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2006
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Clinical DT-MRI estimation, smoothing and fiber tracking with Log-Euclidean metrics

11 years 3 months ago
Clinical DT-MRI estimation, smoothing and fiber tracking with Log-Euclidean metrics
Diffusion tensor MRI is an imaging modality that is gaining importance in clinical applications. However, in a clinical environment, data have to be acquired rapidly, often at the detriment of the image quality. We propose a new variational framework that specifically targets low quality DT-MRI. The Rician nature of the noise on the images leads us to a maximum likelihood strategy to estimate the tensor field. To further reduce the noise, we optimally exploit the spatial correlation by adding to the estimation an anisotropic regularization term. This criterion is easily optimized thanks to the use of recently introduced Log-Euclidean metrics. Results on real clinical data show promising improvements of fiber tracking in the brain and the spinal cord.
Pierre Fillard, Vincent Arsigny, Xavier Pennec, Ni
Added 20 Nov 2009
Updated 20 Nov 2009
Type Conference
Year 2006
Where ISBI
Authors Pierre Fillard, Vincent Arsigny, Xavier Pennec, Nicholas Ayache
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