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
11 - Ultrasound Image Artifact Removal using Deep Neural Networks
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
Ultrasound imaging (US) is susceptible to several types of artifacts. Most artifacts appear because of transducer limitations and simplified assumptions on the wave propagation. The artifacts are sometimes used as a component that contains tissue information; however, they often lead to a misinterpretation in the clinical diagnosis. Therefore, to improve the clinical utility of ultrasound in difficult-to-image patients and settings, a number of artifact removal methods have been proposed that aim at boosting image quality. Classical optimization-based methods have severe limitations due to their limited performance and high computation requirements. Furthermore, it is difficult to obtain parameters for producing high-quality output. A quick remedy for the aforementioned issues is the deep learning approach, which offers high performance compared with the traditional methods despite the significantly reduced runtime complexity. Another big advantage is that the same parameters as those learned during the training phase can be used to process different input images. This has motivated the scientific community to design deep-neural-network-based approaches for US artifact removal tasks.
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- Deep Learning for Biomedical Image Reconstruction , pp. 252 - 276Publisher: Cambridge University PressPrint publication year: 2023