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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
