biomedical imaging: from nano to macro, vols 1 and 2, pp 730–733

    Google Scholar 

  27. Cahill ND, Noble JA, Hawkes DJ (2009) A demons algorithm for image registration with locally adaptive regularization. In: Proceedings of medical image computing and computer-assisted intervention—MICCAI 2009, Pt I, vol 5761, pp 574–581

    Google Scholar 

  28. Cifor A, Risser L, Chung D, Anderson EM, Schnabel JA (2012) Hybrid feature-based log-demons registration for tumour tracking in 2-D liver ultrasound images. In: 2012 9th IEEE international symposium on biomedical imaging (Isbi), pp 724–727

    Google Scholar 

  29. Ebrahimi M, Martel AL (2009) Image registration under varying illumination: hyper-demons algorithm. In: Proceedings of energy minimization methods in computer vision and pattern recognition, vol 5681, pp 303–316

    Google Scholar 

  30. Forsberg D, Rathi Y, Bouix S, Wassermann D, Knutsson H, Westin CF (2011) Improving registration using multi-channel diffeomorphic demons combined with certainty maps. Multimodal Brain Image Anal 7012:19–26

    Article  Google Scholar 

  31. Freiman M, Voss SD, Warfield SK (2011) Demons registration with local affine adaptive regularization: application to registration of abdominal structures. In: 2011 8th IEEE international symposium on biomedical imaging: from nano to macro, pp 1219–1222

    Google Scholar 

  32. Gu X, Jia X, Dong B, Gautier Q, Jiang S (2011) A contour-guided demons deformable image registration algorithm for adaptive radiotherapy. Int J Radiat Oncol Biol Phys 81:S803–S804

    Article  Google Scholar 

  33. Gu XJ, Pan H, Liang Y, Castillo R, Yang DS, Choi DJ, Castillo E, Majumdar A, Guerrero T, Jiang SB (2010) Implementation and evaluation of various demons deformable image registration algorithms on a GPU. Phys Med Biol 55:207–219

    Article  Google Scholar 

  34. Guo YJ, Cheng WH, Lu CC (2007) Non-rigid mammogram registration using demons algorithm: preliminary results. In: Proceedings of the ninth iasted international conference on signal and image processing, pp 437–442

    Google Scholar 

  35. Hub M, Kessler ML, Karger CP (2010) B-spline registration versus demons algorithm—a quantitative comparison of accuracy and invertibility based on artificially created test cases for the lung. World congress on medical physics and biomedical engineering, vol 25, Pt 4: image processing, biosignal processing, modelling and simulation. Biomechanics 25:790–792

    Google Scholar 

  36. Jin S, Li DW, Wang HJ, Yin Y (2013) Registration of PET and CT images based on multiresolution gradient of mutual information demons algorithm for positioning esophageal cancer patients. J Appl Clin Med Phys 14:50–61

    Google Scholar 

  37. Li DW, Yin Y (2012) Deformable registration using multi-resolution demons algorithm for 4DCT. Med Phys 39:3672–3673

    Article  Google Scholar 

  38. Li W, Ibanez L, Andreasen NC, Magnotta VA (2011) The effectiveness of geometry features on multi-resolution diffeomorphic demons registration in the implementation of human cortex surface parcellation. In: 2011 8th IEEE international symposium on biomedical imaging: from nano to macro, pp 586–589

    Google Scholar 

  39. Lin XB, Qiu TS, Nicolier F, Ruan S (2008) An improved method of "demons" non-rigid image registration algorithm. In: Proceedings of Icsp, (2008) 9th international conference on signal processing, vols 1–5, pp 1091–1094

    Google Scholar 

  40. Lu C, Mandal M (2010) Improved demons technique with orthogonal gradient information for medical image registration. Ieice Trans Inf Syst E93d:3414–3417

    Google Scholar 

  41. Lu C, Mandal M (2010) Improved image registration technique based on demons and symmetric orthogonal gradient information. In: 2010 international conference on signal processing and communications (Spcom)

    Google Scholar 

  42. Lu H, Reyes M, Serifovic A, Weber S, Sakurai Y, Yamagata H, Cattin PC (2010) multi-modal diffeomorphic demons registration based on point-wise mutual information. In: 2010 7th IEEE international symposium on biomedical imaging: from nano to macro, pp 372–375

    Google Scholar 

  43. Lu HX, Cattin PC, Reyes M (2010) A hybrid multimodal non-rigid registration of MR images based on diffeomorphic demons. In: 2010 annual international conference of the IEEE engineering in medicine and biology society (Embc), pp 5951–5954

    Google Scholar 

  44. Mansi T, Pennec X, Sermesant M, Delingette H, Ayache N (2011) iLogDemons: a demons-based registration algorithm for tracking incompressible elastic biological tissues. Int J Comput Vis 92:92–111

    Article  Google Scholar 

  45. Muyan-Ozcelik P, Owens JD, Xia JY, Samant SS (2008) Fast deformable registration on the GPU: a CUDA implementation of demons. Proceedings of the International Conference on Computational Sciences and Its Applications, pp 223–233

    Google Scholar 

  46. Nithiananthan S, Brock KK, Daly MJ, Chan H, Irish JC, Siewerdsen JH (2009) Demons deformable registration for CBCT-guided procedures in the head and neck: convergence and accuracy. Med Phys 36:4755–4764

    Article  Google Scholar 

  47. Nithiananthan S, Brock KK, Daly MJ, Chan H, Irish JC, Siewerdsen JH (2010) Demons deformable registration for cone-beam CT guidance: registration of pre- and intra-operative images. Medical imaging 2010: visualization, image-guided procedures, and modeling, vol 7625

    Google Scholar 

  48. Nithiananthan S, Mirota D, Uneri A, Schafer S, Otake Y, Stayman JW, Siewerdsen JH (2011) Incorporating tissue excision in deformable image registration: a modified demons algorithm for cone-beam CT-guided surgery. Medical imaging 2011: visualization, image-guided procedures, and modeling, vol 7964

    Google Scholar 

  49. Nithiananthan S, Schafer S, Mirota DJ, Stayman JW, Zbijewski W, Reh DD, Gallia GL, Siewerdsen JH (2012) Extra-dimensional demons: a method for incorporating missing tissue in deformable image registration. Med Phys 39:5718–5731

    Article  Google Scholar 

  50. Nithiananthan S, Schafer S, Uneri A, Mirota DJ, Stayman JW, Zbijewski W, Brock KK, Daly MJ, Chan H, Irish JC, Siewerdsen JH (2011) Demons deformable registration of CT and cone-beam CT using an iterative intensity matching approach. Med Phys 38:1785–1798

    Article  Google Scholar 

  51. Peyrat JM, Delingette H, Sermesant M, Pennec X, Xu CY, Ayache N (2008) Registration of 4D time-series of cardiac images with multichannel diffeomorphic demons. In: Proceedings of medical image computing and computer-assisted intervention—MICCAI 2008, Pt Ii, vol 5242, pp 972–979

    Google Scholar 

  52. Peyrat JM, Delingette H, Sermesant M, Xu CY, Ayache N (2010) Registration of 4D cardiac CT sequences under trajectory constraints with multichannel diffeomorphic demons. IEEE Trans Med Imaging 29:1351–1368

    Article  Google Scholar 

  53. Pheiffer TS, Ou JJ, Miga MI (2010) Automatic generation of boundary conditions using Demons non-rigid image registration for use in 3D modality-independent elastography. Medical imaging 2010: visualization, image-guided procedures, and modeling, vol 7625

    Google Scholar 

  54. Pheiffer TS, Ou JJ, Ong RE, Miga MI (2011) Automatic generation of boundary conditions using demons nonrigid image registration for use in 3-D modality-independent elastography. IEEE Trans Biomed Eng 58:2607–2616

    Article  Google Scholar 

  55. Seiler, C., Pennec, X., Reyes, M.: Geometry-Aware Multiscale Image Registration via OBBTree-Based Polyaffine Log-Demons. Medical Image Computing and Computer-Assisted Intervention (Miccai 2011), Pt Ii 6892, 631–638 (2011).

    Google Scholar 

  56. Seiler, C., Pennec, X., Ritacco, L., Reyes, M.: Femur Specific Polyaffine Model to Regularize the Log-Domain Demons Registration. Medical Imaging 2011: Image Processing 7962, (2011).

    Google Scholar 

  57. Sharp GC, Kandasamy N, Singh H, Folkert M (2007) GPU-based streaming architectures for fast cone-beam CT image reconstruction and demons deformable registration. Phys Med Biol 52:5771–5783

    Article  Google Scholar 

  58. Shen JK, Matuszewski BJ, Shark LK, Skalski A, Zielinski T, Moore CJ (2008) Deformable image registration—a critical evaluation: demons, B-spline FFD and spring mass system. In: Proceedings medivis, (2008) fifth international conference biomedical visualization—information visualization in medical and biomedical informatics, pp 77–82

    Google Scholar 

  59. Siless V, Guevara P, Pennec X, Fillard P (2011) Joint T1 and brain fiber diffeomorphic registration using the demons. Multimodal Brain Image Anal 7012:10–18

    Article  Google Scholar 

  60. Sosa-Cabrera D, Tristan-Vega A, Vegas-Sanchez-Ferrero G, Gonzalez-Fernandez J, Gomez-Deniz L, Alberla-Lopez C, Ruiz-Alzola J (2008) A new approach to elastography using a modified demons registration algorithm—art. no. 69200X. Medical imaging 2008: ultrasonic imaging and signal processing, vol 6920, pp X9200–X9200

    Google Scholar 

  61. Suh JW, Kwon OK, Scheinost D, Sinusas AJ, Cline GW, Papademetris X (2011) Whole body nonrigid Ct-Pet registration using weighted demons. In: 2011 8th IEEE international symposium on biomedical imaging: from nano to macro, pp 1223–1226

    Google Scholar 

  62. Tristan-Vega A, Vegas-Sanchez-Ferrero G, Aja-Fernandez S (2008) Local similarity measures for demons-like registration algorithms. In: 2008 IEEE international symposium on biomedical imaging: from nano to macro, vols 1–4, pp 1087–1090

    Google Scholar 

  63. Vercauteren T, Pennec X, Malis E, Perchant A, Ayache N (2007) Insight into efficient image registration techniques and the demons algorithm. In: Proceedings information processing in medical imaging, vol 4584, pp 495–506

    Google Scholar 

  64. Vercauteren T, Pennec X, Perchant A, Ayache N (2008) Symmetric log-domain diffeomorphic registration: a demons-based approach. In: Proceedings medical image computing and computer-assisted intervention—MICCAI 2008, Pt I, vol 5241, pp 754–761

    Google Scholar 

  65. Vercauteren T, Pennec X, Perchant A, Ayache N (2009) Diffeomorphic demons: efficient non-parametric image registration. Neuroimage 45:61–72

    Article  Google Scholar 

  66. Vereauteren T, Pennec X, Perchant A, Ayache N (2007) Non-parametric diffeomorphic image registration with the demons algorithm. In: Proceedings medical image computing and computer-assisted intervention—MICCAI 2007, Pt 2, vol 4792, pp 319–326

    Google Scholar 

  67. Wang H, Dong L, O’Daniel J, Mohan R, Garden AS, Ang KK, Kuban DA, Bonnen M, Chang JY, Cheung R (2005) Validation of an accelerated ’demons’ algorithm for deformable image registration in radiation therapy. Phys Med Biol 50:2887–2905

    Article  Google Scholar 

  68. Yang D, Li HD (2009) A probabilistic demons algorithm for texture-rich image registration. In: 2009 16th IEEE international conference on image processing, vols 1–6, pp 161–164

    Google Scholar 

  69. Yeo BTT, Sabuncu M, Vercauteren T, Ayache N, Fischl B, Golland P (2008) Spherical demons: fast surface registration. In: Proceedings of medical image computing and computer-assisted intervention—MICCAI 2008, Pt I, vol 5241, pp 745–753

    Google Scholar 

  70. Yeo BTT, Sabuncu MR, Vercauteren T, Ayache N, Fischl B, Golland P (2010) Spherical demons: fast diffeomorphic landmark-free surface registration. IEEE Trans Med Imaging 29:650–668

    Article  Google Scholar 

  71. Jia H, Wu G, Wang Q, Shen D (2010) ABSORB: atlas building by self-organized registration and bundling. Neuroimage 51:1057–1070

    Article  Google Scholar 

  72. Miller EG, Matsakis NE, Viola PA (2000) Learning from one example through shared densities on transforms. In: Proceedings of IEEE conference on computer vision and pattern recognition, vol 1, pp 464–471

    Google Scholar 

  73. Zollei L, Learned-Miller E, Grimson E, Wells W (2005) Efficient population registration of 3D data. Lect Notes Comput Sc 3765:291–301

    Article  Google Scholar 

  74. Jiang D, Du Y, Cheng H, Jiang T, Fan Y (2013) Groupwise spatial normalization of fMRI data based on multi-range functional connectivity patterns. Neuroimage 82C:355–372

    Article  Google Scholar 

  75. Seghers D, D’Agostino E, Maes F, Vandermeulen D, Suetens P (2004) Construction of a brain template from MR images using state-of-the-art registration and segmentation techniques. In: Proceedings od medical image computing and computer-assisted intervention—MICCAI 2004, Pt 1, vol 3216, pp 696–703

    Google Scholar 

  76. Dijkstra EW (1959) A note on two problems in connexion with graphs. Numerische Mathematik 1:269–271

    Article  MATH  MathSciNet  Google Scholar 

  77. Tenenbaum JB, de Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290, 2319–2323

    Google Scholar 

  78. Prim RC (1957) Shortest connection networks and some generalizations. Bell Syst Tech J 36:1389–1401

    Article  Google Scholar 

  79. Kruskal JB (1956) On the shortest spanning subtree of a graph and the traveling salesman problem. Proc Am Math Soc 7:3

    Article  MathSciNet  Google Scholar 

  80. Cheng B, Yang JC, Yan SC, Fu Y, Huang TS (2010) Learning with l(1)-graph for image analysis. IEEE Trans Image Process 19:858–866

    Article  MathSciNet  Google Scholar 

  81. Donoho DL (2006) For most large underdetermined systems of equations, the minimal l(1)-norm near-solution approximates the sparsest near-solution. Commun Pure Appl Math 59:907–934

    Article  MathSciNet  Google Scholar 

  82. Shattuck DW, Mirza M, Adisetiyo V, Hojatkashani C, Salamon G, Narr KL, Poldrack RA, Bilder RM, Toga AW (2008) Construction of a 3D probabilistic atlas of human cortical structures. Neuroimage 39:1064–1080

    Article  Google Scholar 

  83. Christensen GE, Geng X, Kuhl JG, Bruss J, Grabowski TJ, Pirwani IA, Vannier MW, Allen JS, Damasio H (2006) Introduction to the non-rigid image registration evaluation project (NIREP). In: Proceedings of biomedical image registration, vol 4057, pp 128–135

    Google Scholar 

  84. Jenkinson M, Smith S (2001) A global optimisation method for robust affine registration of brain images. Med Image Anal 5:143–156

    Article  Google Scholar 

  85. Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26:297–302

    Article  Google Scholar 

  86. Conroy BR, Singer BD, Guntupalli JS, Ramadge PJ, Haxby JV (2013) Inter-subject alignment of human cortical anatomy using functional connectivity. Neuroimage 81:400–411

    Article  Google Scholar 

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Acknowledgments

This study was partially supported by the National Basic Research Program of China (973 Program) 2011CB707801, the National High Technology Research and Development Program of China (863 Program) 2012AA011603, the National Science Foundation of China (Grant No. 30970770, 91132707, 81271514, and 81261120419), and the Hundred Talents Program of the Chinese Academy of Sciences.

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  1. Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Zhenyu Tang & Yong Fan

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  1. Zhenyu Tang
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Correspondence to Yong Fan .

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  1. GE Healthcare and University of Western Ontario, London, Ontario, Canada

    Shuo Li

  2. Departamento de Engenharia Mecânica, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

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Tang, Z., Fan, Y. (2014). Groupwise Registration of Brain Images for Establishing Accurate Spatial Correspondence of Brain Structures. In: Li, S., Tavares, J. (eds) Shape Analysis in Medical Image Analysis. Lecture Notes in Computational Vision and Biomechanics, vol 14. Springer, Cham. https://doi.org/10.1007/978-3-319-03813-1_7

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