Diffusion MRI Analysis in 3 D Slicer 1

Diffusion MRI Analysis in 3 D Slicer 1, 2, Steven Pieper 1, 2, Marek Kubicki 1, 3, Ron Kikinis 1, 2 Carl-Fredrik Westin 1 Laboratory of Mathematics in Imaging, Brigham and Women’s Hospital, Harvard Medical School, Boston MA, USA 2 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 Acknowledgements 3 D Slicer, http: www. slicer. org • • • 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 • 3 D 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, 3 D 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. • 3 D 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. 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 Applications Neurosurgery Applications 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. • 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. NIH R 01 MH 074794 NIH P 41 RR 13218 NIH U 41 RR 019703 NIH U 54 EB 005149 References O'Donnell LJ, Westin CF. Automatic tractography segmentation using a highdimensional white matter atlas. IEEE Trans Med Imaging 2007; 26(11): 1562 -1575. Friman O, Farneback G, Westin CF. A Bayesian approach for stochastic white matter tractography. TMI 2006; 25(8): 965 -978. Rosenberger G, Kubicki M, Nestor PG, Connor E, Bushell G, Markant D, Niznikiewicz M, Westin CF, Kikinis R, Saykin A, Mc. Carley 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.