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In-utero Three Dimension High Resolution Fetal Brain Diffusion Tensor Imaging

  • Shuzhou Jiang
  • Hui Xue
  • Serena J. Counsell
  • Mustafa Anjari
  • Joanna Allsop
  • Mary A. Rutherford
  • Daniel Rueckert
  • Joseph V. Hajnal
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4791)

Abstract

We present a methodology to achieve 3D high resolution in-utero fetal brain DTI that shows excellent ADC as well as promising FA maps. After continuous DTI scanning to acquire a repeated series of parallel slices with 15 diffusion directions, image registration is used to realign the images to correct for fetal motion. Once aligned, the diffusion images are treated as irregularly sampled data where each voxel is associated with an appropriately rotated diffusion direction, and used to estimate the diffusion tensor on a regular grid. The method has been tested successful on eight fetuses and has been validated on adults imaged at 1.5T.

Keywords

Apparent Diffusion Coefficient Diffusion Tensor Imaging Diffusion Tensor Fetal Brain Fetal Motion 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Righini, A., et al.: Apparent diffusion coefficient determination in normal fetal brain: a prenatal MR imaging study. AJNR 24, 799–804 (2003)Google Scholar
  2. 2.
    Prayer, D., et al.: MRI of normal fetal brain development. Eur. J. Radiol. 57(2), 199–216 (2006)CrossRefGoogle Scholar
  3. 3.
    Bui, T., et al.: Microstructural development of human brain assessed in utero by diffusion tensor imaging. Pediatr Radio 36, 1133–1140 (2006)CrossRefGoogle Scholar
  4. 4.
    Rousseau, F., et al.: A novel approach to high resolution fetal brain MR imaging. In: Duncan, J.S., Gerig, G. (eds.) MICCAI 2005. LNCS, vol. 3749, pp. 548–555. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Jiang, S., et al.: A novel approach to accurate 3D high resolution and high SNR fetal brain imaging. In: ISBI 2006, pp. 662–665 (2006)Google Scholar
  6. 6.
    Jiang, S., et al.: MRI of moving subjects using multi-slice Snapshot images with Volume Reconstruction (SVR): application to fetal, neonatal and adult brain studies. IEEE Tran. Medical Imaging 26(7), 967–980 (2007)CrossRefGoogle Scholar
  7. 7.
    Studholme, C., et al.: An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition 32(1), 71–86 (1999)CrossRefGoogle Scholar
  8. 8.
    Jenkinson, M., et al.: Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage 17, 825–841 (2002)CrossRefGoogle Scholar
  9. 9.
    Lee, S., et al.: Scattered data interpolation with multilevel B-splines. IEEE Trans. Visualization Comput. Graph. 3, 228–244 (1997)CrossRefGoogle Scholar
  10. 10.
    Paige, C.C., et al.: LSQR: An Algorithm for Sparse Linear Equations and Sparse Least Squares. ACM Transactions on Mathematical Software (TOMS) 8(1), 43–71 (1982)zbMATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Partridge, S.C., et al.: Diffusion tensor imaging: serial quantitation of white matter tract maturity in premature newborns. NeuroImage 22, 1302–1314 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Shuzhou Jiang
    • 1
  • Hui Xue
    • 1
    • 2
  • Serena J. Counsell
    • 1
  • Mustafa Anjari
    • 1
  • Joanna Allsop
    • 1
  • Mary A. Rutherford
    • 1
  • Daniel Rueckert
    • 2
  • Joseph V. Hajnal
    • 1
  1. 1.Imaging Sciences Department, MRC Clinical Sciences Centre, Hammersmith Hospital, Imperial College London, LondonUnited Kingdom
  2. 2.Department of Computing, Imperial College London, LondonUnited Kingdom

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