Semantic Scholar Open Access 2019 264 sitasi

Deep Learning in Ultrasound Imaging

Ruud J. G. van Sloun Regev Cohen Yonina C. Eldar

Lihat Sumber DOI

Abstrak

In this article, we consider deep learning strategies in ultrasound systems, from the front end to advanced applications. Our goal is to provide the reader with a broad understanding of the possible impact of deep learning methodologies on many aspects of ultrasound imaging. In particular, we discuss methods that lie at the interface of signal acquisition and machine learning, exploiting both data structure (e.g., sparsity in some domain) and data dimensionality (big data) already at the raw radio-frequency channel stage. As some examples, we outline efficient and effective deep learning solutions for adaptive beamforming and adaptive spectral Doppler through artificial agents, learn compressive encodings for the color Doppler, and provide a framework for structured signal recovery by learning fast approximations of iterative minimization problems, with applications to clutter suppression and super-resolution ultrasound. These emerging technologies may have a considerable impact on ultrasound imaging, showing promise across key components in the receive processing chain.

Topik & Kata Kunci

Computer Science Engineering

Penulis (3)

Ruud J. G. van Sloun

Regev Cohen

Yonina C. Eldar

Format Sitasi

APA MLA BibTeX

Sloun, R.J.G.v., Cohen, R., Eldar, Y.C. (2019). Deep Learning in Ultrasound Imaging. https://doi.org/10.1109/JPROC.2019.2932116

Akses Cepat

Lihat di Sumber doi.org/10.1109/JPROC.2019.2932116

Informasi Jurnal

Tahun Terbit: 2019
Bahasa: en
Total Sitasi: 264×
Sumber Database: Semantic Scholar
DOI: 10.1109/JPROC.2019.2932116
Akses: Open Access ✓