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
- 5 Deep Learning for CT Image Reconstruction
- 6 Deep Learning in CT Reconstruction: Bringing the Measured Data to Tasks
- 7 Overview of the Deep-Learning Reconstruction of Accelerated MRI
- 8 Model-Based Deep-Learning Algorithms for Inverse Problems
- 9 k-Space Deep Learning for MR Reconstruction and Artifact Removal
- 10 Deep Learning for Ultrasound Beamforming
- 11 Ultrasound Image Artifact Removal using Deep Neural Networks
- Part III Generative Models for Biomedical Imaging
10 - Deep Learning for Ultrasound Beamforming
from Part II - Deep-Learning Architecture for Various Imaging Architectures
Published online by Cambridge University Press: 15 September 2023
- 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
- 5 Deep Learning for CT Image Reconstruction
- 6 Deep Learning in CT Reconstruction: Bringing the Measured Data to Tasks
- 7 Overview of the Deep-Learning Reconstruction of Accelerated MRI
- 8 Model-Based Deep-Learning Algorithms for Inverse Problems
- 9 k-Space Deep Learning for MR Reconstruction and Artifact Removal
- 10 Deep Learning for Ultrasound Beamforming
- 11 Ultrasound Image Artifact Removal using Deep Neural Networks
- Part III Generative Models for Biomedical Imaging
Summary
With the present demand for high-quality image reconstruction and signal extraction from less (e.g., unfocused or parallel) transmissions that facilitate fast imaging, and the push towards compact probes, modern ultrasound imaging leans heavily on innovations in powerful digital receive channel processing. Beamforming, the process of mapping received ultrasound echoes to the spatial image domain, naturally lies at the heart of the ultrasound image formation chain. In this chapter, we discuss why and when deep learning methods can play a compelling role in the digital beamforming pipeline, and then show how these data-driven systems can be leveraged for improved ultrasound image reconstruction.
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- Chapter
- Information
- Deep Learning for Biomedical Image Reconstruction , pp. 223 - 251Publisher: Cambridge University PressPrint publication year: 2023
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