A reliable spatially normalized template of the human spinal cord - Applications to automated white matter/gray matter segmentation and tensor-based morphometry (TBM) mapping of gray matter alterations occurring with age

NeuroImage

Volumn 117, Issue , 2015, Pages 20-28

  Taso, Manuel ^a,b,c,d   Le Troter, Arnaud ^a,c   Sdika, Michaël ^e   Cohen Adad, Julien ^f   Arnoux, Pierre Jean ^b,d   Guye, Maxime ^a,c   Ranjeva, Jean Philippe ^a,c,d   Callot, Virginie ^a,c,d  

^a University of Aix Marseille   (France)

^b UMRT24 INRETS University of Mediterranée   (France)

^c TIMONE HOSPITAL   (France)

^d International Associated Lab in Biomechanics of Spine Injury and Pathologies (BSIP)   (France)

^e UNIVERSITÉ DE LYON   (France)

^f ÉCOLE POLYTECHNIQUE DE MONTRÉAL   (Canada)

Author keywords

Atrophy; Gray matter; MRI; Segmentation; Spinal cord; Template; Tensor based morphometry

Indexed keywords

ADULT; AGE; AGED; ARTICLE; BRAIN MAPPING; CEREBROSPINAL FLUID; FEASIBILITY STUDY; FEMALE; FIRST CERVICAL VERTEBRA; GRAY MATTER; HUMAN; IMAGE PROCESSING; MALE; MEASUREMENT ACCURACY; MORPHOMETRICS; NORMAL HUMAN; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; RELIABILITY; REPRODUCIBILITY; SIGNAL NOISE RATIO; SPATIAL NORMALIZATION; SPINAL CORD; SPINAL CORD ATROPHY; TENSOR BASED MORPHOMETRY; VALIDATION STUDY; WHITE MATTER; AGING; ANATOMY AND HISTOLOGY; AUTOMATED PATTERN RECOGNITION; MIDDLE AGED; PROCEDURES;

ADULT; AGING; GRAY MATTER; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MIDDLE AGED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SPINAL CORD; WHITE MATTER;

EID: 84930616730     PISSN: 10538119     EISSN: 10959572     Source Type: Journal    
DOI: 10.1016/j.neuroimage.2015.05.034     Document Type: Article

References (41)

1. Abdel-Aziz K., et al. Age related changes in metabolite concentrations in the normal spinal cord. PLoS One 2014, 9:e105774.
2. Agosta F., et al. Evidence for cervical cord tissue disorganisation with aging by diffusion tensor MRI. Neuroimage 2007, 36:728-735.
3. Ashburner J., Friston K.J. Voxel-based morphometry-the methods. Neuroimage 2000, 11:805-821.
4. Asman A.J., et al. Groupwise multi-atlas segmentation of the spinal cord's internal structure. Med. Image Anal. 2014, 18:460-471.
5. Avants B.B., et al. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 2008, 12:26-41.
6. Benhamou M., et al. Atlas of white-matter tracts in the human spinal cord. Proceedings of the Joint Annual Meeting of the ISMRM-ESMRMB, Milan, Italy 2014, 13.
7. Bot J.C., et al. Spinal cord abnormalities in recently diagnosed MS patients: added value of spinal MRI examination. Neurology 2004, 62:226-233.
8. Cadotte D.W., et al. Characterizing the location of spinal and vertebral levels in the human cervical spinal cord. AJNR Am. J. Neuroradiol. 2014, 10.3174/ajnr.A4192.
9. Chen M., et al. Automatic magnetic resonance spinal cord segmentation with topology constraints for variable fields of view. Neuroimage 2013, 83:1051-1062.
10. Cohen-Adad J., et al. Demyelination and degeneration in the injured human spinal cord detected with diffusion and magnetization transfer MRI. Neuroimage 2011, 55:1024-1033.
11. Cohen-Adad J., et al. Involvement of spinal sensory pathway in ALS and specificity of cord atrophy to lower motor neuron degeneration. Amyotroph. Lateral Scler. Frontotemporal Degener 2013, 14:30-38.
12. Cohen-Adad J., et al. Spinal Cord Toolbox: an open-source framework for processing spinal cord MRI data. Proceedings of the 20th annual meeting of OHBM, Hamburg, Germany 2014, 3633.
13. Collins D.L., et al. Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J. Comput. Assist. Tomogr. 1994, 18:192-205.
14. Cruz-Sanchez F.F., et al. Evaluation of neuronal loss, astrocytosis and abnormalities of cytoskeletal components of large motor neurons in the human anterior horn in aging. J. Neural Transm. 1998, 105:689-701.
15. De Leener B., et al. Robust, accurate and fast automatic segmentation of the spinal cord. Neuroimage 2014, 98:528-536.
16. Dice L.R. Measures of the amount of ecologic association between species. Ecology 1945, 26:297-302.
17. Ellingson B.M., et al. Gray and white matter delineation in the human spinal cord using diffusion tensor imaging and fuzzy logic. Acad. Radiol. 2007, 14:847-858.
18. Fonov V.S., et al. Framework for integrated MRI average template of the spinal cord white and gray matter: the MNI-Poly-AMU template. Neuroimage 2014, 102P2:817-827.
19. Frackowiak R. Chapter 36 - Morphometry. Human Brain Function 2004, 707-722. Academic Press, Burlington. Second ed. R.S.J. Frackowiak, K.J. Friston, C.D. Frith, R.J. Dolan, C.J. Price, S. Zeki, J.T. Ashburner, W.D. Penny (Eds.).
20. Fradet L., et al. Morphometrics of the entire human spinal cord and spinal canal measured from in vivo high-resolution anatomical magnetic resonance imaging. Spine (Phila Pa 1976) 2014, 39:E262-E269.
21. Giulietti G., et al. Semiautomated segmentation of the human spine based on echoplanar images. Magn. Reson. Imaging 2011, 29:1429-1436.
22. Good C.D., et al. A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage 2001, 14:21-36.
23. Held P., et al. Comparison of two-dimensional gradient echo, turbo spin echo and two-dimensional turbo gradient spin echo sequences in MRI of the cervical spinal cord anatomy. Eur. J. Radiol. 2001, 38:64-71.
24. Held P., et al. MRI of the abnormal cervical spinal cord using 2D spoiled gradient echo multiecho sequence (MEDIC) with magnetization transfer saturation pulse. A T2* weighted feasibility study. J. Neuroradiol. 2003, 30:83-90.
25. Horsfield M.A., et al. Rapid semi-automatic segmentation of the spinal cord from magnetic resonance images: application in multiple sclerosis. Neuroimage 2010, 50:446-455.
26. Jenkinson M., et al. Fsl. Neuroimage 2012, 62:782-790.
27. Kearney H., et al. Improved MRI quantification of spinal cord atrophy in multiple sclerosis. J. Magn. Reson. Imaging 2014, 39:617-623.
28. Kim J., et al. Structural consequences of diffuse traumatic brain injury: a large deformation tensor-based morphometry study. Neuroimage 2008, 39:1014-1026.
29. Mazziotta J., et al. A probabilistic atlas and reference system for the human brain: International Consortium for Brain Mapping (ICBM). Philos. Trans. R. Soc. Lond. B Biol. Sci. 2001, 356:1293-1322.
30. Rocca M.A., et al. Voxel-wise mapping of cervical cord damage in multiple sclerosis patients with different clinical phenotypes. J. Neurol. Neurosurg. Psychiatry 2013, 84:35-41.
31. Schlaeger R., et al. Spinal cord gray matter atrophy correlates with multiple sclerosis disability. Ann. Neurol. 2014, 76:568-580.
32. Sled J.G., et al. A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imaging 1998, 17:87-97.
33. Stroman P.W. Magnetic resonance imaging of neuronal function in the spinal cord: spinal FMRI. Clin. Med. Res. 2005, 3:146-156.
34. Stroman P.W., et al. Spatial normalization, bulk motion correction and coregistration for functional magnetic resonance imaging of the human cervical spinal cord and brainstem. Magn. Reson. Imaging 2008, 26:809-814.
35. Stroman P.W., et al. The current state-of-the-art of spinal cord imaging: methods. Neuroimage 2014, 84:1070-1081.
36. Summers P., et al. A preliminary study of the effects of trigger timing on diffusion tensor imaging of the human spinal cord. AJNR Am. J. Neuroradiol. 2006, 27:1952-1961.
37. Taso M., et al. Construction of an in vivo human spinal cord atlas based on high-resolution MR images at cervical and thoracic levels: preliminary results. Magn. Reson. Mater. Phys. 2014, 27:257-267.
38. Valsasina P., et al. Spatial normalization and regional assessment of cord atrophy: voxel-based analysis of cervical cord 3D T1-weighted images. AJNR Am. J. Neuroradiol. 2012, 33:2195-2200.
39. Valsasina P., et al. Regional cervical cord atrophy and disability in multiple sclerosis: a voxel-based analysis. Radiology 2013, 266:853-861.
40. Wheeler-Kingshott C.A., et al. The current state-of-the-art of spinal cord imaging: applications. Neuroimage 2014, 84:1082-1093.
41. Yiannakas M.C., et al. Feasibility of grey matter and white matter segmentation of the upper cervical cord in vivo: a pilot study with application to magnetisation transfer measurements. Neuroimage 2012, 63:1054-1059.