Abstract #0410
MR-based PET Attenuation Correction for Brain PET-MR Using Support Vector Machines
Yicheng Chen 1 , Di Cui 1,2 , Yingmao Chen 3 , Jinsong Ouyang 4 , Georges El Fakhri 4 , and Kui Ying 1
1
Key Laboratory of Particle and Radiation
Imaging, Ministry of Education, Department of
Engineering Physics, Tsinghua University, Beijing,
Beijing, China,
2
Department of Diagnostic
Radiology, The University of Hong Kong, Hong Kong,
China,
3
Department
of Nuclear Medicine, The general hospital of Chinese
People's Liberation, Beijing, China,
4
Department
of Radiology, Division of Nuclear Medicine and Molecular
Imaging, Harvard Medical School and Massachusetts
General Hospital, Boston, Massachusetts, United States
In this study, a novel method using support vector
machine (SVM) regression to predict continuous pseudo-CT
from MR T2 and UTE information for PET attenuation
correction is proposed. The SVM regression model is
trained and tested with patient data. Compared to
Gaussian mixture regression (GMR) model method, a
pseudo-CT attenuation correction approach, the proposed
method provides higher fidelity to the gold standard CT
with our limited data set.
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