A spline-based non-linear diffeomorphism for multimodal prostate registration

Medical Image Analysis

Volumn 16, Issue 6, 2012, Pages 1259-1279

  Mitra, Jhimli ^a,b   Kato, Zoltan ^c   Martí, Robert ^b   Oliver, Arnau ^b   Lladó, Xavier ^b   Sidibé, Désiré ^a   Ghose, Soumya ^a,b   Vilanova, Joan C ^d   Comet, Josep ^e   Meriaudeau, Fabrice ^a  

^a UNIVERSITÉ DE BOURGOGNE   (France)

^b UNIVERSITY OF GIRONA   (Spain)

^c UNIVERSITY OF SZEGED   (Hungary)

^d Girona Magnetic Resonance Center   (Spain)

^e HOSPITAL JOSEP TRUETA   (Spain)

Author keywords

Multimodal images; Non linear registration; Prostate biopsy; Regularization; Thin plate splines

Indexed keywords

MULTI-MODAL IMAGE; NON-LINEAR REGISTRATION; PROSTATE BIOPSY; REGULARIZATION; THIN PLATE SPLINE;

FUNCTIONS; IMAGE SEGMENTATION; LINEAR EQUATIONS; MAGNETIC RESONANCE; MAGNETIC RESONANCE IMAGING; PORE PRESSURE;

UROLOGY;

ARTICLE; IMAGE ANALYSIS; IMAGE RECONSTRUCTION; IMAGING SYSTEM; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; PROSTATE; REGISTER; TRANSRECTAL ULTRASONOGRAPHY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; NONLINEAR DYNAMICS; PATTERN RECOGNITION, AUTOMATED; PROSTATIC NEOPLASMS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE; ULTRASONOGRAPHY;

EID: 84866118888     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2012.04.006     Document Type: Article

References (72)

1. Alterovitz, R., Goldberg, K., Kurhanewicz, J., Pouliot, J., Hsu, I.C., 2004. Image registration for prostate MR spectroscopy using biomechanical modeling and optimization of force and stiffness parameters. In: Proc. of 26th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBS), San Francisco, CA, USA.
2. Andriole G.L., Crawford E.D., Grubb R.L., Buys S.S., Chia D., Church T.R., Fouad M.N., Gelmann E.P., Reding D.J., Weissfeld J.L., Yokochi L.A., O'Brien B., Clapp J.D., Rathmell J.M., Riley T.L., Hayes R.B., Kramer B.S., Izmirlian G., Miller A.B., Pinsky P.F., Prorok P.C., Gohagan J.K., Berg C.D. Mortality results from a randomized prostate-cancer screening trial. The New England Journal of Medicine 2009, 360:1310-1319.
3. Avants B.B., Epstein C.L., Grossman M., Gee J.C. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Medical Image Analysis 2008, 12:26-41.
4. Bartlett M.S. Properties of sufficiency and statistical tests. Proceedings of the Royal Society of London, Series A: Mathematical and Physical Sciences 1937, 160:268-282.
5. Baumann, M., Mozer, P., Daanen, V., Troccaz, J., 2009. Prostate biopsy assistance system with gland deformation estimation for enhanced precision. In: Proc. of MICCAI, September, pp. 57-64.
6. Baumann M., Mozer P., Daanen V., Troccaz J. Prostate biopsy tracking with deformation estimation. Medical Image Analysis 2012, 16:562-576.
7. Belongie S., Malik J., Puzicha J. Shape matching and object recognition using shape contexts. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002, 24:509-522.
8. Besl P., McKay N. A method for registration of 3D shapes. IEEE Transactions on Pattern and Machine Intelligence (PAMI) 1992, 14:239-256.
9. Bhattacharyya A. On a measure of divergence between two statistical populations defined by their probability distribution. Bulletin of the Calcutta Mathematical Society 1943, 35:99-110.
10. Bogers H.A., Sedelaar J.P., Beerlage H.P., de la Rosette J.J., Debruyne F.M., Wijkstra H., Aarnink R.G. Contrast-enhanced three-dimensional power Doppler angiography of the human prostate: correlation with biopsy outcome. Urology 1999, 54:97-104.
11. du Bois d'Aische, A., Craene, M.D., Haker, S., Weisenfeld, N., Tempany, C., Macq, B., Warfield, S.K., 2004. Improved non-rigid registration of prostate MRI. In: Proc. of MICCAI, Rennes, France, pp. 845-852.
12. Bookstein F. Morphometric Tools for Landmark Data: Geometry and Biology 1991, Cambridge University Press, Cambridge, UK.
13. Carroll, P., Shinohara, K., 2010. Transrectal Ultrasound Guided Prostate Biopsy. Technical Report, Department of Urology, University of California, San Francisco. (accessed 30.12.10). http://urology.ucsf.edu/patientGuides.html.
14. Chen, T., Kim, S., Zhou, J., Metaxas, D., Rajagopal, G., Yue, N., 2009. 3D meshless prostate segmentation and registration in image guided radiotherapy. In: Proc. of MICCAI, London, UK, pp. 43-50.
15. Cheung M.R., Krishnan K. Interactive deformation registration of endorectal prostate MRI using ITK thin plate splines. Academic Radiology 2009, 16:351-357.
16. Chrisochoides, N., Fedorov, A., Kot, A., Archip, N., Black, P., Clatz, O., Golby, A., Kikinis, R.,Warfield, S.K., 2006. Toward real-time image guided neurosurgery using distributed and grid computing. In: Proc. ACM/IEEE Conference on Supercomputing (SC 2006), p. 37.
17. Cool, D.W., Bax, J., Romagnoli, C., Ward, A.D., Gardi, L., Karnik, V., Izawa, J., Chin, J., Fenster, A., 2011. Fusion of MRI to 3D TRUS for mechanically-assisted targeted prostate biopsy: system design and initial clinical experience. In: Proc. of MICCAI Workshop on Prostate Cancer Imaging, Computer Aided Diagnosis, Prognosis and Intervention, pp. 121-133.
18. Daanen V., Gastaldo J., Giraud J.Y., Fourneret P., Descotes J.L., Bolla M., Collomb D., Troccaz J. MRI/TRUS data fusion for brachytherapy. International Journal of Medical Robotics 2006, 2:256-261.
19. Davatzikos C. Spatial transformation and registration of brain images using elastically deformable models. Computer Vision and Image Understanding 1997, 66:207-222.
20. Dice L.R. Measures of the amount of ecologic association between species. Ecology 1945, 26:297-302.
21. Domokos C., Nemeth J., Kato Z. Nonlinear shape registration without correspondences. IEEE Transactions on Pattern Analysis and Machine Intelligence 2012, 34:943-958.
22. Dunn O.J. Multiple comparisons using rank sums. Technometrics 1964, 6:241-252.
23. Fei B.W., Kemper C., Wilson D.L. A comparative study of warping and rigid body registration for the prostate and pelvic MR volumes. Computerized Medical Imaging and Graphics 2003, 27:267-281.
24. Fisher R.A. Statistical Methods for Research Workers 1932, Oliver & Boyd, Edinburgh and London.
25. Friedman M. A correction: the use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of American Statistical Association 1939, 34:109.
26. Ghose, S., Oliver, A., Martí, R., Lladó, X., Freixenet, J., Mitra, J., Vilanova, J.C., Comet, J., Meriaudeau, F., 2011a. Multiple mean models of statistical shape and probability priors for automatic prostate segmentation. In: Proc. of MICCAI Workshop on Prostate Cancer Imaging, Computer Aided Diagnosis, Prognosis and Intervention, pp. 35-46.
27. Ghose, S., Oliver, A., Martí, R., Lladó, X., Freixenet, J., Vilanova, J., Meriaudeau, F., 2010. Prostate segmentation with texture enhanced active appearance model. In: Proc. IEEE International Conference on Signal-Image Technology & Internet-Based Systems, pp. 18-22.
28. Ghose, S., Oliver, A., Martí, R., Lladó, X., Freixenet, J., Vilanova, J., Meriaudeau, F., 2011b. A probabilistic framework for automatic prostate segmentation with a statistical model of shape and appearance. In: Proc. IEEE International Conference on Image Processing, pp. 725-728.
29. Gosset W.S. The probable error of a mean. Biometrika 1908, 6:1-25.
30. Hu Y., Ahmed H.U., Taylor Z., Allem C., Emberton M., Hawkes D., Barratt D. MR to ultrasound registration for image-guided prostate interventions. Medical Image Analysis 2012, 16:687-703.
31. Huttenlocher D.P., Klanderman G.A., Rucklidge W.J. Comparing images using the Hausdorff distance. IEEE Transaction in Pattern Analysis and Machine Intelligence 1993, 15:850-863.
32. Jonker R., Volgenant A. A shortest augmenting path algorithm for dense and sparse linear assignment problems. Computing 1987, 38:325-340.
33. Kadoury, S., Yan, P., Xu, S., Glossop, N., Choyke, P., Turkbey, B., Pinto, P., Wood, B.J., Kruecker, J., 2010. Realtime TRUS/MRI fusion targeted-biopsy for prostate cancer: a clinical demonstration of increased positive biopsy rates. In: Proc. of MICCAI Workshop on Prostate Cancer Imaging, Computer Aided Diagnosis, Prognosis and Intervention, pp. 52-62.
34. Kaplan I., Oldenburg N.E., Meskell P., Blake M., Church P., Holupka E.J. Real time MRI-ultrasound image guided stereotactic prostate biopsy. Magnetic Resonance Imaging 2002, 20:295-299.
35. Karnik V.V., Fenster A., Bax J., Cool D.W., Gardi L., Gyacskov I., Romagnoli C., Ward A.D. Assessment of image registration accuracy in three-dimensional transrectal ultrasound guided prostate biopsy. Medical Physics 2010, 37:802-813.
36. de Koning H.J., Auvinen A., Sanchez A.B., et al. Large-scale randomized prostate cancer screening trials: program performances in the european randomized screening for prostate cancer trial and the prostate, lung, colorectal and ovary cancer trial. International Journal of Cancer 2002, 97:237-244.
37. Kroon, D.J., 2008. B-spline grid, image and point based registration. http://www.mathworks.com/matlabcentral/fileexchange/20057.
38. Kruskal W.H., Wallis W.A. Use of ranks in one-criterion variance analysis. Journal of the American Statistical Association 1952, 47:583-621.
39. Levene H. Robust tests for equality of variances. Contributions to Probability and Statistics 1960, Stanford University Press, Palo Alto, California. I. Olkin ed.
40. Lilliefors H.W. On the Kolmogorov-Smirnov test for normality with mean and variance unknown. Journal of American Statistical Association 1967, 62:399-402.
41. Lu, J., Srikanchana, R., McClain, M., Wang, Y., Xuan, J., Sesterhenn, I.A., Freedman, M.T., Mun, S.K., 2000. A statistical volumetric model for characterization and visualization of prostate cancer. In: Proc. of SPIE, pp. 142-153.
42. Marquardt D.W. An algorithm for least-squares estimation of nonlinear parameters. SIAM Journal on Applied Mathematics 1963, 11:434-441.
43. Maurer C.R., Fitzpatrick J.M., Wang M.Y., Member S., Galloway R.L., Maciunas R.J., Allen G.S. Registration of head volume images using implantable fiducial markers. IEEE Transactions on Medical Imaging 1997, 16:447-462.
44. Maurer C.R., McCrory J.J., Fitzpatrick J.M. Estimation of accuracy in localizing externally attached markers in multimodal volume head images. Proceedings of SPIE Medical Imaging 1993, 1898:43-54.
45. Mitra J., Martí R., Oliver A., Lladó X., Ghose S., Vilanova J.C., Meriaudeau F. Prostate multimodality image registration based on B-splines and quadrature local energy. International Journal of Computer Assisted Radiology and Surgery 2012, 7:445-454.
46. Mitra, J., Oliver, A., Martí, R., Lladó, X., Vilanova, J.C., Meriaudeau, F., 2010. Multimodal prostate registration using thin-plate splines from automatic correspondences. In: Proc. of Intl. Conference on Digital Image Computing:Techniques and Applications (DICTA'10), Sydney, Australia, pp. 587-592.
47. Mitra, J., Srikantha, A., Sidibé, D., Martí, R., Oliver, A., Lladó, X., Vilanova, J.C., Meriaudeau, F., 2012. A shape-based statistical method to retrieve 2D TRUS-MR slice correspondence for prostate biopsy. In: Proc. of SPIE Medical Imaging, San Diego, Calfornia, USA, pp. 83143M-1-9.
48. Mizowaki T., Cohen G.N., Fung A.Y.C., Zaider M. Towards integrating functional imaging in the treatment of prostate cancer with radiation: the registration of the MR spectroscopy imaging to ultrasound/CT images and its implementation in treatment planning. International Journal of Radiation Oncology Biology Physics 2002, 54:1558-1564.
49. Narayanan, R., Kurhanewicz, J., Shinohara, K., Crawford, E.D., Simoneau, A., Suri, J.S., 2009. MRI-ultrasound registration for targeted prostate biopsy. In: Proc. of IEEE Intl. Symposium on Biomedical Imaging: From Nano to Macro (ISBI'2009), Boston, MA, pp. 991-994.
50. Natarajan S., Marks L.S., Margolis D.J., Huang J., Macairan M.L., Lieu P., Fenster A. Clinical application of a 3D ultrasound-guided prostate biopsy system. Urologic Oncology 2011, 29:334-342. Seminar Article.
51. Nemeth, J., Domokos, C., Kato, Z., 2009a. Nonlinear registration of binary shapes. In: Proc. IEEE International Conference on Image Processing (ICIP'09), Cairo, Egypt, pp. 1001-1004.
52. Nemeth, J., Domokos, C., Kato, Z., 2009b. Recovering planar homographies between 2D shapes. In: Proc. IEEE International Conference on Computer Vision (ICCV'09), Kyoto, Japan, pp. 2170-2176.
53. Oguro S., Tokuda J., Elhawary H., Haker S., Kikinis R., Tempany C.M., Hata N. MRI signal intensity based B-spline nonrigid registration for pre- and intraoperative imaging during prostate brachytherapy. Magnetic Resonance Imaging 2009, 30:1052-1058.
54. Ourselin, S., Roche, A., Prima, S., Ayache, N., 2000. Block matching: a general framework to improve robustness of rigid registration of medical images. In: Medical Image Computing and Computer-Assisted Intervention (MICCAI 2000). Lecture Notes in Computer Science, vol. 1935, pp. CH373-CH373.
55. Papadimitriou C., Stieglitz K. Combinatorial Optimization: Algorithms and Complexity 1982, Prentice Hall.
56. Porter, C.R., O'Donnell, C., Crawford, E.D., Gamito, E.J., Kim, J., Tewari, A., 2010. Predicting the Outcome of the Random Prostate Biopsy. Technical Report, Department of Urology, State University of New York, Stony Brook, NY. (accessed 07.06.10). http://Http://www.cancernews.com/data/Article/230.asp.
57. Reynier C., Troccaz J., Fourneret P., Dusserre A., Gay-Jeune C., Descotes J.L., Bolla M., Giraud J.Y. MRI/TRUS data fusion for prostate brachytherapy: preliminary results. Medical Physics 2004, 31:1568-1575.
58. Rohr K., Stiehl H.S., Sprengel R., Buzug T.M., Weese J., Kuhn M.H. Landmark-based elastic registration using approximating thin-plate splines. IEEE Transactions on Pattern Analysis and Machine Intelligence 2001, 20:526-534.
59. Roobol M.J., Schroder F.H. European randomized study of screening for prostate cancer: achievements and presentation. BJU International 2003, 92:117-122.
60. Rueckert D., Sonoda L.I., Hayes C., Hill D.L.G., Leach M.O., Hawkes D.J. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Transactions on Medical Imaging 1999, 18:712-721.
61. Shen D.G., Herskovits E., Davatzikos C. An adaptive focus statistical shape model for segmentation and shape modeling of 3d brain structures. IEEE Transactions on Medical Imaging 2001, 20:257-271.
62. Singh A.K., Kruecker J., Xu S., Glossop N., Guion P., Ullman K., Choyke P.L., Wood B.J. Initial clinical experience with real-time transrectal ultrasonography-magnetic resonance imaging fusion-guided prostate biopsy. British Journal of Urology International 2008, 101:841-845.
63. Sokal R.R., Rohlf F.J. Biometry: The Principles and Practice of Statistics in Biological Research 1995, W.H. Freeman, New York. third ed.
64. Szeliski R., Lavallée S. Matching 3-D anatomical surfaces with non-rigid deformations using octree-splines. International Journal of Computer Vision 1996, 18:171-196.
65. Veltman J., Goossen T., Laguna P., Wijkstra H., de la Rosette J. New technical improvements for TRUS in the diagnosis of prostate cancer. European Urology Supplements 2002, 1:8-14.
66. Vilanova J.C., Barceló-Vidal C., Comet J., Boada M., Barceló J., Ferrer J., Albanell J. Usefulness of prebiopsy multi-functional and morphologic MRI combined with the free-to-total PSA ratio in the detection of prostate cancer. American Journal of Roentgenology 2011, 196:W715-W722.
67. Vishwanath, S., Bloch, B.N., Rosen, M., Chappelow, J., Toth, R., Rofsky, N., Lenkinski, R., Genega, E., Kalyanpur, A., Madabhushi, A., 2009. Integrating structural and functional imaging for computer assisted detection of prostate cancer on multi-protocol In Vivo 3 Tesla MRI. In: Proceedings of SPIE 7260, pp. 72603I.1-12.
68. Welch B.L. The generalization of 'studentŚ' problem when several different population varlances are involved. Biometrika 1947, 34:28-35.
69. Xiao, G., Bloch, B., Chappelow, J., Genega, E., Rofsky, N., Lenkinski, R., Madabhushi, A., 2010. A structural-functional MRI-based disease atlas:application to computer-aided-diagnosis of prostate cancer. In: Proc. of SPIE Medical Imaging:Image Processing, February, pp. 762303.1-12.
70. Xu S., Kruecker J., Turkbey B., Glossop N., Singh A.K., Choyke P., Pinto P., Wood B.J. Real-time MRI-TRUS fusion for guidance of targeted prostate biopsies. Computer Aided Surgery 2008, 13:255-264.
71. Zar J.H. Biostatistical Analysis 1999, Prentice Hall, Upper Saddle River, NJ. fourth ed.
72. Zhu Y., Williams S., Zwiggelaar R. Computer technology in detection and staging of prostate carcinoma: a review. Medical Image Analysis 2006, 10:178-199.