[BIC-announce] CIM-REPARTI Perception Seminar: Haz-Edine Assemlal: Feb. 17/10 in MC437

[Kaleem Siddiqi]

Folks,
Haz-Edine is a new post-doc at McGill, working between CIM and the BIC.
He is giving a seminar tomorrow which could be of interest to many.

Best,
Kaleem

Begin forwarded message:

> From: manager at cim.mcgill.ca
> Date: February 16, 2010 9:33:37 AM EST
> To: cim-all at cim.mcgill.ca
> Cc: manager at cim.mcgill.ca
> Subject: CIM-REPARTI Perception Seminar: Haz-Edine Assemlal: Feb. 17/10 in MC437
> 
> Greetings,
> 
> Please note that everyone is welcome, and encouraged, to attend.
> 
> Marlene
> 
> 
> CIM - REPARTI Perception Seminars
> 
> http://www.cim.mcgill.ca/Members/manager/seminar.2010-02-14.1652242687
> 
> 
> Title: Diffusion MR image analysis for the estimation of tissues local
> architecture
> 
> 
> Speaker: Haz-Edine Assemlal, Post Doctoral Fellow
> http://cim.mcgill.ca/~assemlal
> <http://cim.mcgill.ca/%7Eassemlal>
> 
> 
> 
> Date: February 17, 2010 at  3:00 PM
> 
> 
> Location: George Zames Room MC437
> Centre for Intelligent Machines (CIM)
> 3480 rue University
> 4th floor
> McConnell Engineering Building
> 
> 
> 
> Abstract: We present a method which addresses the following challenging
> problem: given a highly reduced number of samples from in vivo diffusion
> magnetic resonance imaging (dMRI), how to robustly estimate various
> features of the brain tissue ? The considered features enable to study
> the local micro-architecture of the brain tissue, with a special
> interest to the brain white matter. Our proposed approach is flexible as
> it enables to easily plug in a large set of features; and thus unifies
> many previous works linked to the probability density functions (PDF) of
> displacement in dMRI such as the diffusion tensor imaging (DTI) and the
> Q-Ball Imaging (QBI). It also enables to design new features: "true"
> ODF, probability of non diffusion. We propose two frameworks: one which
> is fast and another one which is robust to MR images noise. We validate
> our approach with several comparisons of results to other methods of the
> literature, on both synthetic phantom and MR human brain datasets
> acquired in an acceptable time-frame.
> 
> 
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