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  • 4 - Deep Algorithm Unrolling for Biomedical Imaging

  • from Part I - Theory of Deep Learning for Image Reconstruction
  • Edited by Jong Chul Ye, Korea Advanced Institute of Science and Technology (KAIST), Yonina C. Eldar, Weizmann Institute of Science, Israel, Michael Unser, École Polytechnique Fédérale de Lausanne
  • Book:
    Deep Learning for Biomedical Image Reconstruction
    Published online:
    15 September 2023
    Print publication:
    12 October 2023, pp 53-86

  • Summary

    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.