Medical ultrasound imaging is a widely adopted, non-invasive diagnostic modality valued for its real-time visualisation capabilities and safety. A key component of ultrasound imaging is Beamforming, the signal processing technique used to focus and combine echoes to form high-quality images. However, traditional beamforming methods, such as delay-and-sum, are computationally intensive and often limit imaging speed, thereby constraining real-time applications. Ultra-fast (UF) beamforming algorithms have emerged to overcome these challenges by leveraging advanced signal processing strategies, parallel computing architectures, and, increasingly, artificial intelligence techniques. These innovations enable rapid image acquisition without compromising spatial resolution or contrast, thus expanding the potential for dynamic imaging of fast physiological processes. This chapter provides a comprehensive review of UF beamforming algorithms, discussing their operational principles, performance trade-offs, and implementation strategies. It also xplores future perspectives, including hybrid beamforming approaches, adaptive algorithms tailored to patient-specific characteristics, and integration with complementary imaging modalities, all aimed at advancing the capabilities and clinical value of medical ultrasound imaging.
Author(s) Details
Sreejeesh SG
VLSI Group, National Institute of Electronics and Information Technology,
Calicut (NIELIT) NIT Campus Post, Calicut, India.
Sakthivel R
Vellore Institute of Technology (VIT), Katpadi, Tamil Nadu, India.
Jayaraj U Kidav
National Institute of Electronics and Information Technology, Aurangabad,
(NIELIT) Maharashtra, India.
Please see the book here:- https://doi.org/10.9734/bpi/msraa/v9/6130
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