In simultaneous PET/MR imaging, PET attenuation correction is based on MRI, unlike PET/CT systems, which directly use CT measurements. Various approaches have been developed based on templates, atlas information, direct segmentation of T1-weighted MR images. In the present study, we introduced two approaches of UTE-based attenuation correction for simultaneous PET/MR imaging focusing on children’s brain, including segmentation-based method and Support Vector Machine (SVM) regression method. The results have been compared with Gaussian Mixture Regression (GMR) model method.
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