Image synthesis methods in medical and biomedical imaging are able to augment image datasets, increase image resolution, fill missing or incomplete data, derive data in one modality using another modality, prepare perfect annotation, etc. These use cases (and not only those) are mentioned in the second part of this book. The first part of the book is more theoretical and explains the basic principles of image synthesis methods, how they work and how to implement them. Both parts together form a book that gives the reader insight into the technical background of image synthesis and how it is used, in the particular disciplines of medical and biomedical imaging. The book ends with some perspectives on the best practices to adopt when validating image synthesis approaches, the crucial role that uncertainty quantification plays in medical image synthesis and research directions that should be worth exploring in the future.