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Diffusion MRI of the neonate brain: acquisition, processing and analysis techniques

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Abstract

Diffusion MRI (dMRI) is a popular noninvasive imaging modality for the investigation of the neonate brain. It enables the assessment of white matter integrity, and is particularly suited for studying white matter maturation in the preterm and term neonate brain. Diffusion tractography allows the delineation of white matter pathways and assessment of connectivity in vivo. In this review, we address the challenges of performing and analysing neonate dMRI. Of particular importance in dMRI analysis is adequate data preprocessing to reduce image distortions inherent to the acquisition technique, as well as artefacts caused by head movement. We present a summary of techniques that should be used in the preprocessing of neonate dMRI data, and demonstrate the effect of these important correction steps. Furthermore, we give an overview of available analysis techniques, ranging from voxel-based analysis of anisotropy metrics including tract-based spatial statistics (TBSS) to recently developed methods of statistical analysis addressing issues of resolving complex white matter architecture. We highlight the importance of resolving crossing fibres for tractography and outline several tractography-based techniques, including connectivity-based segmentation, the connectome and tractography mapping. These techniques provide powerful tools for the investigation of brain development and maturation.

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References

  1. Dubois J, Dehaene-Lambertz G, Perrin M et al (2008) Asynchrony of the early maturation of white matter bundles in healthy infants: quantitative landmarks revealed noninvasively by diffusion tensor imaging. Hum Brain Mapp 29:14–27

    Article  PubMed  Google Scholar 

  2. Rodrigues K, Ellen Grant P (2011) Diffusion-weighted imaging in neonates. Neuroimaging Clin N Am 21:127–151, viii

    Article  PubMed  Google Scholar 

  3. Gilmore JH, Zhai G, Wilber K et al (2004) 3 tesla magnetic resonance imaging of the brain in newborns. Psychiatry Res 132:81–85

    Article  PubMed  Google Scholar 

  4. Dagia C, Ditchfield M (2008) 3T MRI in paediatrics: challenges and clinical applications. Eur J Radiol 68:309–319

    Article  PubMed  Google Scholar 

  5. Rona Z, Klebermass K, Cardona F et al (2010) Comparison of neonatal MRI examinations with and without an mr-compatible incubator: advantages in examination feasibility and clinical decision-making. Eur J Paediatr Neurol 14:410–417

    Article  PubMed  CAS  Google Scholar 

  6. Haney B, Reavey D, Atchison L et al (2010) Magnetic resonance imaging studies without sedation in the neonatal intensive care unit: safe and efficient. J Perinat Neonatal Nurs 24:256-266

    PubMed  Google Scholar 

  7. Neubauer V, Griesmaier E, Baumgartner K et al (2011) Feasibility of cerebral MRI in non-sedated preterm-born infants at term-equivalent age: report of a single centre. Acta Paediatr 100:1544-1547

    Google Scholar 

  8. Alexander AL, Lee JE, Lazar M et al (2007) Diffusion tensor imaging of the brain. Neurotherapeutics 4:316–329

    Article  PubMed  Google Scholar 

  9. Conturo TE, McKinstry RC, Aronovitz JA et al (1995) Diffusion MRI: precision, accuracy and flow effects. NMR Biomed 8:307–332

    Article  PubMed  CAS  Google Scholar 

  10. Dudink J, Larkman DJ, Kapellou O et al (2008) High b value diffusion tensor imaging of the neonatal brain at 3 T. AJNR 29:1966–1972

    Article  PubMed  CAS  Google Scholar 

  11. Metzler-Baddeley C, O’Sullivan MJ, Bells S et al (2012) How and how not to correct for csf-contamination in diffusion MRI. Neuroimage 59:1394–1403

    Article  PubMed  Google Scholar 

  12. Pipe J (2009) Pulse sequences for diffusion-weighted MRI. In: Johansen-Berg H, Behrens TEJ (eds) Diffusion MRI: from quantitative measurement to in-vivo neuroanatomy. Academic Press, Amsterdam Boston, pp 11-35

  13. Ardekani S, Selva L, Sayre J et al (2006) Quantitative metrics for evaluating parallel acquisition techniques in diffusion tensor imaging at 3 tesla. Invest Radiol 41:806–814

    Article  PubMed  Google Scholar 

  14. Chang H, Fitzpatrick JM (1992) A technique for accurate magnetic resonance imaging in the presence of field inhomogeneities. IEEE Trans Med Imaging 11:319–329

    Article  PubMed  CAS  Google Scholar 

  15. Reese TG, Heid O, Weisskoff RM et al (2003) Reduction of eddy-current-induced distortion in diffusion MRI using a twice-refocused spin echo. Magn Reson Med 49:177–182

    Article  PubMed  CAS  Google Scholar 

  16. Kwong KK, McKinstry RC, Chien D et al (1991) Csf-Suppressed quantitative single-shot diffusion imaging. Magn Reson Med 21:157–163

    Article  PubMed  CAS  Google Scholar 

  17. Zhu T, Hu R, Qiu X et al (2011) Quantification of accuracy and precision of multi-center DTI measurements: a diffusion phantom and human brain study. Neuroimage 56:1398–1411

    Article  PubMed  Google Scholar 

  18. Jones DK, Cercignani M (2010) Twenty-five pitfalls in the analysis of diffusion MRI data. NMR Biomed 23:803–820

    Article  PubMed  Google Scholar 

  19. Nam H, Park HJ (2011) Distortion correction of high b-valued and high angular resolution diffusion images using iterative simulated images. Neuroimage 57:968–978

    Article  PubMed  Google Scholar 

  20. Vovk U, Pernus F, Likar B (2007) A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans Med Imaging 26:405–421

    Article  PubMed  Google Scholar 

  21. Jenkinson M (2004) Improving the registration of B0-distorted EPI images using calculated cost function weights. Tenth Annual Meeting of the Organization for Human Brain Mapping

  22. Wu M, Chang LC, Walker L et al (2008) Comparison of EPI distortion correction methods in diffusion tensor MRI using a novel framework. Med Image Comput Comput Assist Interv 11:321–329

    PubMed  CAS  Google Scholar 

  23. Pierpaoli C (2010) Artifacts in diffusion MRI. In: Derek K Jones (ed) Diffusion MRI: Theory, Methods and Applications, Oxford University Press, New York, p 303–318

  24. Smith SM, Jenkinson M, Woolrich MW et al (2004) Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23(Suppl 1):S208–S219

    Article  PubMed  Google Scholar 

  25. Bai Y, Alexander DC (2008) Model-based registration to correct for motion between acquisitions in diffusion MR imaging. 2008 IEEE International Symposium on biomedical imaging: from nano to macro, Paris, pp 947-950

  26. Leemans A, Jones DK (2009) The b-matrix must be rotated when correcting for subject motion in DTI data. Magn Reson Med 61:1336–1349

    Article  PubMed  Google Scholar 

  27. Rohde GK, Barnett AS, Basser PJ et al (2004) Comprehensive approach for correction of motion and distortion in diffusion-weighted MRI. Magn Reson Med 51:103–114

    Article  PubMed  CAS  Google Scholar 

  28. Jones DK (2010) The signal intensity must be modulated by the determinant of the Jacobian when correcting for eddy currents in diffusion MR. 19th Annual Meeting of the International Society for Magnetic Resonance in Medicine, Montreal

  29. Chang LC, Jones DK, Pierpaoli C (2005) RESTORE: robust estimation of tensors by outlier rejection. Magn Reson Med 53:1088–1095

    Article  PubMed  Google Scholar 

  30. Morris D, Nossin-Manor R, Taylor MJ et al (2011) Preterm neonatal diffusion processing using detection and replacement of outliers prior to resampling. Magn Reson Med 66:92–101

    Article  PubMed  Google Scholar 

  31. Zhou Z, Liu W, Cui J et al (2011) Automated artifact detection and removal for improved tensor estimation in motion-corrupted DTI data sets using the combination of local binary patterns and 2D partial least squares. Magn Reson Imaging 29:230–242

    Article  PubMed  Google Scholar 

  32. Pasternak O, Sochen N, Gur Y et al (2009) Free water elimination and mapping from diffusion MRI. Magn Reson Med 62:717–730

    Article  PubMed  Google Scholar 

  33. Thompson DK, Inder TE, Faggian N et al (2011) Characterization of the corpus callosum in very preterm and full-term infants utilizing MRI. Neuroimage 55:479–490

    Article  PubMed  Google Scholar 

  34. Rose J, Butler EE, Lamont LE et al (2009) Neonatal brain structure on MRI and diffusion tensor imaging, sex, and neurodevelopment in very-low-birthweight preterm children. Dev Med Child Neurol 51:526–535

    Article  PubMed  Google Scholar 

  35. Bartha AI, Yap KR, Miller SP et al (2007) The normal neonatal brain: MR imaging, diffusion tensor imaging, and 3D MR spectroscopy in healthy term neonates. AJNR 28:1015–1021

    Article  PubMed  CAS  Google Scholar 

  36. Deipolyi AR, Mukherjee P, Gill K et al (2005) Comparing microstructural and macrostructural development of the cerebral cortex in premature newborns: diffusion tensor imaging versus cortical gyration. Neuroimage 27:579–586

    Article  PubMed  Google Scholar 

  37. Partridge SC, Mukherjee P, Henry RG et al (2004) Diffusion tensor imaging: serial quantitation of white matter tract maturity in premature newborns. Neuroimage 22:1302–1314

    Article  PubMed  Google Scholar 

  38. Zhai G, Lin W, Wilber KP et al (2003) Comparisons of regional white matter diffusion in healthy neonates and adults performed with a 3.0-T head-only MR imaging unit. Radiology 229:673–681

    Article  PubMed  Google Scholar 

  39. Mukherjee P, Miller JH, Shimony JS et al (2002) Diffusion-tensor MR imaging of gray and white matter development during normal human brain maturation. AJNR 23:1445–1456

    PubMed  Google Scholar 

  40. Calamante F, Tournier JD, Jackson GD et al (2010) Track-density imaging (TDI): super-resolution white matter imaging using whole-brain track-density mapping. Neuroimage 53:1233–1243

    Article  PubMed  Google Scholar 

  41. Ashburner J, Friston KJ (2000) Voxel-based morphometry—the methods. Neuroimage 11:805–821

    Article  PubMed  CAS  Google Scholar 

  42. Giménez M, Miranda MJ, Born AP et al (2008) Accelerated cerebral white matter development in preterm infants: a voxel-based morphometry study with diffusion tensor MR imaging. Neuroimage 41:728–734

    Article  PubMed  Google Scholar 

  43. Aeby A, Liu Y, De Tiège X et al (2009) Maturation of thalamic radiations between 34 and 41 weeks’ gestation: a combined voxel-based study and probabilistic tractography with diffusion tensor imaging. AJNR 30:1780–1786

    Article  PubMed  CAS  Google Scholar 

  44. Oishi K, Mori S, Donohue PK et al (2011) Multi-contrast human neonatal brain atlas: application to normal neonate development analysis. Neuroimage 56:8–20

    Article  PubMed  Google Scholar 

  45. Jones DK, Symms MR, Cercignani M et al (2005) The effect of filter size on VBM analyses of DT-MRI data. Neuroimage 26:546–554

    Article  PubMed  Google Scholar 

  46. Rosenfeld A, Kak AC (1982) Digital picture processing. Academic Press, New York

    Google Scholar 

  47. Smith SM, Jenkinson M, Johansen-Berg H et al (2006) Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage 31:1487–1505

    Article  PubMed  Google Scholar 

  48. Bassi L, Chew A, Merchant N et al (2012) Diffusion tensor imaging in preterm infants with punctate white matter lesions. Pediatr Res 69:561-566

    Google Scholar 

  49. Ball G, Boardman JP, Rueckert D et al (2012) The effect of preterm birth on thalamic and cortical development. Cereb Cortex 22:1016-10124

    Google Scholar 

  50. Ball G, Counsell SJ, Anjari M et al (2010) An optimised tract-based spatial statistics protocol for neonates: applications to prematurity and chronic lung disease. Neuroimage 53:94–102

    Article  PubMed  Google Scholar 

  51. Anjari M, Counsell SJ, Srinivasan L et al (2009) The association of lung disease with cerebral white matter abnormalities in preterm infants. Pediatrics 124:268–276

    Article  PubMed  Google Scholar 

  52. Rose SE, Hatzigeorgiou X, Strudwick MW et al (2008) Altered white matter diffusion anisotropy in normal and preterm infants at term-equivalent age. Magn Reson Med 60:761–767

    Article  PubMed  Google Scholar 

  53. Bassi L, Ricci D, Volzone A et al (2008) Probabilistic diffusion tractography of the optic radiations and visual function in preterm infants at term equivalent age. Brain 131:573–582

    Article  PubMed  Google Scholar 

  54. Anjari M, Srinivasan L, Allsop JM et al (2007) Diffusion tensor imaging with tract-based spatial statistics reveals local white matter abnormalities in preterm infants. Neuroimage 35:1021–1027

    Article  PubMed  Google Scholar 

  55. Porter EJ, Counsell SJ, Edwards AD et al (2010) Tract-based spatial statistics of magnetic resonance images to assess disease and treatment effects in perinatal asphyxial encephalopathy. Pediatr Res 68:205–209

    Article  PubMed  Google Scholar 

  56. Kindlmann G, Tricoche X, Westin CF (2007) Delineating white matter structure in diffusion tensor MRI with anisotropy creases. Med Image Anal 11:492–502

    Article  PubMed  Google Scholar 

  57. Yushkevich PA, Zhang H, Simon TJ et al (2008) Structure-specific statistical mapping of white matter tracts. Neuroimage 41:448–461

    Article  PubMed  Google Scholar 

  58. Corouge I, Fletcher PT, Joshi S et al (2006) Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis. Med Image Anal 10:786–798

    Article  PubMed  Google Scholar 

  59. Smith SM, Nichols TE (2009) Threshold-free cluster enhancement: addressing problems of smoothing, threshold dependence and localisation in cluster inference. Neuroimage 44:83–98

    Article  PubMed  Google Scholar 

  60. Behrens TE, Berg HJ, Jbabdi S et al (2007) Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? Neuroimage 34:144–155

    Article  PubMed  CAS  Google Scholar 

  61. Seunarine KK, Alexander DC (2009) Multiple fibers: beyond the diffusion tensor. In: Johansen-Berg H, Behrens TEJ (eds) Diffusion MRI: from quantitative measurement to in-vivo neuroanatomy. Academic Press, New York, pp 55-72

  62. Alexander DC, Seunarine KK (2010) Mathematics of crossing fibers. In: Jones DK (ed) Diffusion MRI: theory, methods, and applications. Oxford University Press, New York, pp 451–464

  63. Jbabdi S, Behrens TE, Smith SM (2010) Crossing fibres in tract-based spatial statistics. Neuroimage 49:249–256

    Article  PubMed  Google Scholar 

  64. Raffelt D, Tournier JD, Rose S et al (2012) Apparent fibre density: a novel measure for the analysis of diffusion-weighted magnetic resonance images. Neuroimage 59:3976-3994

    Google Scholar 

  65. Tournier JD, Calamante F, Connelly A (2007) Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. Neuroimage 35:1459–1472

    Article  PubMed  Google Scholar 

  66. Salmela MB, Cauley KA, Nickerson JP et al (2010) Magnetic resonance diffusion tensor imaging (MRDTI) and tractography in children with septo-optic dysplasia. Pediatr Radiol 40:708–713

    Article  PubMed  Google Scholar 

  67. Liu Y, Balériaux D, Kavec M et al (2010) Structural asymmetries in motor and language networks in a population of healthy preterm neonates at term equivalent age: a diffusion tensor imaging and probabilistic tractography study. Neuroimage 51:783–788

    Article  PubMed  Google Scholar 

  68. Adams E, Chau V, Poskitt KJ et al (2010) Tractography-based quantitation of corticospinal tract development in premature newborns. J Pediatr 156:882–888, 888.e1

    Article  PubMed  Google Scholar 

  69. Gilmore JH, Lin W, Corouge I et al (2007) Early postnatal development of corpus callosum and corticospinal white matter assessed with quantitative tractography. AJNR 28:1789–1795

    Article  PubMed  CAS  Google Scholar 

  70. van Pul C, Buijs J, Vilanova A et al (2006) Infants with perinatal hypoxic ischemia: feasibility of fiber tracking at birth and 3 months. Radiology 240:203–214

    Article  PubMed  Google Scholar 

  71. Partridge SC, Mukherjee P, Berman JI et al (2005) Tractography-based quantitation of diffusion tensor imaging parameters in white matter tracts of preterm newborns. J Magn Reson Imaging 22:467–474

    Article  PubMed  Google Scholar 

  72. Mukherjee P, McKinstry RC (2006) Diffusion tensor imaging and tractography of human brain development. Neuroimaging Clin N Am 16:19–43, vii

    Article  PubMed  Google Scholar 

  73. Behrens TE, Woolrich MW, Jenkinson M et al (2003) Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn Reson Med 50:1077–1088

    Article  PubMed  CAS  Google Scholar 

  74. Dubois J, Hertz-Pannier L, Dehaene-Lambertz G et al (2006) Assessment of the early organization and maturation of infants’ cerebral white matter fiber bundles: a feasibility study using quantitative diffusion tensor imaging and tractography. Neuroimage 30:1121–1132

    Article  PubMed  CAS  Google Scholar 

  75. Behrens TE, Johansen-Berg H, Woolrich MW et al (2003) Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat Neurosci 6:750–757

    Article  PubMed  CAS  Google Scholar 

  76. Counsell SJ, Dyet LE, Larkman DJ et al (2007) Thalamo-cortical connectivity in children born preterm mapped using probabilistic magnetic resonance tractography. Neuroimage 34:896–904

    Article  PubMed  Google Scholar 

  77. Sporns O, Tononi G, Kötter R (2005) The human connectome: a structural description of the human brain. PLoS Comput Biol 1:e42

    Article  PubMed  Google Scholar 

  78. Hagmann P (2005) From diffusion MRI to brain connectomics. PhD Thesis, Ecole Polytechnique Fédérale de Lausanne, Lausanne

  79. Rose S, Pannek K, Bell C et al (2012) Direct evidence of intra- and interhemispheric corticomotor network degeneration in amyotrophic lateral sclerosis: an automated MRI structural connectivity study. Neuroimage 59:2661-2669

    Google Scholar 

  80. Hagmann P, Cammoun L, Gigandet X et al (2010) MR connectomics: principles and challenges. J Neurosci Methods 194:34–45

    Article  PubMed  Google Scholar 

  81. Wee CY, Yap PT, Li W et al (2011) Enriched white matter connectivity networks for accurate identification of MCI patients. Neuroimage 54:1812–1822

    Article  PubMed  Google Scholar 

  82. Tymofiyeva O, Hess CP, Ziv E et al (2012) Towards the “baby connectome”: mapping the structural connectivity of the newborn brain. PLoS One 7:e31029

    Article  PubMed  CAS  Google Scholar 

  83. Pannek K, Mathias JL, Bigler ED et al (2011) The average pathlength map: a diffusion MRI tractography-derived index for studying brain pathology. Neuroimage 55:133–141

    Article  PubMed  Google Scholar 

  84. Pannek K, Mathias JL, Rose S (2011) MRI diffusion indices sampled along streamline trajectories: quantitative tractography mapping. Brain Connectivity 1:331–338

    Article  PubMed  Google Scholar 

  85. Calamante F, Tournier JD, Smith RE et al (2012) A generalised framework for super-resolution track-weighted imaging. Neuroimage 59:2494-503

    Google Scholar 

  86. Jbabdi S, Johansen-Berg H (2011) Tractography–where do we go from here? Brain Connectivity 1:169–183

    Article  PubMed  Google Scholar 

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Pannek, K., Guzzetta, A., Colditz, P.B. et al. Diffusion MRI of the neonate brain: acquisition, processing and analysis techniques. Pediatr Radiol 42, 1169–1182 (2012). https://doi.org/10.1007/s00247-012-2427-x

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