Book contents
- Frontmatter
- Dedication
- Contents
- List of Contributors
- Preface
- Part I Theory of Deep Learning for Image Reconstruction
- Part II Deep-Learning Architecture for Various Imaging Architectures
- Part III Generative Models for Biomedical Imaging
- 12 Image Synthesis in Multi-Contrast MRI with Generative Adversarial Networks
- 13 Regularizing Deep-Neural-Network Paradigm for the Reconstruction of Dynamic Magnetic Resonance Images
- 14 Regularizing Neural Network for Phase Unwrapping
15 - CryoGAN: A Deep Generative Adversarial Approach to Single-Particle Cryo-EM
from Part III - Generative Models for Biomedical Imaging
Published online by Cambridge University Press: 15 September 2023
Book contents
- Frontmatter
- Dedication
- Contents
- List of Contributors
- Preface
- Part I Theory of Deep Learning for Image Reconstruction
- Part II Deep-Learning Architecture for Various Imaging Architectures
- Part III Generative Models for Biomedical Imaging
- 12 Image Synthesis in Multi-Contrast MRI with Generative Adversarial Networks
- 13 Regularizing Deep-Neural-Network Paradigm for the Reconstruction of Dynamic Magnetic Resonance Images
- 14 Regularizing Neural Network for Phase Unwrapping
Summary
CryoGAN uses ideas from deep generative adversarial learning to perform image reconstruction in single-particle cryo-electron microscopy (cryo-EM). In this chapter, we begin by introducing single-particle cryo-EM. We then formulate the associated image-reconstruction problem and discuss the main solutions found in the literature. Next, we describe the CryoGAN algorithm and show some representative results. Finally, we discuss what our experiences with Cryo-GAN suggest about the advantages and disadvantages of such deep generative adversarial methods in single-particle cryo-EM and beyond.
- Type
- Chapter
- Information
- Deep Learning for Biomedical Image Reconstruction , pp. 325 - 342Publisher: Cambridge University PressPrint publication year: 2023