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. Juttukonda^3,4, Shaney Flores¹, Tammie L. S. Benzinger^1,5, 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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