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SUMMARY:From Variable Density Sampling to Continuous Sampling Using Two Al
 ternative Strategies
DTSTART:20130702T130000
DTEND:20130702T140000
DTSTAMP:20260408T055746Z
UID:2472cc0774eacf25c3b8ad669848035c738915b2e3d2273bcede68d5
CATEGORIES:Conferences - Seminars
DESCRIPTION:Philippe Ciuciu\, Ph.D.\, CEA/NeuroSpin and INRIA Saclay\, Par
 is (F)\nBio: Philippe CIUCIU (IEEE Senior Member 2010) received the engine
 ering degree from ESIEA Paris\, France\, and a DEA degree in automatic con
 trol and signal processing from the Université Paris-Sud (XI)\, Orsay\, F
 rance\, both in 1996. In 2000\, he then received the PhD and degree from U
 niversité Paris-Sud in 2000 for a work done at Groupe Problèmes Inverses
  (L2S\, UMR 8506) in radar Doppler imaging. He then became a post-doctoral
  fellow (2000-2001) in the fMRI signal processing group headed by JB Polin
 e at Service Hospitalier Frédéric Joliot (CEA\, Life Science Division\, 
 Orsay\, France) before being appointed on a permanent research scientist p
 osition by the same institute in November 2001. Since 2007\, Dr Ciuciu has
  been with the brand new NeuroSpin centre dedicated to ultra-high field MR
 I and applications in cognitive and clinical neurosciences. In 2008\, he r
 eceived the « Habilitation à Diriger des Recherches » degree from the U
 niversité Paris-Sud\, Orsay France. In the same year\, he has been elegat
 ed to the Principal Investigator position for driving the neurodynamics re
 sarch program in collaboration with Andreas Kleinschmidt (DR1 INSERM\, Cog
 nitive Neuroimaging Unit U992). Dr Ciuciu published 22 refereed journal pa
 pers\, 2 book chapters\, more than 50 conference papers and 1 MRI-related 
 pending patent. In 2008\, he has been invited as a guest editor to the IEE
 E Journal of Selected Topics in Signal Processing for a special issue on B
 rain Mapping. Dr Ciuciu has also organized several workshops and special s
 essions in international conferences (IEEE ICASSP'06\, MICCAI'09\, ISBI'11
 ). In 2003\, he was the recipient of the young researcher best paper award
  at the IPMI conference (with G. Marrelec). In 2009\, he received the best
  paper award at the IEEE Machine Learning for Signal Processing workshop a
 nd successfully applied to the Young researcher ANR call with his SCHUBERT
  project on SCaling analysis of the HUman Brain Evoked and Rest acTivity.\
 nDr Ciuciu has served as regular reviewer for twelve international top-ran
 ked journals including six IEEE Transactions\, MedIA\, Neuroimage\, Human 
 Brain Mapping\, Journal of Magnetic Resonance Imaging\, NMR Biomed\, Patte
 rn Recognition and for funding agencies (ANR "Programme Blanc"\, BBSRC (Br
 itish)\, Technology Foundation STW (Dutch)).\nSince 2007\, he has elaborat
 ed the PyHRF software\, which has received a very good reception at the Hu
 man Brain Mapping (HBM'10) and European Scientific Python (EuroScipy'11) c
 onferences. Since 2008\, Dr Ciuciu has been in charge of the scientific su
 pervision of fMRI-based clinical trials for neurodegenerative deseases in 
 several agreements linking CEA to pharmaceutical companies (Servier\, Sano
 fi).\nJoint work with:\nNicolas Chauffert (CEA/NeuroSpin\, & INRIA Saclay\
 , Parietal)\, Pierre Weiss (ITAV/CNRS & IMT\, University of Toulouse) and 
 Jonas Kahn (CNRS UMR 8524 & Univ. of Lille).\nSince its discovery over the
  last decade\, Compressed Sensing (CS) has been successfully applied to Ma
 gnetic Resonance Imaging (MRI) as a powerful way to reduce scanning time w
 ithout sacrificing image quality [1-3]. MR images are actually strongly co
 mpressible in a wavelet basis\, the latter being largely incoherent with t
 he k-space or spatial Fourier domain where acquisition is performed. Never
 theless\, since its first application to MRI [1]\, the theoretical justifi
 cation of actual k-space sampling strategies [2\,4] is questionable. Indee
 d\, the vast majority of k-space sampling distributions have been heuristi
 cally designed (e.g.\, variable density) or driven by experimental feasibi
 lity considerations (e.g.\, random radial or spiral sampling to achieve sm
 oothness k-space trajectory). In this talk\, we first bring a novel answer
  to the CS synthesis problem\, which amounts to deriving the optimal k-spa
 ce sampling distribution according to a given criterion [5]. Then\, we try
  to reconcile very recent CS results with the MRI specificities (magnetic 
 field gradients) by enforcing the measurements\, i.e. samples of k-space\,
  to fit smooth trajectories. To this end\, we propose to follow two altern
 ative research tracks: First\, we consider random while continuous samplin
 g based on Markov chains and we compare the reconstruction quality of this
  scheme to the state-of-the art [6]. Second\, we propose to generate conti
 nuous sampling trajectories by drawing a small set of measurements indepen
 dently and joining them using a traveling salesman problem solver. Our con
 tribution lies in the theoretical derivation of the appropriate probabilit
 y density of the initial drawings [7]. Preliminary simulation results in 2
 D and 3D show that this strategy is as efficient as independent drawings w
 hile being implementable on real acquisition systems.\n[1] M. Lustig\, D. 
 Donoho and J. M. Pauly. “Sparse MRI: the application of compressed sensi
 ng for rapid MR imaging”. Magn Reson in Med\, vol. 58\, pp. 1182-1195\, 
 2007.\n[2] J. P. Haldar\, D. Hernando and Z. P. Liang. “Compressed sensi
 ng MRI with random encoding”. IEEE Trans. Med. Imaging\, vol. 30\, no. 4
 \, pp. 893-903\, 2011.\n[3] G. Puy\, J.P. Marques\, R. Gruetter\, J.-P. Th
 iran\, D. van de Ville\, P. Vandergheynst and Y. Wiaux. “Spread spectrum
  magnetic resonance imaging”. IEEE Trans. Med. Imaging\, vol. 31\, no. 3
 \, pp. 586-598\, March\, 2012.\n[4] M. Seeger\, H. Nickisch\, R. Pohmann a
 nd B. Schölkopf. “Optimization of k-space trajectories for compressed s
 ensing by Bayesian experimental design”. Magn Reson in Med\, vol. 63\, p
 p. 116-126\, 2010.\n[5] N. Chauffert\, P. Ciuciu and P. Weiss. “Variable
  density compressed sensing in MRI. Theoretical vs heuristic sampling stra
 tegies”. Accepted to the 10th IEEE ISBI conference\, San Francisco\, USA
 \, Jan 2013.\n[6] N. Chauffert\, P. Ciuciu\, P. Weiss\, F. Gamboa\, From v
 ariable density sampling to continuous sampling using Markov chains\, in: 
 Proc. 10th Int. Conf. Sampl. Theory App. (SampTA’13)\, Bremen\, Germany\
 , July 2013.\n[7] N. Chauffert\, P. Ciuciu\, J. Kahn\, P. Weiss\, Travelin
 g salesman-based variable density sampling\, in: Proc. 10th Int. Conf. Sam
 pl. Theory App. (SampTA’13)\, Bremen\, Germany\, July 2013.
LOCATION:SV1717a http://map.epfl.ch/?room=sv1717a
STATUS:CONFIRMED
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