@Article{RI-ASSEMLAL-2011, author = {Haz-Edine Assemlal and David Tschumperle and Luc Brun and Kaleem Siddiqi}, title = {Recent advances in diffusion MRI modeling: Angular and radial reconstruction}, journal = {Medical image analysis}, year = 2011, volume = 15, number = 4, pages = {369-396}, month = {August}, theme = {misc}, url={pdf:=https://tschumperle.users.greyc.fr/publications/tschumperle_media11.pdf}, abstract="The analysis of the diffusion signal is closely related to the sampling of the q-space. (a) Full sampling of the q-space is currently impractical in vivo due to the significant acquisition time it would imply. (b) Low angular resolution sampling used in DTI. (c) High angular resolution sampling (HARDI). (d) Radial only sampling used in diffusion NMR. (e) Sparse sampling which combines radial and angular measurements. * Acquisition time for a diffusion image at full resolution is approximately 1h. * Direct processing of the data is not reliable due to the limited number of samples. * Numerous reconstruction models of the literature are described in this review. * Three groups of methods based on the nature of sampling: angular, radial and combined. Recent advances in diffusion magnetic resonance image (dMRI) modeling have led to the development of several state of the art methods for reconstructing the diffusion signal. These methods allow for distinct features to be computed, which in turn reflect properties of fibrous tissue in the brain and in other organs. A practical consideration is that to choose among these approaches requires very specialized knowledge. In order to bridge the gap between theory and practice in dMRI reconstruction and analysis we present a detailed review of the dMRI modeling literature. We place an emphasis on the mathematical and algorithmic underpinnings of the subject, categorizing existing methods according to how they treat the angular and radial sampling of the diffusion signal. We describe the features that can be computed with each method and discuss its advantages and limitations. We also provide a detailed bibliography to guide the reader." } @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}, url={HAL:= http://hal.archives-ouvertes.fr/hal-00332804, pdf := http://hal.archives-ouvertes.fr/docs/00/33/28/04/PDF/tschumperle_brun_lnla2008.pdf} } @TechReport{TR-TSCHUMPERLE-2008, author = {Tschumperl{\'e}, D. and Brun, L.}, title = {Defining Some Variational Methods on the Space of Patches : Application to Multi-Valued Image Denoising and Registration}, institution = {GREYC}, year = 2008, number = {08/01}, theme = {misc} } @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={HAL:= http://hal.archives-ouvertes.fr/hal-00250226, pdf:=http://hal.archives-ouvertes.fr/docs/00/25/02/26/PDF/conf_icip2007_review.pdf, ps:=http://hal.archives-ouvertes.fr/docs/00/25/02/26/PS/conf_icip2007_review.ps, presentation:=https://brunl01.users.greyc.fr/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={HAL := http://hal.archives-ouvertes.fr/hal-00329157, pdf:=http://hal.archives-ouvertes.fr/docs/00/32/91/57/PDF/gretsi07_publi.pdf,presentation:=https://brunl01.users.greyc.fr/ARTICLES/gretsi07_presentation.pdf} } @article{assemlal09, author = {Assemlal, H-E. and David Tschumperl{'e} and Luc Brun}, title = {Efficient and Robust Computation of PDF Features from Diffusion MR Signal}, journal = {Medical Image Analysis}, year = {2009}, volume=13, number=5, pages={715-729}, abstract=" We present a method for the estimation of various features of the tissue micro-architecture using the diffusion magnetic resonance imaging. The considered features are designed from the displacement probability density function (PDF). The estimation is based on two steps: first the approximation of the signal by a series expansion made of Gaussian-Laguerre and Spherical Harmonics functions; followed by a projection on a finite dimensional space. Besides, we propose to tackle the problem of the robustness to Rician noise corrupting \emphin-vivo acquisitions. Our feature estimation is expressed as a variational minimization process leading to a variational framework which is robust to noise. This approach is very flexible regarding the number of samples and enables the computation of a large set of various features of the local tissues structure. We demonstrate the effectiveness of the method with results on both synthetic phantom and real MR datasets acquired in a clinical time-frame.", url={ HAL := http://hal.archives-ouvertes.fr/hal-00410615, pdf:=http://hal.archives-ouvertes.fr/docs/00/41/06/15/PDF/assemlal_MedIA09.pdf} } @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={HAL:=http://hal.archives-ouvertes.fr/hal-00329263, pdf:=http://hal.archives-ouvertes.fr/docs/00/32/92/63/PDF/cdmri08.pdf, Presentation:=https://brunl01.users.greyc.fr/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:=https://brunl01.users.greyc.fr/ARTICLES/miccai08.pdf,Slides:=https://brunl01.users.greyc.fr/ARTICLES/poster.pdf} } @inproceedings{elrhabi:hal-01161832, TITLE = {{Estimation de la pose d'une cam{\'e}ra {\`a} partir d'un flux vid{\'e}o en s'approchant du temps r{\'e}el}}, AUTHOR = {El Rhabi, Youssef and Simon, Loic and Brun, Luc}, URL = {https://hal.archives-ouvertes.fr/hal-01161832}, BOOKTITLE = {{Journ{\'e}es francophones des jeunes chercheurs en vision par ordinateur}}, ADDRESS = {Amiens, France}, HAL_LOCAL_REFERENCE = {poster}, YEAR = {2015}, MONTH = Jun, KEYWORDS = {R{\'e}alit{\'e} augment{\'e}e ; SfM ; SLAM ; calcul de pose temps r{\'e}el ; recalage 2D/3D}, url = {PDF:=https://hal.archives-ouvertes.fr/hal-01161832/file/Estimation_de_la_pose_d_une_camera_a_partir_d_un_flux_video_en_s_approchant_du_temps_reel.pdf}, HAL_ID = {hal-01161832}, HAL_VERSION = {v1}, theme="misc" }