Published on Fri Jun 26 2020

Pushing the Limit of Unsupervised Learning for Ultrasound Image Artifact Removal

Shujaat Khan, Jaeyoung Huh, Jong Chul Ye

Ultrasound (US) imaging is a fast and non-invasive imaging modality. It often suffers from poor visual quality from various origins. Classical methods to deal with these problems include image-domain signal processing approaches. Recently, deep learning approaches have successfully used for ultrasound imaging field.

0
0
0
Abstract

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.

Fri Jul 10 2020
Machine Learning
OT-driven Multi-Domain Unsupervised Ultrasound Image Artifact Removal using a Single CNN
Ultrasound imaging (US) often suffers from distinct image artifacts from various sources. Classic approaches for solving these problems are usually model-based iterative approaches. A single neural network can be used to deal with different types of US artifacts simply by changing a mask vector.
0
0
0
Fri Aug 28 2020
Machine Learning
CNN-Based Image Reconstruction Method for Ultrafast Ultrasound Imaging
Ultrafast ultrasound (US) revolutionized biomedical imaging with its ability to acquire full-view frames at over 1 kHz. Yet, US imaging suffers from strong diffraction artifacts, mainly caused by grating lobes, side lobes or edge waves. Multiple acquisitions are typically required to achieve sufficient image quality. We propose a two-step convolutional neural network (
0
0
0
Fri Sep 04 2020
Machine Learning
Fast ultrasonic imaging using end-to-end deep learning
Ultrasonic imaging algorithms used in many clinical and industrial applications consist of three steps: A data pre-processing, an image formation and an image post-processing step. In this work, we propose a novel deep learning architecture that integrates all three steps to enable end-to-end training.
0
0
0
Tue Apr 09 2019
Computer Vision
End-to-End Learning-Based Ultrasound Reconstruction
Ultrasound imaging is caught between the quest for the highest image quality, and the necessity for clinical usability. We propose a novel fully convolutional neural network for ultrasound reconstruction.
0
0
0
Fri Jul 05 2019
Machine Learning
Deep learning in ultrasound imaging
We consider deep learning strategies in ultrasound systems, from the front-end to advanced applications. These emerging technologies may have considerable impact on ultrasound imaging. We outline efficient and effective deep learning solutions for adaptive beamforming and adaptive spectral Doppler.
0
0
0
Mon Aug 03 2020
Computer Vision
3D B-mode ultrasound speckle reduction using deep learning for 3D registration applications
Ultrasound (US) speckles are granular patterns which can impede image segmentation and registration. We show that our deep learning framework can obtain similar suppression and mean preservation index (1.066) on speckle reduction.
0
0
0