A normal distribution for tensor-valued random variables: applications to diffusion tensor MRI. Export

@article{citeulike:3175312, abstract = {Diffusion tensor magnetic resonance imaging (DT-MRI) provides a statistical estimate of a symmetric, second-order diffusion tensor of water, D, in each voxel within an imaging volume. We propose a new normal distribution, p(D) alpha exp(-1/2 D: A: D), which describes the variability of D in an ideal DT-MRI experiment. The scalar invariant, D : A : D, is the contraction of a positive definite symmetric, fourth-order precision tensor, A, and D. A correspondence is established between D: A: D and the elastic strain energy density function in continuum mechanics--specifically between D and the second-order infinitesimal strain tensor, and between A and the fourth-order tensor of elastic coefficients. We show that A can be further classified according to different classical elastic symmetries (i.e., isotropy, transverse isotropy, orthotropy, planar symmetry, and anisotropy). When A is an isotropic fourth-order tensor, we derive an explicit analytic expression for p(D), and for the distribution of the three eigenvalues of D, p(gamma1, gamma2, gamma3), which are confirmed by Monte Carlo simulations. We show how A can be estimated from either real or synthetic DT-MRI data for any given experimental design. Here we propose a new criterion for an optimal experimental design: that A be an isotropic fourth-order tensor. This condition ensures that the statistical properties of D (and quantities derived from it) are rotationally invariant. We also investigate the degree of isotropy of several DT-MRI experimental designs. Finally, we show that the univariate and multivariate distributions are special cases of the more general tensor-variate normal distribution, and suggest how to generalize p(D) to treat normal random tensor variables that are of third- (or higher) order. We expect that this new distribution, p(D), should be useful in feature extraction; in developing a hypothesis testing framework for segmenting and classifying noisy, discrete tensor data; and in designing experiments to measure tensor quantities.}, address = {STBB/LIMB/NICHD, National Institutes of Health, Bldg. 13, Rm. 3W 16, 13 South Drive, Bethesda, MD 20892-5772, USA. pjbasser@helix.nih.gov}, author = {Basser, P. J. and Pajevic, S.}, citeulike-article-id = {3175312}, citeulike-linkout-0 = {http://dx.doi.org/10.1109/TMI.2003.815059}, citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/12906233}, citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=12906233}, doi = {10.1109/TMI.2003.815059}, issn = {0278-0062}, journal = {IEEE transactions on medical imaging}, keywords = {basser\_pj, pajevic\_s, random, tensor-valued, variables}, month = {July}, number = {7}, pages = {785--794}, posted-at = {2009-06-30 21:06:26}, priority = {2}, title = {A normal distribution for tensor-valued random variables: applications to diffusion tensor MRI.}, url = {http://dx.doi.org/10.1109/TMI.2003.815059}, volume = {22}, year = {2003} }

TY - JOUR ID - citeulike:3175312 L3 - citeulike-article-id:3175312 N2 - Diffusion tensor magnetic resonance imaging (DT-MRI) provides a statistical estimate of a symmetric, second-order diffusion tensor of water, D, in each voxel within an imaging volume. We propose a new normal distribution, p(D) alpha exp(-1/2 D: A: D), which describes the variability of D in an ideal DT-MRI experiment. The scalar invariant, D : A : D, is the contraction of a positive definite symmetric, fourth-order precision tensor, A, and D. A correspondence is established between D: A: D and the elastic strain energy density function in continuum mechanics--specifically between D and the second-order infinitesimal strain tensor, and between A and the fourth-order tensor of elastic coefficients. We show that A can be further classified according to different classical elastic symmetries (i.e., isotropy, transverse isotropy, orthotropy, planar symmetry, and anisotropy). When A is an isotropic fourth-order tensor, we derive an explicit analytic expression for p(D), and for the distribution of the three eigenvalues of D, p(gamma1, gamma2, gamma3), which are confirmed by Monte Carlo simulations. We show how A can be estimated from either real or synthetic DT-MRI data for any given experimental design. Here we propose a new criterion for an optimal experimental design: that A be an isotropic fourth-order tensor. This condition ensures that the statistical properties of D (and quantities derived from it) are rotationally invariant. We also investigate the degree of isotropy of several DT-MRI experimental designs. Finally, we show that the univariate and multivariate distributions are special cases of the more general tensor-variate normal distribution, and suggest how to generalize p(D) to treat normal random tensor variables that are of third- (or higher) order. We expect that this new distribution, p(D), should be useful in feature extraction; in developing a hypothesis testing framework for segmenting and classifying noisy, discrete tensor data; and in designing experiments to measure tensor quantities. IS - 7 JF - IEEE transactions on medical imaging SN - 0278-0062 AD - STBB/LIMB/NICHD, National Institutes of Health, Bldg. 13, Rm. 3W 16, 13 South Drive, Bethesda, MD 20892-5772, USA. pjbasser@helix.nih.gov EP - 794 TI - A normal distribution for tensor-valued random variables: applications to diffusion tensor MRI. VL - 22 SP - 785 KW - basser_pj KW - pajevic_s KW - random KW - tensor-valued KW - variables AU - Basser, PJ AU - Pajevic, S PY - 2003/07// UR - http://dx.doi.org/10.1109/TMI.2003.815059 ER -