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Reconstruction methods for sparse-data tomography

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  • UserSamuli Siltanen (University of Helsinki)
  • ClockMonday 21 November 2016, 14:00-15:00
  • HouseMR 14, CMS.

If you have a question about this talk, please contact Carola-Bibiane Schoenlieb.

Joint ACA-cmih seminar

The aim of classical tomography is to recover the inner structure of a physical body from X-ray images taken from all around the body. The mathematical model behind tomography, applicable to a wide range of practical 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 regularization methods have been proposed for tomographic reconstruction. Discussed here are total (generalized) variation regularization and sparsity-promoting methods using multiscale transforms such as wavelets and shearlets. A low-dose 3D dental X-ray imaging product is presented as a practical example.

This talk is part of the Applied and Computational Analysis series.

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