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UID:6570@i2m.univ-amu.fr
DTSTART;TZID=Europe/Paris:20210122T143000
DTEND;TZID=Europe/Paris:20210122T153000
DTSTAMP:20241120T201742Z
URL:https://www.i2m.univ-amu.fr/evenements/accelerated-mr-imaging-from-sho
 rter-data-acquisition-to-faster-image-reconstruction-philippe-ciuciu/
SUMMARY:Philippe Ciuciu (CEA/NeuroSpin & Inria Parietal\, CEA Research Dire
 ctor ): Accelerated MR Imaging: From shorter data acquisition to faster im
 age reconstruction - Philippe Ciuciu
DESCRIPTION:Philippe Ciuciu: Reducing acquisition time is a major challenge
  in high-resolution magnetic resonance imaging (MRI) that has been success
 fully addressed by the Compressed Sensing (CS) theory. Slow CS-MR image re
 construction is however a major bottleneck. In this talk\, we address both
  aspects for collecting data faster (first part) and speeding up MR image 
 reconstruction (second part).\nFirst\, most of Fourier encoding schemes no
 wadays consist in downsampling existing k-space trajectories. Recently\, w
 e have overcome this issue by proposing the Spreading Projection Algorithm
  for Rapid K-space sampLING (SPARKLING) for T2* 2D non-Cartesian imaging a
 nd extended this approach to 3D imaging using the usual stacking strategy 
 of 2D SPARKLING (SoS) sampling patterns. In this talk\, I will present rec
 ent advancements based on a globally optimized 3D SPARKLING extension [1] 
 and will show how this version outperforms the SoS strategy both on phanto
 m and in vivo human brain data collected at 3 Tesla.\nSecond\, current sol
 ution to faster image reconstruction rely on deep learning (DL) and big da
 ta. I will present our XPDNet DL architecture that has been ranked second 
 in the 2020 brain fastMRI challenge [2]. I will summarize the main results
  and showcase XPDNet’s transfer learning capacity on out-of-distribution
  7T T2 images collected at NeuroSpin. Last\, I will explain how XPDNet can
  easily handle non-Cartesian data such as SPARKLING.\nReferences:\n[1] Cha
 ithya GR\, Weiss P\, Massire A\, Vignaud A\, Ciuciu P. Globally optimized 
 3D SPARKLING trajectories for high-resolution T2*-weighted Magnetic Resona
 nce Imaging. https://hal.inria.fr/hal-03090471/document\n[2] Muckley MJ\, 
 Riemenschneider B\, Radmanesh A\, Kim S\, Jeong G\, Ko J\, Jun Y\, Shin H\
 , Hwang D\, Mostapha M\, Arberet S. State-of-the-art Machine Learning MRI 
 Reconstruction in 2020: Results of the Second fastMRI Challenge. arXiv pre
 print arXiv:2012.06318. 2020 Dec 9. https://arxiv.org/pdf/2012.06318.pdf
ATTACH;FMTTYPE=image/jpeg:https://www.i2m.univ-amu.fr/wp-content/uploads/2
 020/12/Philippe_Ciuciu.jpg
CATEGORIES:Séminaire,Signal et Apprentissage
LOCATION:I2M Chateau-Gombert - CMI\, Salle de Séminaire R164 (1er étage)\
 , 39 Rue Joliot Curie\, 13013 Marseille\, France\, Campus Château-Gombert
 \, 
X-APPLE-STRUCTURED-LOCATION;VALUE=URI;X-ADDRESS=39 Rue Joliot Curie\, 13013
  Marseille\, France\, Campus Château-Gombert\, ;X-APPLE-RADIUS=100;X-TITL
 E=I2M Chateau-Gombert - CMI\, Salle de Séminaire R164 (1er étage):geo:0,
 0
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DTSTART:20201025T020000
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