Abstract #3425
A New Region Based Volume Wised Method for PET-MR Imaging Using Artificial Neural Network
Chenguang Peng 1 , Rong Guo 1 , Yicheng Chen 1 , Yingmao Chen 2 , Quanzheng Li 3 , Georges El Fakhr 3 , and Kui Ying 1
1
Key Laboratory of Particle and Radiation
Imaging, Ministry of Education, Department of
Engineering, Beijing, China,
2
Department
of Nuclear Medicine, The general hospital of Chinese
People's Liberation, Beijing, China, Beijing, China,
3
Department
of Radiology, Division of Nuclear Medicine and Molecular
Imaging, Harvard Medical School, Boston, United States
PET is a practical medical imaging technique for brain
function diagnosis. However, the low spatial resolution
limits the use of PET in neurology and disease like
Alzheimer's disease. With the help of MRI-PET, people
can use high resolution MRI to provide anatomical
information to correct partial volume effect of PET
image which is a great cause for low resolution.
Nevertheless, traditional partial volume effect
correction method requires an accurate MRI segmentation
and PVE model estimation which are not usually
applicable. In this work, we proposed a method that is
insensitive to PVE model estimation error and
segmentation error.
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