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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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