This chapter provides a summary of some popular model-based deep learning methods and their extensions. Section 8.1 briefly describes classical model-based methods and their benefit as well as limitations. Section 8.2 describes how deep learning can help in overcoming some limitations of classical model-based methods. Section 8.3 discusses how to incorporate a pre-trained deep network as a regularizer using the plug-and-play approach. Section 8.4 describes end-to-end training using a model-based deep learning framework. This section also discusses some benefits and limitations of end-to-end training. Section 8.5 and 8.6 describe unsupervised model-based deep learning approaches when a clean training dataset is not available. Section 8.6 considers model mismatch issues as well as the joint design of acquisition and reconstruction frameworks.

This chapter focuses on biomedical image reconstruction methods at the intersection of MBIR and machine learning. After briefly reviewing classical MBIR methods for image reconstruction, we discuss the combination of MBIR with unsupervised learning, supervised learning, or both. Such combinations offer potential advantages for learning even with limited data.