Computational Diffusion MRI

Diffusion MRI is a technique introduced in the mid-1980s (Le Bihan & al., 1985; Merboldt & al, 1985; Taylor & al, 1985) from which has stemmed a number of variations, such as Diffusion Tensor Imaging (DTI), which was invented by Dr. Basser, a partner in this team, in the mid-1990s (P.J. Basser & al, 1994). High Angular Resolution Diffusion Imaging (HARDI) techniques such as Q-Ball Imaging (QBI) or Diffusion Spectrum Imaging (DSI) pioneered by Tuch & al. (Tuch; 2002) are more recent examples of DMRI. These powerful techniques have helped efficiently tackle and solve a number of important and challenging problems. They have also opened up a landscape of extremely exciting discoveries for medicine and neuroscience. The development of novel mathematical analysis tools for DTI or HARDI such as Q-Ball Imaging (QBI) will result in fundamental advancements for research on stroke, multiple sclerosis, amyotrophic lateral sclerosis, Alzheimer's and Parkinson's diseases, HIV/AIDS, neurosurgery, tumor growth modeling or neuropsychiatric disorders like schizophrenia. Moreover, our understanding of the development of the human brain, the effect of aging or the organization of anatomo-functional networks has already started to greatly benefit from this unprecedented insight into brain microstructure.

The Computational Diffusion MRI (CD-MRI) Associate Team involves three research institutions : Inria Sophia-Antipolis Méditerranée (R. Deriche, ATHENA Project Team), the National Institute of Child Health and Human Development (NICHD, NIH, P.Basser, Section on Tissue Biophysics and Biomimetics) and the University of Minnesota (UofM, G. Sapiro, Department of Electrical and Computer Engineering and C. Lenglet, Center for Magnetic Resonance Research).

This CD-MRI Associate Team was created to bring each partner's expertise together in the field of Diffusion Magnetic Resonance Imaging with the objective to help in making significant contributions to the processing and analysis of diffusion weighted imaging data, a task well known to be extremely challenging due to the complex underlying properties of the tissue being imaged and the structure of the data

The rational behind this project was to benefit from the complementarity and the synergy of our expertise and to combine our efforts and ideas to succeed achieving this exciting and challenging objective. Indeed, our groups at Inria, NICHD and the University of Minnesota have great and complementary expertise in Diffusion MRI.

ATHENA Project Team, which grew out of the former ODYSSÉE Project Team, has greatly contributed during the last years to develop original and efficient algorithms relying on Riemannian geometry, differential geometry, partial differential equations and front propagation techniques to correctly and efficiently process Diffusion MRI data. The group of Peter Basser at NICHD had pioneered the so-called diffusion tensor imaging in the mid 90's, and since then has developed numerous and important applications to clinical research. The Center for Magnetic Resonance Research at University of Minnesota is a research lab, unique in the domain of high field MRI, with no less than six high field magnets; and the group of Guillermo Sapiro in the department of Electrical and Computer Engineering is one of the best worldwide dedicated to research in imaging sciences.

Therefore, the CD-MRI started in 2009 with the following main objectives :

• Develop rigorous mathematical and computational tools for the analysis of Diffusion MRI data.
• Improve acquisition techniques for High Angular Resolution Diffusion Imaging,
• Write joint publications and help address challenging clinical applications.

Through an extensive exchange program involving PhD's as well Post-Docs's and senior scientists between all the partners, our Associate Team has been able to tackle these challenging objectives. Indeed, we have contributed to advance the state-of-the-art in Computational Diffusion MRI, we have been very successful to initiate and pursue research on optimal diffusion gradient schemes (single, multi-shell), online ODF reconstruction and motion detection, optimal reconstruction of the propagator and decoding axon diameter distribution information encrypted in radial NMR attenuation signals. We proposed new Kalman based acquisition and sampling techniques particularly well adapted to process HARDI data and make it clinically feasible, and wrote several joint publications in international conferences, some of which are or will be submitted to journals.

We are currently pursuing this effort together with our partners and we'll keep collaborating with great pleasure with all our partners during the next years.

References

1. Basser, P., Mattiello, J., and Le Bihan, D. (1994). MR diffusion tensor spectroscopy and imaging. Biophysical Journal, 66(1):259-267.
2. Basser, P., Mattiello, J., and Le Bihan, D. (1994). Estimation of the effective self-diffusion tensor from the NMR spin echo. Journal of Magnetic Resonance, B(103):247-254.
3. Delmaire, C., Vidailhet, M., Wassermann, D., Descoteaux, M., Valabregue, R., Bourdin, F., Lenglet, C., Sangla, S., Terrier, A., Deriche, R., Lehéricy, S. (2008). Diffusion abnormalities in the primary sensorimotor pathways in writer’s cramp. Archives of Neurology, In press.
4. Khachaturian, M.H.,Wisco, J.J., and Tuch, D.S. (2007). Boosting the sampling efficiency of q-Ball imaging using multiple wavevector fusion. Magn. Reson. Med. 57(2):289-296
5. Le Bihan, D. and Breton, E. (1985). Imagerie de diffusion in vivo par résonance magnétique nucléaire. C. R. Acad. Sci. Paris, 301 Série II:1109-1112.
6. Merboldt, K., Hanicke, W., and Frahm, J. (1985). Self-diffusion NMR imaging using stimulated echoes. J. Magn. Reson , 64:479-486.
7. Taylor, D. and Bushell, M. (1985). The spatial mapping of translational diffusion coefficients by the NMR imaging technique. Physics in Medicine and Biology, 30(4):345-349.
8. Tuch, D. (2002). Diffusion MRI of Complex Tissue Structure. PhD thesis, Harvard University and Massachusetts Institute of Technology.