A global probabilistic fiber tracking approach based on the voting process provided by the Hough transform is introduced in this work. The proposed framework tests candidate 3D curves in the volume, assigning to each one a score computed from the diffusion images, and then selects the curves with the highest scores as the potential anatomical connections. The algorithm avoids local minima by performing an exhaustive search at the desired resolution. The technique is easily extended to multiple subjects, considering a single representative volume where the registered high-angular resolution diffusion images (HARDI) from all the subjects are non-linearly combined, thereby obtaining population-representative tracts. The tractography algorithm is run only once for the multiple subjects, and no tract alignment is necessary. We present experimental results on HARDI volumes, ranging from simulated and 1.5T physical phantoms to 7T and 4T human brain and 7T monkey brain datasets.
Bibliographical noteFunding Information:
This work was partly supported by NIH (P41 RR008079, P30 NS057091, R01 HD050735, R01 EB007813, R01 MH060662, R01 EB008432, R01 EB008645, CON000000004051–3014, CON000000015793–3014, NLM T15 LM07356), NSF, ONR, NGA, ARO, DARPA, and the University of Minnesota Institute for Translational Neuroscience. Computing resources were provided by the University of Minnesota Supercomputing Institute, and the Laboratory of Neuro Imaging (UCLA). We would like to thank Jennifer Campbell of McGill University, Katie L. McMahon and Greig I. de Zubicaray of the Centre for Magnetic Resonance, University of Queensland, and Margaret J. Wright of Queensland Institute of Medical Research for providing us with additional HARDI data.
- Diffusion-weighted magnetic resonance imaging (DWI)
- Hough transform
- Orientation distribution function (ODF)
- Population studies