  Synthesizing medical images, such as PET, is a challenging task due to the
fact that the intensity range is much wider and denser than those in
photographs and digital renderings and are often heavily biased toward zero.
Above all, intensity values in PET have absolute significance, and are used to
compute parameters that are reproducible across the population. Yet, usually
much manual adjustment has to be made in pre-/post- processing when
synthesizing PET images, because its intensity ranges can vary a lot, e.g.,
between -100 to 1000 in floating point values. To overcome these challenges, we
adopt the Bidirectional Encoder Representations from Transformers (BERT)
algorithm that has had great success in natural language processing (NLP),
where wide-range floating point intensity values are represented as integers
ranging between 0 to 10000 that resemble a dictionary of natural language
vocabularies. BERT is then trained to predict a proportion of masked values
images, where its "next sentence prediction (NSP)" acts as GAN discriminator.
Our proposed approach, is able to generate PET images from MRI images in wide
intensity range, with no manual adjustments in pre-/post- processing. It is a
method that can scale and ready to deploy.
