Diffusion MRI Analysis in 3D SlicerCarl-Fredrik Westin1,2, Steven Pieper1,2, Marek Kubicki1,3, Ron Kikinis1,2

1 Laboratory of Mathematics in Imaging, Brigham and Women’s Hospital, Harvard Medical School, Boston MA, USA2 Surgical Planning Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston MA, USA

3 Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston MA, USA

Background Tools

3D Slicer, http:www.slicer.org

• 3D Slicer is a multi-platform, free open source software (FOSS)

• History: Slicer was initiated as a masters thesis project between the Surgical Planning Laboratory at the Brigham and Women's Hospital and the MIT Artificial Intelligence Laboratory in 1998. Slicer has been downloaded many thousand times. Since 2005, 3D Slicer is a national effort supported by NA-MIC, and several other NIH grants.

• Sophisticated complex visualization capabilities.

• Multi-platform support: pre-compiled binaries for Windows, Mac OS X, and Linux.

• Extensive support for image guided therapy and diffusion tensor imaging.

• Advanced registration / data fusion capabilities.

• 3D Slicer consists of more than 550 thousand lines of code, mostly C++. This massive software development effort has been enabled by the participation of several large scale NIH funded efforts, including the NA-MIC, NAC, BIRN, CIMIT and NCIGT communities.

• The funding support comes from several federal funding sources including NCRR, NIBIB, NIH Roadmap, NCI, NSF and the DOD as well as others.

• In April of 2009 version 3.3 of Slicer was released.

• Noise reduction of diffusion weighted images• Tensor estimation• Spatial transforms• Resampling of tensor volumes• ROI tools• Tractography

•Probe-based real-time tractograohy •Region-based (volumes and surfaces) tractography•Stochastic tractography•Fiber clustering

• Sophisticated composite rendering of stream-lines and glyphs.

• Scene description framework, save multiple states

Schizophrenia• Neuroimaging studies over the last two decades have led to

major progress in delineating gray matter abnormalities in schizophrenia. By comparison, far less is known about white matter abnormalities, especially those affecting white matter tracts that connect the frontal and temporal lobes, tracts that have long been thought to be abnormal in schizophrenia.

Neurosurgery

References

Sylvain Bouix, Guido Gerig, Alex Golby, Polina Golland, Randy Gollub, Casey Goodlett, Nathan Hageman, Nobuhiko Hata, Jean-Jaques Lemaire, Haying Liu, James Miller, Marc Niethammer, Isaiah Norton, Lauren O’Donnell, Xenios Papademetris, Sonia Pujol, Raul San-Jose, Martha Shenton, Julien von Siebenthal, Allen Tannenbaum, Demian Wasserman, Ross Whitaker, Alex Yarmarcovich

Applications

Acknowledgements

Applications

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NIH R01 MH074794

NIH U54 EB005149

NIH P41 RR13218

NIH U41 RR019703

O'Donnell LJ, Westin CF. Automatic tractography segmentation using a high-dimensional white matter atlas. IEEE Trans Med Imaging 2007;26(11):1562-1575.

Rosenberger G, Kubicki M, Nestor PG, Connor E, Bushell G, Markant D, Niznikiewicz M, Westin CF, Kikinis R, Saykin A, McCarley R, Shenton ME. Age-related deficits in fronto-temporal connections in schizophrenia: A diffusion tensor imaging study. Schizophr Res 2008;102(1-3):181-188.

Friman O, Farneback G, Westin CF. A Bayesian approach for stochastic white matter tractography. TMI 2006;25(8):965-978.