DTI Analysis Methods: Region of Interest Analysis



Region of interest (ROI) analysis is a widely used method for the analysis of DTI data. An anatomically defined region—either based on anatomical borders or a geometrical shape—is used to extract DTI measures for each subject, which can later be analyzed statistically. ROI analysis can be done either automatically by aligning all subjects to a template, or by manual delineation. In this chapter the basic principles of ROI analysis are discussed, as well as the appropriate use of ROI analysis and potential pitfalls. Finally some examples using real data are shown.


Pros and cons of ROI analysis Atlas-based ROI analysis Effect of motion and size on ROI results 


  1. 1.
    Soares JM, Marques P, et al. A hitchhiker’s guide to diffusion tensor imaging. Front Neurosci. 2013;7:31.PubMedCentralCrossRefPubMedGoogle Scholar
  2. 2.
    Cercignani M. Strategies for patient–control comparison of diffusion MR data. In: Jones DK, editor. Diffusion MRI theory, methods, and applications. New York, NY: Oxford University Press; 2011.Google Scholar
  3. 3.
    Law M, Young R, et al. Histogram analysis versus region of interest analysis of dynamic susceptibility contrast perfusion MR imaging data in the grading of cerebral gliomas. AJNR Am J Neuroradiol. 2007;28(4):761–6.PubMedGoogle Scholar
  4. 4.
    Young R, Babb J, et al. Comparison of region-of-interest analysis with three different histogram analysis methods in the determination of perfusion metrics in patients with brain gliomas. J Magn Reson Imaging. 2007;26(4):1053–63.CrossRefPubMedGoogle Scholar
  5. 5.
    Snook L, Plewes C, et al. Voxel based versus region of interest analysis in diffusion tensor imaging of neurodevelopment. Neuroimage. 2007;34(1):243–52.CrossRefPubMedGoogle Scholar
  6. 6.
    Smith SM, Jenkinson M, et al. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage. 2006;31(4):1487–505.CrossRefPubMedGoogle Scholar
  7. 7.
    Wu O, Dijkhuizen RM, et al. Multiparametric magnetic resonance imaging of brain disorders. Top Magn Reson Imaging. 2010;21(2):129–38.PubMedCentralCrossRefPubMedGoogle Scholar
  8. 8.
    Kleiser R, Staempfli P, et al. Impact of fMRI-guided advanced DTI fiber tracking techniques on their clinical applications in patients with brain tumors. Neuroradiology. 2010;52(1):37–46.CrossRefPubMedGoogle Scholar
  9. 9.
    Mazerolle EL, Beyea SD, et al. Confirming white matter fMRI activation in the corpus callosum: co-localization with DTI tractography. Neuroimage. 2010;50(2):616–21.CrossRefPubMedGoogle Scholar
  10. 10.
    Preti MG, Makris N, et al. A novel approach of fMRI-guided tractography analysis within a group: construction of an fMRI-guided tractographic atlas. Conf Proc IEEE Eng Med Biol Soc. 2010;2012:2283–6.Google Scholar
  11. 11.
    Oishi K, Zilles K, et al. Human brain white matter atlas: identification and assignment of common anatomical structures in superficial white matter. Neuroimage. 2008;43(3):447–57.PubMedCentralCrossRefPubMedGoogle Scholar
  12. 12.
    Eickhoff SB, Stephan KE, et al. A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data. Neuroimage. 2005;25(4):1325–35.CrossRefPubMedGoogle Scholar
  13. 13.
    Irfanoglu MO, Walker L, et al. Effects of image distortions originating from susceptibility variations and concomitant fields on diffusion MRI tractography results. Neuroimage. 2012;61(1):275–88.CrossRefPubMedGoogle Scholar
  14. 14.
    Pajevic S, Basser PJ. Parametric and non-parametric statistical analysis of DT-MRI data. J Magn Reson. 2003;161(1):1–14.CrossRefPubMedGoogle Scholar
  15. 15.
    Vos SB, Jones DK, et al. Partial volume effect as a hidden covariate in DTI analyses. Neuroimage. 2011;55(4):1566–76.CrossRefPubMedGoogle Scholar
  16. 16.
    Jones DK, Cercignani M. Twenty-five pitfalls in the analysis of diffusion MRI data. NMR Biomed. 2010;23(7):803–20.CrossRefPubMedGoogle Scholar
  17. 17.
    Chen NK, Wyrwicz AM. Correction for EPI distortions using multi-echo gradient-echo imaging. Magn Reson Med. 1999;41(6):1206–13.CrossRefPubMedGoogle Scholar
  18. 18.
    Jezzard P, Balaban RS. Correction for geometric distortion in echo planar images from B0 field variations. Magn Reson Med. 1995;34(1):65–73.CrossRefPubMedGoogle Scholar
  19. 19.
    Robson MD, Gore JC, et al. Measurement of the point spread function in MRI using constant time imaging. Magn Reson Med. 1997;38(5):733–40.CrossRefPubMedGoogle Scholar
  20. 20.
    Zeng H, Constable RT. Image distortion correction in EPI: comparison of field mapping with point spread function mapping. Magn Reson Med. 2002;48(1):137–46.CrossRefPubMedGoogle Scholar
  21. 21.
    Andersson JL, Skare S. A model-based method for retrospective correction of geometric distortions in diffusion-weighted EPI. Neuroimage. 2002;16(1):177–99.CrossRefPubMedGoogle Scholar
  22. 22.
    Chang H, Fitzpatrick JM. A technique for accurate magnetic resonance imaging in the presence of field inhomogeneities. IEEE Trans Med Imaging. 1992;11(3):319–29.CrossRefPubMedGoogle Scholar
  23. 23.
    Leemans A, Jeurissen B, et al. ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data. Proceedings 17th Scientific Meeting, International Society for Magnetic Resonance in Medicine, Honolulu; 2009.Google Scholar
  24. 24.
    Evans AC, Janke AL, et al. Brain templates and atlases. Neuroimage. 2012;62(2):911–22.CrossRefPubMedGoogle Scholar
  25. 25.
    Talairach J, Tournoux P. Co-planar stereotaxic atlas of the human brain: 3-dimensional proportional system - an approach to cerebral imaging. New York, NY: Thieme Medical Publishers; 1988.Google Scholar
  26. 26.
    Evans AC, Collins DL, et al. 3D statistical neuroanatomical models from 305 MRI volumes. Nuclear Science Symposium and Medical Imaging Conference, 1993. 1993 IEEE Conference Record; 1993.Google Scholar
  27. 27.
    Tamietto M, Pullens P, et al. Subcortical connections to human amygdala and changes following destruction of the visual cortex. Curr Biol. 2012;22(15):1449–55.CrossRefPubMedGoogle Scholar
  28. 28.
    Arsigny V, Fillard P, et al. Log-Euclidean metrics for fast and simple calculus on diffusion tensors. Magn Reson Med. 2006;56(2):411–21.CrossRefPubMedGoogle Scholar
  29. 29.
    Dupont WD, editor. Statistical modeling for biomedical researchers. Cambridge: Cambridge University Press; 2009.Google Scholar
  30. 30.
    Miller RG, editor. Simultaneous statistical inference. New York, NY: Springer; 1981.Google Scholar
  31. 31.
    Ozturk A, Sasson AD, et al. Regional differences in diffusion tensor imaging measurements: assessment of intrarater and interrater variability. AJNR Am J Neuroradiol. 2008;29(6):1124–7.CrossRefPubMedGoogle Scholar
  32. 32.
    Astrakas LG, Argyropoulou MI. Shifting from region of interest (ROI) to voxel-based analysis in human brain mapping. Pediatr Radiol. 2010;40(12):1857–67.CrossRefPubMedGoogle Scholar
  33. 33.
    Chanraud S, Zahr N, et al. MR diffusion tensor imaging: a window into white matter integrity of the working brain. Neuropsychol Rev. 2010;20(2):209–25.PubMedCentralCrossRefPubMedGoogle Scholar
  34. 34.
    Mukherjee P, Chung SW, et al. Diffusion tensor MR imaging and fiber tractography: technical considerations. AJNR Am J Neuroradiol. 2008;29(5):843–52.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Image Sciences InstituteUniversity Medical Center UtrechtUtrechtThe Netherlands
  2. 2.icometrixLeuvenBelgium
  3. 3.Department of Radiology, Antwerp University HospitalUniversity of AntwerpAntwerpBelgium
  4. 4.PROVIDI LabImage Sciences Institute, University Medical Center UtrechtUtrechtThe Netherlands

Personalised recommendations