@InProceedings{assemlal09-1,
author = {Assemlal, H-E. and David Tschumperl{'e} and Luc Brun},
title = {Estimation de caractéristiques quelconques de la PDF à partir d'un signal IRM de diffusion},
booktitle = {GRETSI},
year = 2009,
address = {Dijon,France},
month = {September},
theme = {misc}
}
@InProceedings{assemlal09-2,
author = {Assemlal, H-E. and David Tschumperl{'e} and Luc Brun},
title = {Evaluation of q-Space Sampling Strategies for the Diffusion Magnetic Resonance Imaging}
booktitle = {MICCAI},
year = 2009,
address = {London/England},
month = {September},
theme = {misc},
abstract="We address the problem of efficient sampling of the
diffusion space for the Diffusion Magnetic Resonance Imaging (dMRI)
modality. While recent scanner improvements enable the acquisition of
more and more detailed images, it is still unclear which q-space
sampling strategy gives the best performance. We evaluate several
q-space sampling distributions by an approach based on the
approximation of the MR signal by a series expansion of Spherical
Harmonics and Laguerre-Gaussian functions. With the help of synthetic
experiments, we identify a subset of sampling distributions which
leads to the best reconstructed data."
}
@InProceedings{assemlal08,
author = {Assemlal, H-E. and David Tschumperl{'e} and Luc Brun},
title = {Robust Variational Estimation of PDF functions from Diffusion MR Signal.},
booktitle = {CDMRI},
year = 2008,
address = {New York/USA},
month = {September},
theme = {misc},
abstract="We address the problem of robust estimation of tissue
microstructure from Diffusion Magnetic Resonance Imaging (dMRI). On
one hand, recent hardware improvements enable the acquisition of more
detailed images, on the other hand, this comes along with a low Signal
to Noise (SNR) ratio. In such a context, the approximation of the
Rician acquisition noise as Gaussian is not accurate. We propose to
estimate the volume of PDF-based characteristics from data samples by
minimizing a nonlinear energy functional which considers Rician MR
acquisition noise as well as additional spatial regularity
constraints. This approach relies on the approximation of the MR
signal by a series expansion based on Spherical Harmonics and
Laguerre-Gaussian functions. Results are presented to depict the
performance of this PDE-based approach on synthetic data and human
brain data sets respectively.",
url={pdf:=http://www.greyc.ensicaen.fr/~luc/ARTICLES/cdmri08.pdf, Presentation:=http://www.greyc.ensicaen.fr/~luc/ARTICLES/cdmri08-2.pdf}
}
@inproceedings{assemlal08-1,
author = {Assemlal, Haz-Edine and Tschumperl'{e}, David and Brun, Luc},
title = {Efficient Computation of PDF-Based Characteristics from Diffusion MR Signal},
booktitle = {MICCAI '08: Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II},
year = {2008},
isbn = {978-3-540-85989-5},
pages = {70--78},
location = {New York, New York},
doi = {http://dx.doi.org/10.1007/978-3-540-85990-1_9},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
theme = {misc},
abstract="We present a general method for the computation of PDF-based
characteristics of the tissue micro-architecture in MR imaging. The
approach relies on the approximation of the MR signal by a series
expansion based on Spherical Harmonics and Laguerre-Gaussian
functions, followed by a simple projection step that is efficiently
done in a finite dimensional space. The resulting algorithm is
generic, flexible and is able to compute a large set of useful
characteristics of the local tissues structure. We illustrate the
effectiveness of this approach by showing results on synthetic and
real MR datasets acquired in a clinical time-frame.",
url={pdf:=http://www.greyc.ensicaen.fr/~luc/ARTICLES/miccai08.pdf,Poster:=http://www.greyc.ensicaen.fr/~luc/ARTICLES/poster.pdf}
}
@TechReport{TR-Assemlal-2007,
author = {Assemlal, H-E. and David Tschumperl{'e} and Luc Brun},
title = {A Variational Framework for the Robust Estimation of ODFs From High Angular Resolution Diffusion Images.},
institution = {GREYC},
year = 2007,
number = {07-01},
month = {April},
theme = {misc},
url= {pdf :=http://www.greyc.ensicaen.fr/~dtschump/data/cahier_greyc07-01.pdf}
}
@InProceedings{CI-Assemlal-2007,
author = {Assemlal, H-E. and David Tschumperl{'e} and Luc Brun},
title = {Fiber Tracking on HARDI Data Using Robust ODF Fields.},
booktitle = {ICIP'2007, IEEE International Conference on Image Processing},
year = 2007,
address = {San Antonio/USA},
month = {September},
theme = {misc},
abstract= "We present a robust method to retrieve neuronal
fibers in human brain w hite matter from High-Angular
Resolution MRI (HARDI datasets). Contrary to class
ical fiber-tracking techniques done on the
traditional 2nd-order tensor model (D TI) which may
lead to truncated or biased estimated diffusion
directions in case of fiber crossing configurations,
we propose here a more complex approach based on a
variational estimation of Orientation Diffusion
Functions (ODF) modeled wi th spherical
harmonics. This kind of model can correctly retrieve
multiple fiber directions corresponding to underlying
intra-voxel fibers populations. Our tech nique is
able to consider the Rician noise model of the MRI
acquisition in order to better estimate the white
matter fiber tracks. Results on both synthetic and
real human brain white matter HARDI datasets
illustrate the effectiveness of th e proposed
approach.",
url={pdf:=http://www.greyc.ensicaen.fr/~luc/ARTICLES/icip07.pdf,
presentation:=http://www.greyc.ensicaen.fr/~luc/ARTICLES/icip07_presentation.pdf}
}
@InProceedings{CN-Assemlal-2007,
author = {Assemlal, H-E. and David Tschumperl{'e} and Luc Brun},
title = {Estimation variationnelle robuste de mod{`e}les complexes de diffusion en IRM {`a} haute r{'e}solution angulaire et tractographie.},
booktitle = {GRETSI'2007},
year = 2007,
address = {Troyes/France},
month = {September},
theme = {misc},
abstract= "We present a robust method to retrieve neuronal
fibers in human brain white matter from High-Angular
Resolution MRI (HARDI datasets). Contrary to
classical fiber-tracking techniques done on the
traditional 2nd-order tensor model (DTI) which may
lead to truncated or biased estimated diffusion
directions in case of fiber crossing configurations,
we propose here a more complex approach based on a
variational estimation of Orientation Diffusion
Functions (ODF) modeled with spherical
harmonics. This kind of model can correctly retrieve
multiple fiber directions corresponding to underlying
intravoxel fibers populations. Our technique is able
to consider the Rician noise model of the MRI
acquisition in order to better estimate the white
matter fiber tracks. Results on both synthetic and
real human brain white matter HARDI datasets
illustrate the effectiveness of the proposed
approach."
url={pdf:=http://www.greyc.ensicaen.fr/~luc/ARTICLES/gretsi07.pdf,presentation:=http://www.greyc.ensicaen.fr/~luc/ARTICLES/gretsi07_presentation.pdf}
}
@InProceedings{CI-TSCHUMPERLE-2008-1,
author = {Tschumperl{'e}, D. and Brun, L.},
title = {Image Denoising and Registration by PDE's on the Space of Patches},
booktitle = {International Workshop on Local and Non-Local Approximation in Image Processing (LNLA'08)},
pages = {} ,
year = {2008},
theme = {misc},
address = {Lausanne}
}
