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SUMMARY:The application of compressed sensing for longitudinal MRI - Dr Li
 or Weizman\, Technion - Israel Institute of Technology
DTSTART:20140731T130000Z
DTEND:20140731T140000Z
UID:TALK53514@talks.cam.ac.uk
CONTACT:Prof. Ramji Venkataramanan
DESCRIPTION:Magnetic Resonance Imaging (MRI) is the method of choice for d
 iagnosis\, evaluation and follow-up of brain pathologies. In the common tr
 eatment scheme\, patients are repeatedly scanned every few weeks or months
  to assess disease progression and treatment response. Although the import
 ant information for clinical evaluation lies in the change between the fol
 low-up MRI and the former one\, every follow-up scan is acquired anew. Thi
 s makes most of the data in the later scan redundant. \n\nIn MRI\, data is
  acquired in a spatial frequency domain\, called "k-space". In my talk I'l
 l discuss the application of compressed sensing (CS) for MRI and the mutua
 l similarity of follow-up scans in longitudinal MRI studies. I'll present 
 a sampling and reconstruction framework that exploits the redundancy of th
 e acquired data in longitudinal studies. This would rely on two extensions
  of compressed sensing\, adaptive-CS and weighted-CS.  In adaptive CS\, k-
 space sampling locations are optimized such that the acquired data is focu
 sed on the change between the follow-up MRI and the former one. Weighted C
 S uses the locations of the nonzero coefficients in the sparse domains as 
 a prior in the recovery process. Results are presented on MRI scans of pat
 ients with brain tumors\, and demonstrate improved spatial resolution and 
 accelerated acquisition for 2D and 3D brain imaging at 10-fold k-space und
 ersampling.\n\n
LOCATION:LR5\, Cambridge University Engineering Department
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