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Synthesizing medical images, such as PET, is a challenging task due to thefact that the intensity range is much wider and denser than those inphotographs and digital renderings and are often heavily biased toward zero.Above all, intensity values in PET have absolute significance, and are used tocompute parameters that are reproducible across the population. Yet, usuallymuch manual adjustment has to be made in pre-/post- processing whensynthesizing PET images, because its intensity ranges can vary a lot, e.g.,between -100 to 1000 in floating point values. To overcome these challenges, weadopt 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 integersranging between 0 to 10000 that resemble a dictionary of natural languagevocabularies. BERT is then trained to predict a proportion of masked valuesimages, where its "next sentence prediction (NSP)" acts as GAN discriminator.Our proposed approach, is able to generate PET images from MRI images in wideintensity range, with no manual adjustments in pre-/post- processing. It is amethod that can scale and ready to deploy.

GANBERT: Generative Adversarial Networks with Bidirectional Encoder Representations from Transformers for MRI to PET synthesis