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Abstract #5032

Deep learning-based synthesis of TSPO PET from T1-weighted MRI images only

Matteo Ferrante1, Marianna Inglese2, Ludovica Brusaferri3, Marco L Loggia3, and Nicola Toschi4,5
1Biomedicine and prevention, University of Rome Tor Vergata, Roma, Italy, 2Biomedicine and prevention, University of Rome Tor Vergata, Rome, Italy, 3Martinos Center For Biomedical Imaging, MGH and Harvard Medical School (USA), Boston, MA, United States, 4BioMedicine and prevention, University of Rome Tor Vergata, Rome, Italy, 5Department of Radiology,, Athinoula A. Martinos Center for Biomedical Imaging and Harvard Medical school, Boston, MA, USA, Boston, MA, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Neuroinflammation, PET, image synthesisChronic pain-related biomarkers can be found using a specific binding radiotracer called [11C]PBR28 able to target the translocator protein (TSPO), whose expression is increased in activated glia and can be considered as a biomarker for neuroinflammation. One of the main drawbacks of PET imaging is radiation exposure, for which we attempted to develop a deep learning model able to synthesize PET images of the brain from T1w MRI only. Our model produces synthetic TSPO-PET images from T1W MRI which are statistically indistinguishable from the original PET images both on a voxel-wise and on a ROI-wise level.

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Keywords