MITK Diffusion Imaging

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), MITK-DI, 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 installable 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.

[1]  D. Arciniegas,et al.  Diffusion tensor imaging in mild traumatic brain injury litigation. , 2011, The journal of the American Academy of Psychiatry and the Law.

[2]  Heinz-Otto Peitgen,et al.  Diffusion tensor imaging in primary brain tumors: Reproducible quantitative analysis of corpus callosum infiltration and contralateral involvement using a probabilistic mixture model , 2006, NeuroImage.

[3]  E. Gasparetto,et al.  Diffusion MR imaging in central nervous system. , 2011, Magnetic resonance imaging clinics of North America.

[4]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[5]  G. Sapiro,et al.  Reconstruction of the orientation distribution function in single‐ and multiple‐shell q‐ball imaging within constant solid angle , 2010, Magnetic resonance in medicine.

[6]  Bram Stieltjes,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 , 2009, Investigative radiology.

[7]  H. Thoeny.  Diffusion-weighted MRI in head and neck radiology: applications in oncology , 2011, Cancer imaging : the official publication of the International Cancer Imaging Society.

[8]  R. Deriche,et al.  Regularized, fast, and robust analytical Q‐ball imaging , 2007, Magnetic resonance in medicine.

[9]  Alan Connelly,et al.  Track-density imaging (TDI): Super-resolution white matter imaging using whole-brain track-density mapping , 2010, NeuroImage.

[10]  V. Kiselev,et al.  Gibbs tracking: A novel approach for the reconstruction of neuronal pathways , 2008, Magnetic resonance in medicine.

[11]  P. Grenier,et al.  MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. , 1986, Radiology.

[12]  Daniel Rueckert,et al.  Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data , 2006, NeuroImage.

[13]  Marco Nolden,et al.  The Medical Imaging Interaction Toolkit , 2005, Medical Image Anal..

[14]  References , 1971 .

[15]  Susanne Schnell,et al.  Global fiber reconstruction becomes practical , 2011, NeuroImage.

[16]  Marco Nolden,et al.  Interactive segmentation framework of the Medical Imaging Interaction Toolkit , 2009, Comput. Methods Programs Biomed..

[17]  M. Albert,et al.  DTI analyses and clinical applications in Alzheimer's disease. , 2011, Journal of Alzheimer's disease : JAD.

[18]  Susumu Mori,et al.  Fiber tracking: principles and strategies – a technical review , 2002, NMR in biomedicine.

[19]  D. Tuch.  Q‐ball imaging , 2004, Magnetic resonance in medicine.

[20]  P. Basser,et al.  Estimation of the effective self-diffusion tensor from the NMR spin echo. , 1994, Journal of magnetic resonance. Series B.

[21]  Mara Cercignani,et al.  Twenty‐five pitfalls in the analysis of diffusion MRI data , 2010, NMR in biomedicine.