Pushing the Limit of Unsupervised Learning for Ultrasound Image Artifact Removal
This addresses the challenge of obtaining paired training data in ultrasound imaging, offering a practical solution for clinical applications, though it is incremental as it builds on existing unsupervised learning techniques.
The paper tackled ultrasound image artifact removal without needing paired high-quality reference images by applying an unsupervised learning method based on OT-cycleGAN, achieving results comparable to supervised learning across tasks like deconvolution and speckle removal.
Ultrasound (US) imaging is a fast and non-invasive imaging modality which is widely used for real-time clinical imaging applications without concerning about radiation hazard. Unfortunately, it often suffers from poor visual quality from various origins, such as speckle noises, blurring, multi-line acquisition (MLA), limited RF channels, small number of view angles for the case of plane wave imaging, etc. Classical methods to deal with these problems include image-domain signal processing approaches using various adaptive filtering and model-based approaches. Recently, deep learning approaches have been successfully used for ultrasound imaging field. However, one of the limitations of these approaches is that paired high quality images for supervised training are difficult to obtain in many practical applications. In this paper, inspired by the recent theory of unsupervised learning using optimal transport driven cycleGAN (OT-cycleGAN), we investigate applicability of unsupervised deep learning for US artifact removal problems without matched reference data. Experimental results for various tasks such as deconvolution, speckle removal, limited data artifact removal, etc. confirmed that our unsupervised learning method provides comparable results to supervised learning for many practical applications.