TY - JOUR
T1 - Probabilistic inference on Q-ball imaging data
AU - Fonteijn, Hubert M.J.
AU - Verstraten, Frans A.J.
AU - Norris, David G.
N1 - Funding Information:
Manuscript received May 15, 2007; revised August 21, 2007. This work was supported by a Pionier Grant from the Netherlands Organization for Scientific Research (NWO). Asterisk indicates corresponding author. *H. M. J. Fonteijn is with the Helmholtz Institute, Universiteit Utrecht, 3584 CS Utrecht, The Netherlands and with the F. C. Donders Centre for Cognitive Neuroimaging, 6500 HB Nijmegen, The Netherlands (e-mail: [email protected]). F. A. J. Verstraten is with the Helmholtz Institute, Universiteit Utrecht, 3584 CS Utrecht, The Netherlands (e-mail: [email protected]). D. G. Norris with the F. C. Donders Centre for Cognitive Neuroimaging, 6500 HB Nijmegen, The Netherlands (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TMI.2007.907297
PY - 2007/11
Y1 - 2007/11
N2 - Diffusion-weighted magnetic resonance imaging (MRI) and especially diffusion tensor imaging (DTI) have proven to be useful for the characterization of the microstructure of brain white matter structures in vivo. However, DTI suffers from a number of limitations in characterizing more complex situations. The most notable problem occurs when multiple fibre bundles are present within a voxel. In this paper, we have expanded the existing Q-ball imaging method to a Bayesian framework in order to fully characterize the uncertainty around the fibre directions, given the quality of the data. We have done this by using a recently proposed spherical harmonics decomposition of the diffusion-weighted signal and the resulting Q-ball orientation distribution function. Moreover, we have incorporated a model selection procedure which determines the appropriate smoothness of the orientation distribution function from the data. We show by simulation that our framework can indeed characterize the posterior probability of the fibre directions in cases with multiple fibre populations per voxel and have provided examples of the algorithm's performance on real data where this situation is known to occur.
AB - Diffusion-weighted magnetic resonance imaging (MRI) and especially diffusion tensor imaging (DTI) have proven to be useful for the characterization of the microstructure of brain white matter structures in vivo. However, DTI suffers from a number of limitations in characterizing more complex situations. The most notable problem occurs when multiple fibre bundles are present within a voxel. In this paper, we have expanded the existing Q-ball imaging method to a Bayesian framework in order to fully characterize the uncertainty around the fibre directions, given the quality of the data. We have done this by using a recently proposed spherical harmonics decomposition of the diffusion-weighted signal and the resulting Q-ball orientation distribution function. Moreover, we have incorporated a model selection procedure which determines the appropriate smoothness of the orientation distribution function from the data. We show by simulation that our framework can indeed characterize the posterior probability of the fibre directions in cases with multiple fibre populations per voxel and have provided examples of the algorithm's performance on real data where this situation is known to occur.
KW - Bayesian analysis
KW - Crossing fibres
KW - Diffusion imaging
KW - Q-ball imaging
KW - n/a OA procedure
UR - http://www.scopus.com/inward/record.url?scp=35648986677&partnerID=8YFLogxK
U2 - 10.1109/TMI.2007.907297
DO - 10.1109/TMI.2007.907297
M3 - Article
C2 - 18041266
AN - SCOPUS:35648986677
SN - 0278-0062
VL - 26
SP - 1515
EP - 1524
JO - IEEE transactions on medical imaging
JF - IEEE transactions on medical imaging
IS - 11
ER -