Diffusion Tensor Imaging Category

Comparison between Traditional DTI and CFARI at SNR of 25:1 and b-value of 1000 s/mm2. (a) Traditional DTI, where fibers having FA<=0.25 are considered to be isotropic component (shown in black). (b) CFARI can resolve multiple crossing fibers.

Effect of Regularization Parameter and Scan Time on Crossing Fibers with Constrained Compressed Sensing

Feb. 1, 2011—Fatma Elzahraa A. ElShahaby, Bennett A. Landman, and Jerry L. Prince. “Effect of Regularization Parameter and Scan Time on Crossing Fibers with Constrained Compressed Sensing”, In Proceedings of the SPIE Medical Imaging Conference. Lake Buena Vista, Florida, February 2011 PMC3087384 † Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3087384/ Abstract Diffusion tensor imaging (DTI) is an MR imaging technique that...

Intra-voxel orientations estimated with in vivo data show patterns consistent with anatomy, which can be clearly appreciated in the region of the corpus callosum (cc) and internal capsule (ic).

“Resolution of Crossing Fibers with Constrained Compressed Sensing using Traditional Diffusion Tensor MRI”

Feb. 12, 2010—A. Landman, H. Wan, J. Bogovic, P.-L. Bazin, and J. L. Prince. “Resolution of Crossing Fibers with Constrained Compressed Sensing using Traditional Diffusion Tensor MRI”, In Proceedings of the SPIE Medical Imaging Conference. San Diego, CA, February 2010 (Oral Presentation) PMC2854412 Full text Abstract Diffusion tensor imaging (DTI) is widely used to characterize tissue micro-architecture...

Simulations of free diffusion. Error between theoretical and simulated diffusion probability-density functions was minimized with 5–10 time-steps when the number of spins × the number of time-steps was held constant. Curves show mean error ± standard error for three diffusion times.

Complex Geometric Models of Diffusion and Relaxation in Healthy and Damaged White Matter

Jan. 31, 2010—. B. A. Landman, J. A.D. Farrell, S. A. Smith, D. Reich, P. Calabresi, and P. C.M. van Zijl, “Complex Geometric Models of Diffusion and Relaxation in Healthy and Damaged White Matter”, NMR in Biomed, Volume 23 Issue 2, Pages 152 – 162. (2010). PMC2838925 † Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2838925/ Abstract Which aspects of tissue microstructure...

Simulation results. In the idealized simulation (left), the background estimator (BE) method performed better than other methods both in terms of mean squared error (first row) and variability (second row). In the simulation incorporating spatial variability and artifacts (right), the proposed spatially regularized robust (SRR) method greatly exceeded the performance of the standard approaches.

Estimation and application of spatially variable noise fields in diffusion tensor imaging.

Feb. 28, 2009—B. A. Landman, P-L Bazin, and J. L. Prince, “Estimation and Application of Spatially Variable Noise Fields in Diffusion Tensor Imaging”, Magnetic Resonance Imaging, Volume 27, Issue 6, Pages 741-751. (2009) PMC2733233 Full text: https://www.ncbi.nlm.nih.gov/pubmed/19250784 Abstract Optimal interpretation of magnetic resonance image content often requires an estimate of the underlying image noise, which is typically...

Interpolation of the genu of the Corpus Callosum (CC) and longitudinal fasciculus
(LF) near the lateral ventricle (V): region of interest (ROI) selected on an
orientation color coded anisotropy map (A,B). MD (C) and log-determinant (D) are
shown at the reconstructed resolution. Upsampled (4 × 4) and interpolated contrasts
are shown for linear (E-G), Log-Euclidean (H-J), and CLEAR (K-M) tensor interpolation.
While the linear interpolation has smooth FA and MD (E,F), it exhibits abrupt
changes in the determinant (G). The Log-Euclidean interpolation exhibits FA artifacts
in regions of high anisotropy and curvature (H). CLEAR avoids all these problems

Tensor Interpolation by Concise Local Estimation of Anisotropy and Rotation

Sep. 1, 2008—A. Landman, P-L. Bazin, and J. L. Prince. “Tensor Interpolation by Concise Local Estimation of Anisotropy and Rotation”, In Proceedings of the Workshop on Computational Diffusion MRI at the 11th International Conference on Medical Image Computing and Computer Assisted Intervention, New York, NY, September 2008. Full Text:http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.701.4262&rep=rep1&type=pdf#page=265 Abstract In diffusion tensor imaging, tensors represent the...

CFARI identified consistent intra-voxel structure in the crossing fibers between
the corpus callosum (CC, lower left, red) and internal capsule (IC, blue, lower right).
Conventional tensor modeling does not preserve the connectivity of the corpus callosum
to the lateral hemispheres

Compressed Sensing of Multiple Intra-Voxel Orientations with Traditional DTI

Sep. 1, 2008—A. Landman, J. Bogovic, and J. L. Prince. “Compressed Sensing of Multiple Intra-Voxel Orientations with Traditional DTI”, In Proceedings of the Workshop on Computational Diffusion MRI at the 11th International Conference on Medical Image Computing and Computer Assisted Intervention, New York, NY, September 2008. Full Text:http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.701.4262&rep=rep1&type=pdf#page=183 Abstract Diffusion tensor imaging (DTI) is widely used to...

Schematic of reliability estimation with experimental data (upper panel) and simulated data (lower panel).

Diffusion Tensor Imaging at Low SNR: Non-monotonic behaviors of tensor contrasts

Jul. 31, 2008—B. A. Landman, J. Farrell, H. Huang, J. Prince, and S. Mori. “Diffusion Tensor Imaging at Low SNR: Non-monotonic behaviors of tensor contrasts”, Magnetic Resonance Imaging. 26(6):790-800. July 2008 PMC2583784 Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2583784/ Abstract Diffusion tensor imaging (DTI) provides measurements of directional diffusivities and has been widely used to characterize changes in tissue micro-architecture in...

Orthogonal views of the Forceps Major delineated (a–b) manually and with our (c–d) automated processing. The semi-transparent cortical outer surface is also shown.

Automatically identifying white matter tracts using cortical labels

May. 1, 2008—A. Bogovic, A. Carass, J. Wan, B. A. Landman, and J. L. Prince. “Automatically identifying white matter tracts using cortical labels,” In Proceedings of the 2008 IEEE International Symposium on Biomedical Imaging, Paris, France, May 2008 PMC2812932 Full Text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2812932/ Abstract Diffusion tensor imaging (DTI) has become a standard clinical procedure in assessing the health...

FA estimates with acquired data. Systematic bias is seen between DTEMRL and LLMMSE when simultaneously using all 15 datasets (A,B). Mean differences (bias) between methods are preserved when single datasets are analyzed (C,D). Variability of DTEMRL is lower throughout the brain (E,F).

Diffusion Tensor Estimation by Maximizing Rician Likelihood

Oct. 1, 2007—B. A. Landman, P-L. Bazin, and J. L. Prince. “Diffusion Tensor Estimation by Maximizing Rician Likelihood”, In Proceedings of the 2007 International Conference on Computer Vision Workshop on Mathematical Methods in Biomedical Image Analysis, Rio de Janeiro, Brazil, October 2007. (Oral Presentation) Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3488430/ Abstract Diffusion tensor imaging (DTI) is widely used to characterize...

Minimum potential energy (PE) partitions of the Jones30 DW scheme. The optimal PE partitions (left) are evenly distributed as indicated by the shading which is proportional to the area of the spherical Voronoi tessellations of the DW directions. The realized directions are distinct (right) from the specified ones (left) because the gradient tables are corrected for subject motion. The right panel shows 30 clusters, where each cluster represents a specified DW direction and consists of three sub-clusters which represent realized DW directions from each session. The separation of the sub-clusters shows the inter-session effects, while the distribution of the symbols shows the intra-session effects. Large ovals indicate the subset of the Jones30 that was used to construct the PE6 partition.

Effects of diffusion weighting schemes on the reproducibility of DTI-derived fractional anisotropy, mean diffusivity, and principal eigenvector measurements at 1.5T.

Apr. 30, 2007—B. A. Landman, J. A. Farrell, C. K. Jones, S. A. Smith, J. L. Prince, P. C. van Zijl, and S. Mori. “Effects of Diffusion Weighting Schemes on the Reproducibility of DTI-derived Fractional Anisotropy, Mean Diffusivity, and Principal Eigenvector Measurements at 1.5T”, NeuroImage. 36(4): 1123-1138. July 2007. PMID17532649 Full text: https://www.ncbi.nlm.nih.gov/pubmed/17532649 Abstract Diffusion tensor imaging...