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SUMMARY:Reconstruction methods for sparse-data tomography - Samuli Siltane
 n (University of Helsinki)
DTSTART:20161121T140000Z
DTEND:20161121T150000Z
UID:TALK67289@talks.cam.ac.uk
CONTACT:Carola-Bibiane Schoenlieb
DESCRIPTION:The aim of classical tomography is to recover the inner struct
 ure of a physical body from X-ray images taken from all around the body. T
 he mathematical model behind tomography\, applicable to a wide range of pr
 actical applications\, is to reconstruct a function from the knowledge of 
 integrals of the function over a collection of lines. This is an ill-posed
  inverse problem\, especially so if the collection of lines is restricted.
  Such restrictions arise for example in medical imaging when the radiation
  dose to the patient is minimized. In recent years\, many powerful regular
 ization methods have been proposed for tomographic reconstruction. Discuss
 ed here are total (generalized) variation regularization and sparsity-prom
 oting methods using multiscale transforms such as wavelets and shearlets. 
 A low-dose 3D dental X-ray imaging product is presented as a practical exa
 mple.
LOCATION:MR 14\, CMS
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