1 / 9

Utah NAMIC EAB 2007

Utah NAMIC EAB 2007. Rician Noise and Tensor Estimation. Tensor bias from Rician noise. Utah. Tensor Estimation. ML/MAP tensor estimates Filtering on DW with simultaneous estimates of tensors Complex vs magnitude averaging/estimation Basu et al., MICCAI 2006. Utah.

vivi
Télécharger la présentation

Utah NAMIC EAB 2007

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Utah NAMIC EAB 2007

  2. Rician Noise and Tensor Estimation • Tensor bias from Rician noise Utah

  3. Tensor Estimation • ML/MAP tensor estimates • Filtering on DW with simultaneous estimates of tensors • Complex vs magnitude averaging/estimation • Basu et al., MICCAI 2006 Utah

  4. Volumetric Connectivity • Define paths between ROIs • Analyze circuits (cortex, subcortex) • Work entirely on the grid • Quantify point-wise evidence for connection Utah

  5. Distance along tract Define 3D Paths from ROIs FA FA Utah

  6. Particle Shape Correspondence Utah, UNC, Harvard PNL • No explicit parameterization • Wider class of shapes, no topological constraints • Information theory regularization • Reduced free parameters • Compares favorably against MDL in 2D • Simple examples

  7. Particle 3D Correspondence • Works well in 3D • Adaptive distribution of surface points • Work in progress on caudates Surface sampling with a max entropy particle system

  8. Software • Segmentation • ITK/NAMIC sandbox of brain tissue classification based on entropy of nonparametric statistics (Tasdizen et al., MICCAI 2005) • DTI • MAP smoothing in ITK (Basu et al., MICCAI 2006) • Integration of module into Slicer3 • Shape • ITK implementation of particle system for shape correspondence

  9. Publications • DTI • Fletcher PT, Joshi S. Riemannian Geometry for the Statistical Analysis of Diffusion Tensor Data. Signal Processing, 2006. (To appear). • Corouge I, Fletcher PT, Sarang J, Gouttard S, Gerig G. Fiber Tract-Oriented Statistics for Quantitative Diffusion Tensor MRI Analysis. MedIA (To appear) • Basu S, Fletcher PT, Whitaker RT. Rician Noise Removal in Diffusion Tensor MRI, MICCAI'06. • Corouge I, Fletcher PT, Joshi S, Gilmore JH, Gerig G. Fiber Tract-Oriented Statistics for Quantitative Diffusion Tensor MRI Analysis. MICCAI, 2005 • Shape • Fletcher PT, Whitaker RT. Riemannian Metrics on the Space of Solid Shapes. MICCAI'06 Workshop on Mathematical Foundations of Computational Anatomy (MFCA). • Cates J, Meyer M, Fletcher PT, Whitaker R. Entropy-Based Particle Systems for Shape Correspondence. MICCAI'06 Workshop on Mathematical Foundations of Computational Anatomy (MFCA).

More Related