Original paper
Self‐supervised learning for accelerated 3D high‐resolution ultrasound imaging
Abstract
Purpose Ultrasound (US) imaging has been widely used in diagnosis, image‐guided intervention, and therapy, where high‐quality three‐dimensional (3D) images are highly desired from sparsely acquired two‐dimensional (2D) images. This study aims to develop a deep learning‐based algorithm to reconstruct high‐resolution (HR) 3D US images only reliant on the acquired sparsely distributed 2D images. Methods We propose a self‐supervised learning...
Paper Details
Title
Self‐supervised learning for accelerated 3D high‐resolution ultrasound imaging
Published Date
Jun 2, 2021
Journal
Volume
48
Issue
7
Pages
3916 - 3926
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Notes
History