From Variable Density Sampling to Continuous Sampling Using Two Alternative Strategies
Event details
| Date | 02.07.2013 |
| Hour | 13:00 › 14:00 |
| Speaker |
Philippe Ciuciu, Ph.D., CEA/NeuroSpin and INRIA Saclay, Paris (F) Bio: Philippe CIUCIU (IEEE Senior Member 2010) received the engineering degree from ESIEA Paris, France, and a DEA degree in automatic control and signal processing from the Université Paris-Sud (XI), Orsay, France, both in 1996. In 2000, he then received the PhD and degree from Université 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 Poline at Service Hospitalier Frédéric Joliot (CEA, Life Science Division, Orsay, France) before being appointed on a permanent research scientist position by the same institute in November 2001. Since 2007, Dr Ciuciu has been with the brand new NeuroSpin centre dedicated to ultra-high field MRI and applications in cognitive and clinical neurosciences. In 2008, he received the « Habilitation à Diriger des Recherches » degree from the Université Paris-Sud, Orsay France. In the same year, he has been elegated to the Principal Investigator position for driving the neurodynamics resarch program in collaboration with Andreas Kleinschmidt (DR1 INSERM, Cognitive Neuroimaging Unit U992). Dr Ciuciu published 22 refereed journal papers, 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 IEEE Journal of Selected Topics in Signal Processing for a special issue on Brain Mapping. Dr Ciuciu has also organized several workshops and special sessions 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 and successfully applied to the Young researcher ANR call with his SCHUBERT project on SCaling analysis of the HUman Brain Evoked and Rest acTivity. Dr Ciuciu has served as regular reviewer for twelve international top-ranked journals including six IEEE Transactions, MedIA, Neuroimage, Human Brain Mapping, Journal of Magnetic Resonance Imaging, NMR Biomed, Pattern Recognition and for funding agencies (ANR "Programme Blanc", BBSRC (British), Technology Foundation STW (Dutch)). Since 2007, he has elaborated the PyHRF software, which has received a very good reception at the Human Brain Mapping (HBM'10) and European Scientific Python (EuroScipy'11) conferences. Since 2008, Dr Ciuciu has been in charge of the scientific supervision of fMRI-based clinical trials for neurodegenerative deseases in several agreements linking CEA to pharmaceutical companies (Servier, Sanofi). |
| Location | |
| Category | Conferences - Seminars |
Joint work with:
Nicolas Chauffert (CEA/NeuroSpin, & INRIA Saclay, Parietal), Pierre Weiss (ITAV/CNRS & IMT, University of Toulouse) and Jonas Kahn (CNRS UMR 8524 & Univ. of Lille).
Since its discovery over the last decade, Compressed Sensing (CS) has been successfully applied to Magnetic Resonance Imaging (MRI) as a powerful way to reduce scanning time without sacrificing image quality [1-3]. MR images are actually strongly compressible in a wavelet basis, the latter being largely incoherent with the k-space or spatial Fourier domain where acquisition is performed. Nevertheless, since its first application to MRI [1], the theoretical justification of actual k-space sampling strategies [2,4] is questionable. Indeed, the vast majority of k-space sampling distributions have been heuristically designed (e.g., variable density) or driven by experimental feasibility considerations (e.g., random radial or spiral sampling to achieve smoothness k-space trajectory). In this talk, we first bring a novel answer to the CS synthesis problem, which amounts to deriving the optimal k-space 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 alternative research tracks: First, we consider random while continuous sampling 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 continuous sampling trajectories by drawing a small set of measurements independently and joining them using a traveling salesman problem solver. Our contribution lies in the theoretical derivation of the appropriate probability density of the initial drawings [7]. Preliminary simulation results in 2D and 3D show that this strategy is as efficient as independent drawings while being implementable on real acquisition systems.
[1] M. Lustig, D. Donoho and J. M. Pauly. “Sparse MRI: the application of compressed sensing for rapid MR imaging”. Magn Reson in Med, vol. 58, pp. 1182-1195, 2007.
[2] J. P. Haldar, D. Hernando and Z. P. Liang. “Compressed sensing MRI with random encoding”. IEEE Trans. Med. Imaging, vol. 30, no. 4, pp. 893-903, 2011.
[3] G. Puy, J.P. Marques, R. Gruetter, J.-P. Thiran, 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.
[4] M. Seeger, H. Nickisch, R. Pohmann and B. Schölkopf. “Optimization of k-space trajectories for compressed sensing by Bayesian experimental design”. Magn Reson in Med, vol. 63, pp. 116-126, 2010.
[5] N. Chauffert, P. Ciuciu and P. Weiss. “Variable density compressed sensing in MRI. Theoretical vs heuristic sampling strategies”. Accepted to the 10th IEEE ISBI conference, San Francisco, USA, Jan 2013.
[6] N. Chauffert, P. Ciuciu, P. Weiss, F. Gamboa, From variable density sampling to continuous sampling using Markov chains, in: Proc. 10th Int. Conf. Sampl. Theory App. (SampTA’13), Bremen, Germany, July 2013.
[7] N. Chauffert, P. Ciuciu, J. Kahn, P. Weiss, Traveling salesman-based variable density sampling, in: Proc. 10th Int. Conf. Sampl. Theory App. (SampTA’13), Bremen, Germany, July 2013.
Nicolas Chauffert (CEA/NeuroSpin, & INRIA Saclay, Parietal), Pierre Weiss (ITAV/CNRS & IMT, University of Toulouse) and Jonas Kahn (CNRS UMR 8524 & Univ. of Lille).
Since its discovery over the last decade, Compressed Sensing (CS) has been successfully applied to Magnetic Resonance Imaging (MRI) as a powerful way to reduce scanning time without sacrificing image quality [1-3]. MR images are actually strongly compressible in a wavelet basis, the latter being largely incoherent with the k-space or spatial Fourier domain where acquisition is performed. Nevertheless, since its first application to MRI [1], the theoretical justification of actual k-space sampling strategies [2,4] is questionable. Indeed, the vast majority of k-space sampling distributions have been heuristically designed (e.g., variable density) or driven by experimental feasibility considerations (e.g., random radial or spiral sampling to achieve smoothness k-space trajectory). In this talk, we first bring a novel answer to the CS synthesis problem, which amounts to deriving the optimal k-space 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 alternative research tracks: First, we consider random while continuous sampling 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 continuous sampling trajectories by drawing a small set of measurements independently and joining them using a traveling salesman problem solver. Our contribution lies in the theoretical derivation of the appropriate probability density of the initial drawings [7]. Preliminary simulation results in 2D and 3D show that this strategy is as efficient as independent drawings while being implementable on real acquisition systems.
[1] M. Lustig, D. Donoho and J. M. Pauly. “Sparse MRI: the application of compressed sensing for rapid MR imaging”. Magn Reson in Med, vol. 58, pp. 1182-1195, 2007.
[2] J. P. Haldar, D. Hernando and Z. P. Liang. “Compressed sensing MRI with random encoding”. IEEE Trans. Med. Imaging, vol. 30, no. 4, pp. 893-903, 2011.
[3] G. Puy, J.P. Marques, R. Gruetter, J.-P. Thiran, 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.
[4] M. Seeger, H. Nickisch, R. Pohmann and B. Schölkopf. “Optimization of k-space trajectories for compressed sensing by Bayesian experimental design”. Magn Reson in Med, vol. 63, pp. 116-126, 2010.
[5] N. Chauffert, P. Ciuciu and P. Weiss. “Variable density compressed sensing in MRI. Theoretical vs heuristic sampling strategies”. Accepted to the 10th IEEE ISBI conference, San Francisco, USA, Jan 2013.
[6] N. Chauffert, P. Ciuciu, P. Weiss, F. Gamboa, From variable density sampling to continuous sampling using Markov chains, in: Proc. 10th Int. Conf. Sampl. Theory App. (SampTA’13), Bremen, Germany, July 2013.
[7] N. Chauffert, P. Ciuciu, J. Kahn, P. Weiss, Traveling salesman-based variable density sampling, in: Proc. 10th Int. Conf. Sampl. Theory App. (SampTA’13), Bremen, Germany, July 2013.
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Practical information
- Informed public
- Free
- This event is internal
Organizer
- Prof. Dimitri Van De Ville