Jean-Luc Starck: Invited Speaker@AIDA-2020

  • Jean-Luc Starck will give an invited talk on Sparse reconstruction of radio transients and multichannel images at the forthcoming AIDA-2020 (Academia meets Industry) conference  dedicated to Medical Imaging and Image Processing.
  • Ming Jiang & Carole Lazarus will present posters on their PhD work on the application of CS to astrophysics and MRI, respectively.  


Invitation to the Special Session “Compressed sensing and inversion” at the 2017 Gretsi conference

We have been invited to contribute to the special session led by Jérôme Idier (IRCCyN/CNRS) and Cédric  Herzet (INRIA Rennes) on the Compressed Sensing topic. We will highlight the recent results we obtained at 7 Tesla in CS-MRI  based on the novel SPARKLING (Segmented Projection Algorithm for Random K-space samplING say shortly: SPARse K-space samplING) trajectories for ex-vivo T2* imaging.

BASP Frontiers Workshop

We (Jean-Luc Starck & Philippe Ciuciu) recently presented our own contribution in CS at the 4th edition of the BASP Frontiers workshop which held at Villars sur Ollon in January 2017.  You can check out the Proceedings of the conference there.

  • The poster presented by the NeuroSpin team and entitled New Physically Plausible Compressive Sampling Schemes for high resolution MRI is available here.
  • The talks given by Jean-Luc Starck can be downloaded there.


Welcome to the COSMIC website!

COSMIC: COmpressed Sensing for Magnetic resonance Imaging & Cosmology

This project has been funded by the CEA Division of Fundamental Research for 1 year in 2016 to foster  collaborations between CEA research scientists and engineers working in biomedical imaging at NeuroSpin( team leader: P. Ciuciu) and astrophysics at the CosmotStat Lab (team leader: JL Starck) within the Astrophysics Department) on Compressed Sensing (or Compressive Sampling).

Compressed Sensing is a mathematical principle that has been elaborated for now 10 years by two groups in the US: (i) Emmanuel Candès and his colleagues Terence Tao and Justin Romberg on the one hand and (ii) David Donoho on the other hand. The idea is to surpass the Shannon-Nyquist sampling rate by collecting less data samples while allowing the perfect (at least in a noiseless setting) recovery of the underlying signal or image. 

This approach has been popularized in Magnetic Resonance Imaging (MRI) over the last decade as well as in astrophysics (noticeably in radio-astronomy).  For instance, its first application to MRI emerged in 2007 through the sparse MRI paper by Miki Lustig and his colleagues.

Through COSMIC, our interactions will allow us to share different expertise both at the acquisition and reconstruction stages in order to speed-up acquisition or to improve image quality, both in MRI and in radio-astronomy (thanks to the interferometry principle).