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Abstract #0394

Deep learning based T1-enhanced selection of linear attenuation coefficients for PET/MR attenuation correction: accuracy and repeatability

Chunwei Ying1, Yasheng Chen2, Michael M. Binkley2, Meher R. Juttukonda3,4, Shaney Flores1, Tammie L. S. Benzinger1,5, and Hongyu An1
1Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, United States, 2Department of Neurology, Washington University School of Medicine, St. Louis, MO, United States, 3Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, 4Department of Radiology, Harvard Medical School, Boston, MA, United States, 5Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, United States

We proposed a 3D patch based residual U-Net method to estimate pseudo CT images for PET/MR attenuation correction by including quantitative R1 maps as input. The proposed deep learning based T1-enhanced selection of linear attenuation coefficients (DL-TESLA) method outperformed the deep learning methods using UTE-R2* or MPRAGE as inputs with a similar network structure. Moreover, we demonstrated that DL-TESLA had an excellent PET test-retest repeatability that was comparable to PET/CT, supporting its use for PET/MR AC in longitudinal studies of neurodegenerative diseases.

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