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Vortrag

A Machine Learning Approach for Determining the PET Attenuation Map from Magnetic Resonance Images

MPG-Autoren
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/persons84193

Judenhofer MS, Claussen CD, Schölkopf,  B
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Hofmann, M., Steinke, F., Judenhofer MS, Claussen CD, Schölkopf, B., & Pichler, B. (2006). A Machine Learning Approach for Determining the PET Attenuation Map from Magnetic Resonance Images. Talk presented at IEEE Medical Imaging Conference 2006. San Diego, CA, USA.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-CF9F-D
Zusammenfassung
A promising new combination in multimodality imaging is MR-PET, where the high soft tissue contrast of Magnetic Resonance Imaging (MRI) and the functional information of Positron Emission Tomography (PET) are combined. Although many technical problems have recently been solved, it is still an open problem to determine the attenuation map from the available MR scan, as the MR intensities are not directly related to the attenuation values. One standard approach is an atlas registration where the atlas MR image is aligned with the patient MR thus also yielding an attenuation image for the patient. We also propose another approach, which to our knowledge has not been tried before: Using Support Vector Machines we predict the attenuation value directly from the local image information. We train this well-established machine learning algorithm using small image patches. Although both approaches sometimes yielded acceptable results, they also showed their specific shortcomings: The registration often fails with large deformations whereas the prediction approach is problematic when the local image structure is not characteristic enough. However, the failures often do not coincide and integration of both information sources is promising. We therefore developed a combination method extending Support Vector Machines to use not only local image structure but also atlas registered coordinates. We demonstrate the strength of this combination approach on a number of examples.