Publications

Learning based approach for speed-of-sound adaptive Rx beamforming

AUTHORS: Young-Min Kim¹, Myeong-Gee Kim¹, Seok-Hwan Oh¹, Guil Jung¹, Hyeon-Min Bae¹
PUBLISHED: IEEE, International Symposium on Biomedical Imaging (ISBI)
DOI: 10.1109/IUS52206.2021.9593323

1. Department of Electrical Engineering, KAIST, Daejeon, South Korea

Abstract:

The conventional delay-and-sum algorithm is based on the assumption that a target object is composed of substances with identical speed-of-sound (SoS)(i.e. 1540 m/s) and proper delay is applied to received RF signals to synthesize output images. However, such an assumption compromises the resolution of images due to the inhomogeneity of body tissues. In this paper, we propose an SoS adaptive Rx beamforming method that generates high-resolution ultrasonic images. A neural network (NN) approach has been adopted to reconstruct SoS distribution and determine the accurate time-of-flight (ToF) of each channel from the generated SoS map.

Optical Flow Assisted Super-Resolution Ultrasound Localization Microscopy using Deep Learning

Learning-based Attenuation Quantification in Abdominal Ultrasound

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