Jaeyoung Huh

TL;DR: In this article, a deep learning approach was proposed to interpolate the missing RF data by utilizing redundancy in the Rx-Xmit plane, which can effectively reduce the data rate without sacrificing the image quality.

TL;DR: A deep neural network is designed to directly process full or subsampled radio frequency data acquired at various subsampling rates and detector configurations so that it can generate high-quality US images using a single beamformer.

Abstract: Recently, deep learning approaches have been successfully used for ultrasound (US) image artifact removal. However, paired high-quality images for supervised training are difficult to obtain in many practical situations. Inspired by the recent theory of unsupervised learning using optimal transport driven CycleGAN (OT-CycleGAN), here, we investigate the applicability of unsupervised deep learning for US artifact removal problems without matched reference data. Two types of OT-CycleGAN approaches are employed: one with the partial knowledge of the image degradation physics and the other with the lack of such knowledge. Various US artifact removal problems are then addressed using the two types of OT-CycleGAN. Experimental results for various unsupervised US artifact removal tasks confirmed that our unsupervised learning method delivers results comparable to supervised learning in many practical applications.

TL;DR: This paper proposes a novel deep learning approach that directly interpolates the missing RF data by utilizing redundancy in the Rx–Xmit plane and results confirm that the proposed method can effectively reduce the data rate without sacrificing the image quality.

TL;DR: It is demonstrated that a single data-driven adaptive beamformer designed as a deep neural network can generate high quality images robustly for various detector channel configurations and subsampling rates.