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MR-Based Attenuation Correction for PET/MR

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83974

Hofmann,  M
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84235

Steinke,  F
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83809

Bezrukov,  I
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons84193

Kolb A, Aschoff P, Lichy M, Erb M, Nägele T, Brady M, Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Hofmann, M., Steinke, F., Bezrukov, I., Kolb A, Aschoff P, Lichy M, Erb M, Nägele T, Brady M, Schölkopf, B., & Pichler, B. (2009). MR-Based Attenuation Correction for PET/MR. Poster presented at 17th Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM 2009), Honolulu, HI, USA.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-C567-8
Abstract
There has recently been a growing interest in combining PET and MR. Attenuation correction (AC), which accounts for radiation attenuation properties of the tissue, is mandatory for quantitative PET. In the case of PET/MR the attenuation map needs to be determined from the MR image. This is intrinsically difficult as MR intensities are not related to the electron density information of the attenuation map. Using ultra-short echo (UTE) acquisition, atlas registration and machine learning, we present methods that allow prediction of the attenuation map based on the MR image both for brain and whole body imaging.