  In this chapter, we review biomedical applications and breakthroughs via
leveraging algorithm unrolling, an important technique that bridges between
traditional iterative algorithms and modern deep learning techniques. To
provide context, we start by tracing the origin of algorithm unrolling and
providing a comprehensive tutorial on how to unroll iterative algorithms into
deep networks. We then extensively cover algorithm unrolling in a wide variety
of biomedical imaging modalities and delve into several representative recent
works in detail. Indeed, there is a rich history of iterative algorithms for
biomedical image synthesis, which makes the field ripe for unrolling
techniques. In addition, we put algorithm unrolling into a broad perspective,
in order to understand why it is particularly effective and discuss recent
trends. Finally, we conclude the chapter by discussing open challenges, and
suggesting future research directions.
