Download PDF
Methods Inf Med 2012; 51(05): 441-448
DOI: 10.3414/ME11-02-0031
Focus Theme – Original Articles
Schattauer GmbH

MITK Diffusion Imaging

K. H. Fritzsche
1   German Cancer Research Centre, Division of Medical and Biological Informatics, Heidelberg, Germany
2   German Cancer Research Center, Section Quantitative Imaging-based Disease Characterization, Heidelberg, Germany
,
P. F. Neher
1   German Cancer Research Centre, Division of Medical and Biological Informatics, Heidelberg, Germany
,
I. Reicht
1   German Cancer Research Centre, Division of Medical and Biological Informatics, Heidelberg, Germany
,
T. van Bruggen
1   German Cancer Research Centre, Division of Medical and Biological Informatics, Heidelberg, Germany
,
C. Goch
1   German Cancer Research Centre, Division of Medical and Biological Informatics, Heidelberg, Germany
,
M. Reisert
3   University Hospital Freiburg, Department of Radiology, Medical Physics, Freiburg, Germany
,
M. Nolden
1   German Cancer Research Centre, Division of Medical and Biological Informatics, Heidelberg, Germany
,
S. Zelzer
1   German Cancer Research Centre, Division of Medical and Biological Informatics, Heidelberg, Germany
,
H.-P. Meinzer
1   German Cancer Research Centre, Division of Medical and Biological Informatics, Heidelberg, Germany
,
B. Stieltjes
2   German Cancer Research Center, Section Quantitative Imaging-based Disease Characterization, Heidelberg, Germany
› Author Affiliations
Further Information

Publication History

received:13 October 2011

accepted:23 March 2012

Publication Date:
20 January 2018 (online)

    Permissions and Reprints

Summary

Background: Diffusion-MRI provides a unique window on brain anatomy and insights into aspects of tissue structure in living humans that could not be studied previously. There is a major effort in this rapidly evolving field of research to develop the algorithmic tools necessary to cope with the complexity of the datasets.

Objectives: This work illustrates our strategy that encompasses the development of a modularized and open software tool for data processing, visualization and interactive exploration in diffusion imaging research and aims at reinforcing sustainable evaluation and progress in the field.

Methods: In this paper, the usability and capabilities of a new application and toolkit component of the Medical Imaging and Interaction Toolkit (MITK, www.mitk.org), MITKDI, are demonstrated using in-vivo datasets.

Results: MITK-DI provides a comprehensive software framework for high-performance data processing, analysis and interactive data exploration, which is designed in a modular, extensible fashion (using CTK) and in adherence to widely accepted coding standards (e.g. ITK, VTK). MITK-DI is available both as an open source software development toolkit and as a ready-to-use in stallable application.

Conclusions: The open source release of the modular MITK-DI tools will increase verifiability and comparability within the research community and will also be an important step towards bringing many of the current techniques towards clinical application.

Keywords

Diffusion tensor imaging - Q-ball imaging - open-source software toolkit - tractography - common toolkit CTK
 

  • References

  • 1 Basser PJ, Mattiello J, LeBihan D. Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B 1994; 103 (03) 247-254.
    CrossrefPubMedSearch in Google Scholar
  • 2 de Carvalho Rangel C, Hygino Cruz Jr LC, Takayassu TC, Gasparetto EL, Domingues RC. Diffusion MR imaging in central nervous system. Magn Reson Imaging Clin N Am 2011; 19 (01) 23-53.
    CrossrefPubMedSearch in Google Scholar
  • 3 Wortzel HS, Kraus MF, Filley CM, Anderson CA, Arciniegas DB. Diffusion tensor imaging in mild traumatic brain injury litigation. J Am Acad Psychiatry Law 2011; 39 (04) 511-523.
    PubMedSearch in Google Scholar
  • 4 Oishi K, Mielke MM, Albert M, Lyketsos CG, Mori S. DTI analyses and clinical applications in Alzheimer’s disease. J Alzheimers Dis 2011; 26 (03) 287-296.
    CrossrefPubMedSearch in Google Scholar
  • 5 Thoeny HC. Diffusion-weighted MRI in head and neck radiology: applications in oncology. Cancer Imaging 2011; 10 (01) 209-214.
    CrossrefPubMedSearch in Google Scholar
  • 6 Tuch DS. Q-ball imaging. Magn Reson Med 2004; 52 (06) 1358-1372.
    CrossrefPubMedSearch in Google Scholar
  • 7 Jones DK, Cercignani M. Twenty-five pitfalls in the analysis of diffusion MRI data. NMR in Biomedicine 2010; 23 (07) 803-820.
    CrossrefPubMedSearch in Google Scholar
  • 8 Wolf I, Vetter M, Wegner I, Böttger T, Nolden M, Schöbinger M. et al. The medical imaging interaction toolkit. Med Image Anal 2005; 9 (06) 594-604.
    CrossrefPubMedSearch in Google Scholar
  • 9 Fritzsche KH, Meinzer HP. Numerical Q-Ball Image Reconstruction: an ITK Implementation. The Insight Journal. 2009: 643
    Search in Google Scholar
  • 10 Descoteaux M, Angelino E, Fitzgibbons S, Deriche R. Regularized, Fast and Robust Analytical Q-Ball Imaging. Mag Res Med 2007; 58 (03) 497-510.
    CrossrefPubMedSearch in Google Scholar
  • 11 Aganj I, Lenglet C, Sapiro G, Yacoub E, Ugurbil K, Harel N. Reconstruction of the orientation distribution function in single and multiple shell q-ball imaging within constant solid angle. Mag Res Med 2010; 64 (02) 554-566.
    PubMedSearch in Google Scholar
  • 12 Calamante F, Tournier JD, Jackson GD, Connelly A. Track-density imaging (TDI): super-resolution white matter imaging using whole-brain track-density mapping. Neuroimage 2010; 53 (04) 1233-1243.
    CrossrefPubMedSearch in Google Scholar
  • 13 Maleike D, Nolden M, Meinzer HP, Wolf I. Interactive segmentation framework of the Medical Imaging Interaction Toolkit. Comput Methods Programs Biomed 2009; 96 (01) 72-83.
    CrossrefPubMedSearch in Google Scholar
  • 14 Stieltjes B, Schlüter M, Didinger B, Weber MA, Hahn HK, Parzer P. et al. Diffusion tensor imaging in primary brain tumors: reproducible quantitative analysis of corpus callosum infiltration and contralateral involvement using a probabilistic mixture model. Neuroimage 2006; 31 (02) 531-542.
    CrossrefPubMedSearch in Google Scholar
  • 15 Mori S, van Zijl PCM. Fiber tracking: principles and strategies - a technical review. NMR Biomed 2002; 15: 468-480.
    CrossrefPubMedSearch in Google Scholar
  • 16 Kreher BW, Mader I, Kiselev VG. Gibbs Tracking: A Novel Approach for the Reconstruction of Neuronal Pathways. Mag Res Med 2008; 60: 953-963.
    CrossrefPubMedSearch in Google Scholar
  • 17 Reisert M, Mader I, Anastasopoulos C, Weigel M, Schnell S, Kiselev V. Global fiber reconstruction becomes practical. Neuroimage 2011; 54: 955-962.
    CrossrefPubMedSearch in Google Scholar
  • 18 Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet M. MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology 1986; 161 (02) 401-407.
    CrossrefPubMedSearch in Google Scholar
  • 19 Lemke A, Laun FB, Klauss M, Re TJ, Simon D, Delorme S. et al. Differentiation of pancreas carcinoma from healthy pancreatic tissue using multiple b-values: comparison of apparent diffusion coefficient and intravoxel incoherent motion derived parameters. Invest Radiol 2009; 44 (12) 769-775.
    CrossrefPubMedSearch in Google Scholar
  • 20 Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TEJ, Johansen-Berg H. et al. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage 2004; 23 (01) 208-219.
    CrossrefPubMedSearch in Google Scholar
  • 21 Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE. et al. Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. NeuroImage 2006; 31: 1487-1505.
    CrossrefPubMedSearch in Google Scholar