TY - GEN
T1 - Generalized diffusion tractography based on directional data clustering
AU - da Silva, Adelino R.Ferreira
PY - 2014
Y1 - 2014
N2 - A new methodology to reduce uncertainty in estimating the orientation of neuronal pathways in diffusion magnetic resonance imaging is proposed. The methodology relies on three main features. First, an optimized high angular resolution diffusion imaging reconstruction technique is adopted. For each voxel, the orientation distribution function (ODF) on the unit sphere is reconstructed to extract the principal diffusion directions. Second, directional statistics are used to estimate the principal ODF profile directions from data distributed on the unit sphere. For this purpose, a mixture-model approach to clustering directional data based on von Mises-Fisher distributions is adopted. Third, a modified streamline algorithm able to accommodate multiple fiber tracts and multiple orientations per voxel is used, to exploit the directional information gathered from estimated ODF profiles. The methodology has been tested on synthetic data simulations of crossing fibers and on a real data set.
AB - A new methodology to reduce uncertainty in estimating the orientation of neuronal pathways in diffusion magnetic resonance imaging is proposed. The methodology relies on three main features. First, an optimized high angular resolution diffusion imaging reconstruction technique is adopted. For each voxel, the orientation distribution function (ODF) on the unit sphere is reconstructed to extract the principal diffusion directions. Second, directional statistics are used to estimate the principal ODF profile directions from data distributed on the unit sphere. For this purpose, a mixture-model approach to clustering directional data based on von Mises-Fisher distributions is adopted. Third, a modified streamline algorithm able to accommodate multiple fiber tracts and multiple orientations per voxel is used, to exploit the directional information gathered from estimated ODF profiles. The methodology has been tested on synthetic data simulations of crossing fibers and on a real data set.
KW - Fiber tractography
KW - Generalized q-Sampling Imaging (GQI)
KW - High Angular Resolution Diffusion Imaging (HARDI)
KW - Von Mises-Fisher distributions
UR - http://www.scopus.com/inward/record.url?scp=84911032348&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-11271-8_20
DO - 10.1007/978-3-319-11271-8_20
M3 - Conference contribution
AN - SCOPUS:84911032348
T3 - Studies in Computational Intelligence
SP - 311
EP - 320
BT - Computational Intelligence - International Joint Conference, IJCCI 2012, Revised Selected Papers
A2 - Correia, António Dourado
A2 - Rosa, Agostinho C.
A2 - Madani, Kurosh
A2 - Filipe, Joaquim
PB - Springer Verlag
T2 - 4th International Joint Conference on Computational Intelligence, IJCCI 2012
Y2 - 5 October 2012 through 7 October 2012
ER -