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Positron emission tomography (PET) serves as an essential tool for diagnosisof encephalopathy and brain science research. However, it suffers from thelimited choice of tracers. Nowadays, with the wide application of PET imagingin neuropsychiatric treatment, 6-18F-fluoro-3, 4-dihydroxy-L-phenylalanine(DOPA) has been found to be more effective than 18F-labeledfluorine-2-deoxyglucose (FDG) in the field. Nevertheless, due to the complexityof its preparation and other limitations, DOPA is far less widely used thanFDG. To address this issue, a tracer conversion invertible neural network(TC-INN) for image projection is developed to map FDG images to DOPA imagesthrough deep learning. More diagnostic information is obtained by generatingPET images from FDG to DOPA. Specifically, the proposed TC-INN consists of twoseparate phases, one for training traceable data, the other for rebuilding newdata. The reference DOPA PET image is used as a learning target for thecorresponding network during the training process of tracer conversion.Meanwhile, the invertible network iteratively estimates the resultant DOPA PETdata and compares it to the reference DOPA PET data. Notably, the reversiblemodel employs variable enhancement technique to achieve better powergeneration. Moreover, image registration needs to be performed before trainingdue to the angular deviation of the acquired FDG and DOPA data information.Experimental results exhibited excellent generation capability in mappingbetween FDG and DOPA, suggesting that PET tracer conversion has great potentialin the case of limited tracer applications.

PET Tracer Conversion among Brain PET via Variable Augmented Invertible Network