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Abstract #2693

PSEUDO-CT IMAGE GENERATION FROM MULTIPLE ECHO TIME 7 TESLA MR DATA VIA GAUSSIAN MIXTURE MODELLING

Shaeez Usman Abdulla1, David Reutens1, and Viktor Vegh1
1University of Queensland, Brisbane, Australia

With the increasing introduction of new PET-MR systems, MRI-based methods for pseudo-CT image generation for PET attenuation correction have to be made widely applicable. Existing methods requiring prior anatomical information for estimating CT values form MR signals have been shown to perform poorly, especially in patients. Ultrashort echo time (UTE) MR data has shown some promise, as it is sensitive to tissue classes captured in CT images. We applied existing MR-based methods and a new Gaussian mixture model approach relying on multi-echo MR magnitude and phase data for pseudo-CT image generation. The Gaussian mixture modelling method outperformed other methods investigated.

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