Introduction: Pre-clinical molecular imaging, particularly with mice, is an essential part of drug and radiopharmaceutical development. There remain ethical challenges to reduce, refine and replace animal imaging where possible. Method: A number of approaches have been adopted to reduce the use of mice including using algorithmic approaches to animal modelling. Digital twins have been used to create a virtual model of mice, however, exploring the potential of deep learning approaches to digital twin development may enhance capabilities and application in research. Results: Generative adversarial networks produce generated images that sufficiently resemble reality that they could be adapted to create digital twins. Specific genetic mouse models have greater homogeneity making them more receptive to modelling and suitable specifically for digital twin simulation. Conclusion: There are numerous benefits of digital twins in pre-clinical imaging including improved outcomes, fewer animal studies, shorter development timelines and lower costs.