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

Machine Learning;Computer Vision and Pattern Recognition