LCC-Demons: A robust and accurate symmetric diffeomorphic registration algorithm

NeuroImage

Volumn 81, Issue , 2013, Pages 470-483

  Lorenzi, M ^a,b   Ayache, N ^a   Frisoni, G B ^b   Pennec, X ^a  

^a INRIA   (France)

^b IRCCS ISTITUTO CENTRO SAN GIOVANNI DI DIO FATEBENEFRATELLI   (Italy)

Author keywords

Alzheimer's disease; Demons; Longitudinal atrophy; Non linear registration; Optimization

Indexed keywords

ACCURACY; ALGORITHM; ALZHEIMER DISEASE; ARTICLE; BRAIN ATROPHY; BRAIN SIZE; CONTROLLED STUDY; DIFFEOMORPHIC REGISTRATION ALGORITHM; HUMAN; IMAGE PROCESSING; LOCAL CORRELATION COEFFICIENT DEMONS ALGORITHM; NEUROIMAGING; NONLINEAR REGISTRATION; PRIORITY JOURNAL; ATROPHY; BRAIN; COMPUTER ASSISTED DIAGNOSIS; PATHOLOGY; PROCEDURES;

ALGORITHMS; ALZHEIMER DISEASE; ATROPHY; BRAIN; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; NEUROIMAGING;

EID: 84879728404     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2013.04.114     Document Type: Article

References (57)

1. Arsigny V., Commowick O., Pennec X., Ayache N. A Log-Euclidean framework for statistics on diffeomorphisms. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2006, 924-931. Springer Verlag.
2. Ashburner J. A fast diffeomorphic image registration algorithm. Neuroimage 2007, 38:95-113.
3. Ashburner J., Friston K. Voxel-based morphometry - the methods. Neuroimage 2000, 11:805-821.
4. Avants B., Anderson C., Grossman M., Gee J. Spatiotemporal normalization for longitudinal analysis of gray matter atrophy in frontotemporal dementia. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007, 303-310. Springer Verlag.
5. 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. Med. Image Anal. 2008, 12:26-41.
6. Bajcsy R., Gee J.C., Reivich M. Elastically deforming 3D Atlas to match anatomical brain images. J. Comput. Assist. Tomogr. 1993, 17:225-236.
7. Beg M.F., Miller M.I., Trouvé A., Younes L. Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int. J. Comput. Vis. 2005, 61:139-157.
8. Bossa M., Hernandez M., Olmos S. Contributions to 3D diffeomorphic atlas estimation: application to brain images. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2007, 667-674. Springer Verlag.
9. Boyes R., Rueckert D., Aljabar P., Whitwell J., Schott J., Hill D., Fox N. Cerebral atrophy measurements using Jacobian integration: comparison with the boundary shift integral. Neuroimage 2006, 32.
10. Cachier P., Ayache N. Isotropic energies, filters and splines for vectorial regularization. J. Math. Imaging Vis. 2004, 20:251-265.
11. Cachier P., Bardinet E., Dormont D., Pennec X., Ayache N. Iconic feature based nonrigid registration: the PASHA algorithm. Comp. Vision Image Underst. 2003, 89:272-298. (Special Issue on Nonrigid Registration).
12. Camara O., Schnabel J., Ridgway G., et al. Accuracy assessment of global and local atrophy measurement techniques with realistic simulated longitudinal Alzheimer's disease images. Neuroimage 2008, 42.
13. Chung M., Worsley K., Paus T., et al. A unified statistical approach to deformation-based morphometry. Neuroimage September 2001, 14(3):595-606.
14. Collins L.D., Holmes C.J., Peters T.M., Evans A.C. Automatic 3-D model-based neuroanatomical segmentation. Hum. Brain Mapp. 1995, 3:190-208.
15. Davis B., Fletcher P., Bullit E., Joshi S. Population shape regression from random design data. ICCV 2007, 4:375-405.
16. Dong L., Boyer A.L. An image correlation procedure for digitally reconstructed radiographs and electronic portal images. Int. J. Radiat. Oncol. Biol. Phys. 1995, 33:1053-1060.
17. Durrleman S., Pennec X., Trouvé A., Ayache N., Braga J. Comparison of the endocranial ontogenies between chimpanzees and bonobos via temporal regression and spatiotemporal registration. J. Hum. Evol. 2012, 62:74-88.
18. Fonov V.S., Evans A.C., McKinstry R.C., Almli C.R., Collins D.L. Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. Neuroimage 2009, 47:S102.
19. Fox N., Cousens S., Scahill R., Harvey R., Rossor M. Using serial registered brain magnetic resonance imaging to measure disease progression in Alzheimer disease: power calculations and estimates of sample size to detect treatment effects. Arch. Neurol. 2000, 57:339-344.
20. Fox N., Crum W., Schaill R., Stevens J., Janssen J., Rossnor M. Imaging of onset and progression of Alzheimer's disease with voxel compression mapping of serial magnetic resonance images. Lancet 2001, 358:201-205.
21. Fox N., Ridgway G., Schott J. Algorithms, atrophy and Alzheimer's disease: cautionary tales for clinical trials. Neuroimage 2011, 57:15-18.
22. FreeBorough P., Fox N. The boundary shift integral: an accurate and robust measure of cerebral volume changes from registered repeat MRI. IEEE Trans. Med. Imaging 1997, 16.
23. Hermosillo G., Faugeras O.D. Well-posedness of two nonrigid multimodal image registration methods. SIAM J. Appl. Math. 2004, 64:1550-1587.
24. Hou Z. A review on MR image intensity inhomogeneity correction. Int. J. Biomed. Imaging 2006, 49515.
25. Jolly M.P., Guetter C., Guehring J. Cardiac segmentation in MR cine data using inverse consistent deformable registration 2010, 484-487. ISBI (Ed.).
26. Joshi S., Davis B., Jomier B.M., G.G.B Unbiased diffeomorphic atlas construction for computational anatomy. Neuroimage 2004, 23:151-160.
27. Klein A., Andersson J., Ardekani B., et al. Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage 2009, 46:786-802.
28. Leow A., Yanovsky I., Chiang M., Lee A., et al. Statistical properties of Jacobian maps and the realization of unbiased large-deformation nonlinear image registration. TMI 2007, 26:822-832.
29. Leung K.K., Clarkson M.J., Bartlett J.W., Clegg S., Jack C.R., Weiner M.W., Fox N.C., Ourselin S. Robust atrophy rate measurement in Alzheimer's disease using multi-site serial MRI: tissue-specific intensity normalization and parameter selection. Neuroimage 2009, 59.
30. Lombaert H., Grady L., Pennec X., Ayache N., Cheriet F. Spectral demons - image registration via global spectral correspondence 2012, 2:30-44. ECCV (Ed.).
31. 1-42 levels on the longitudinal changes in healthy aging: hierarchical modeling based on stationary velocity fields. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2011, 663-670. Springer Verlag.
32. Lorenzi M., Ayache N., Pennec X. Regional flux analysis of longitudinal atrophy in Alzheimer's disease. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2012, Springer Verlag.
33. Lorenzi M., Ayache N., Frisoni G., Pennec X. Probabilistic flux analysis of cerebral longitudinal atrophy 2012, MICCAI 2012 Workshop on Novel Imaging Biomarkers for Alzheimer's Disease and Related Disorders (Ed.).
34. Lorenzi M., Ayache N., Pennec X., Frisoni G. Disentangling the normal aging from the pathological Alzheimer's disease progression on cross-sectional structural MR images 2012, MICCAI Workshop on Novel Imaging Biomakers in Alzheimer's Disease (Ed.).
35. Maes F., Collignon A., Vandermeulen D., Marchal G., Suetens P. Medical Image Computing and Computer-Assisted Intervention - MICCAIF. Multimodality image registration by maximization of mutual information. IEEE Trans. Med. Imaging 1997, 187-198.
36. Mansi T., Pennec X., Sermesant M., Delingette H., Ayache N. LogDemons revisited: consistent regularisation and incompressibility constraint for soft tissue tracking in medical images. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2010, 652-659. Springer Verlag.
37. Mansi T., Pennec X., Sermesant M., Delingette H., Ayache N. iLogDemons: a Demons-based registration algorithm for tracking incompressible elastic biological tissues. Int. J. Comput. Vis. 2011, 92:92-111.
38. McLeod K., Seiler C., Sermesant M., Pennec X. A near-incompressible poly-affine motion model for cardiac function analysis. MICCAI Workshop on Statistical Atlases and Computational Models of the Heart: Mapping Structure and Function (STACOM2012) 2012.
39. Modat M., Ridgway G., Daga P., Cardoso M., Hawkes D., Ashburner J., Ourselin S. Log-Euclidean free-form deformation. Proc. of SPIE Medical Imaging 2011 2011, vol. 7962. SPIE.
40. Patenaude B., Smith S., Kennedy D., Jenkinson M. A Bayesian model of shape and appearance for subcortical brain. Neuroimage 2011, 56(3):907-922.
41. Resnik S., Goldszal A., Davatzikos C., Golski S., Kraut M., Metter E., Bryan R., Zonderman A. One year age changes in MRI brain volumes in older adults. Cereb. Cortex 2000, 10:464-472.
42. Riddle W.R., Li R., Fitzpatrick J.M., DonLevy S.C., Dawant B.M., Price R.R. Characterizing changes in MR images with color-coded Jacobians. Magn. Reson. Imaging 2004, 22:769-777.
43. 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, 631-638. Springern Verlag.
44. Siless V., Glaunés J., Guevara P., Mangin J.F., Poupon C., Bihan D., Thirion B., Fillard P. Joint T1 and brain fiber Log-Demons registration using currents to model geometry. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2012, 57-65. Springer Verlag.
45. Sled J., Zijdenbos A., Evans A. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imaging 1998, 17:87-97.
46. Smith S. Fast robust automated brain extraction. Hum. Brain Mapp. 2002, 17.
47. Smith S., Zhang Y., Jenkinson M., Chen J., Matthews P., Federico A., Stefano N.D. Accurate, robust, and automated longitudinal and cross-sectional brain change analysis. Neuroimage 2002, 17.
48. Stefanescu R. Parallel Nonlinear Registration of Medical Images with A Priori Information on Anatomy and Pathology 2005, (Phd Thesis), Université de Nice - Sophia-Antipolis.
49. Studholme C., Hill D., Hawkes D. Automated 3-D registration of MR and CT images of the head. Med. Image Anal. 1996, 1:163-175.
50. Sweet A., Pennec X. Log-domain diffeomorphic registration of diffusion tensor images. Proc. of WBIR: Workshop on Biomedical Image Registration 2010.
51. Thompson P., Ayashi K., Zubicaray G., Janke A., Rose S., Semple J., Herman D., Hong M., Dittmer S., Dodrell D., Toga A. Dynamics of gray matter loss in Alzheimer's disease. J. Neurosci. 2003, 23:994-1005.
52. Tustison N., Cook B.A.P., Zheng Y., Egan A., Yushkevich P., Gee J. N4itk: improved n3 bias correction. IEEE Trans. Med. Imaging 2010, 29.
53. Vasilevskiy A., Siddiqi K. Flux maximizing geometric flows. IEEE Trans. Pattern Anal. Mach. Intell. 2002, 24:1565-1578.
54. Vercauteren T. Image Registration and Mosaicing for Dynamic In Vivo Fibered Confocal Microscopy 2008, (Phd thesis), École Nationale Supérieure des Mines de Paris.
55. Vercauteren T., Pennec X., Perchant A., Ayache N. Diffeomorphic demons: efficient non-parametric image registration. Neuroimage 2008, 45(Suppl. 1):S61-S72.
56. Vercauteren T., Pennec X., Perchant A., Ayache N. Symmetric Log-domain diffeomorphic registration: a Demons-based approach. Medical Image Computing and Computer-Assisted Intervention - MICCAI 2008, 754-761. Springer Verlag.
57. Wells W., Viola P., Atsumi H., Nakajima S., Kikinis R. Multi-modal volume registration by maximization of mutual information. Med. Image Anal. 1996, 1:35-51.