Diffusion Workshop
Mathematics of Diffusion MRI Workshop
Time: Monday June 27, 9am-12pm
Location: Star conference room, Stata Center, MIT
Organizers: Carl-Fredrik Westin and David Tuch
Overview: In parallel with the NAMIC Programmer's Week, we will hold a workshop on the Mathematics of Diffusion MRI. The goals of the workshop are to identify the most significant open problems in the field, and formulate research plans to address those challenges. The workshop will be small (ideally < 12) with a focus on informal discussions. Please note that this workshop is now oversubscribed. If you would like to attend, please send an email to Dave Tuch.
Syllabus (working):
- 900-915am: Intro/objectives (Westin/Tuch)
- 915-930: Diffusion physics (Tuch).
- Challenge: Solve the diffusion boundary inverse problem. Impact: Medium. Difficulty: High
- Challenge: Differentiate crossing from bending fibers. Impact: Medium. Difficulty: High
- 930-1000: Tensor statistics (Westin)
- 1000-1015: Statistics on Curved Spaces (Fletcher)
- 1015-1030: Statistical group comparison (Tuch)
- Challenge: Solve the localization problem. Impact: High. Difficulty: Very High
- Challenge: Develop multiple comparisons method for spatially small effects. Impact: High. Difficulty: Medium
- 1030-1100: Clustering (Westin)
- 1100-1130: Tractography (Pichon).
- Challenge: Develop a constructive framework for the tractography problem. Impact: High. Difficulty: High
- 1130-1200: Grand challenges (All)
Background reading:
Diffusion physics
- Callaghan PT. Principles of nuclear magnetic resonance microscopy. Oxford Press. 1993.
- Assaf Y, Freidlin RZ, Rohde GK, Basser PJ. New modeling and experimental framework to characterize hindered and restricted water diffusion in brain white matter. Magn Reson Med. 2004 Nov;52(5):965-78.
Tensor Statisics
- Basser PJ, Pajevic S: A Normal Distribution for Tensor-valued Random Variables: Applications to Diffusion Tensor MRI. IEEE Trans. Med. Imaging 22(7): 785-794 (2003)
Manifold Learning
- Roweis S, Saul L. Nonlinear dimensionality reduction by locally linear embedding. Science, v.290 no.5500 , Dec.22, 2000. pp.2323--2326.
- Belkin M, Niyogi P. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation. Comp. Sci. and Statistics, January 2002.
- Brun A, Westin CF, Herberthson M, Knutsson H. Fast Manifold Learning Based on Riemannian Normal Coordinates. Proceedings of the 14th Scandinavian conference on image analysis (SCIA'05), Joensuu, Finland, June, 2005.
Fiber Clustering
- Brun A, Knutsson H Park HJ, Shenton M, and Westin CF. Clustering fiber traces using normalized. Proceedings of the Seventh International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI'2004), Rennes, Saint Malo, France, September 26-30, 2004
Tractography
- Behrens TE, Johansen-Berg H, Woolrich MW, Smith SM, Wheeler-Kingshott CA, Boulby PA, Barker GJ, Sillery EL, Sheehan K, Ciccarelli O, Thompson AJ, Brady JM, Matthews PM. Non-invasive mapping of connections between human thalamus and cortex using diffusion imaging. Nat Neurosci. 2003 Jul;6(7):750-7.
Statistical group comparison
- Brett M, Johnsrude IS, Owen AM. The problem of functional localization in the human brain. Nat Rev Neurosci. 2002 Mar;3(3):243-9.
- Hayasaka S, Nichols TE. Combining voxel intensity and cluster extent with permutation test framework. Neuroimage. 2004 Sep;23(1):54-63.
Attendees:
- Carl-Fredrik Westin, BWH, Core 1
- David Tuch, MGH, Core 1
- Steve Pieper, Isomics/BWH, Core 2
- John Melonakos, Georgia Tech, Core 1
- Eric Pichon, Georgia Tech, Core 1
- Xiaodong Tao, GE, Core 2
- Mahnaz Maddah, MIT, Core 1
- Andrea Mewes, BWH
- Casey Goodlett, UNC, Core 1
- Isabelle Corouge, UNC, Core 1
- Sonia Pujol, BWH, Core 5
- Tom Fletcher, Utah, Core 1
- Sylvain Bouix, Harvard, Core 3
- Daniel Goldberg, BWH
- Lauren O'Donnell, MIT, Core 1
- Torsten Rohlfing, SRI International
- Luis Ibanez, Kitware, Core 2
- Katie Hayes, BWH, Core 2
