Noise reduction for ultrasound images using deep interpolation

Li, Xiaobai

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https://hdl.handle.net/2142/120586 Copy

Description

Title

Noise reduction for ultrasound images using deep interpolation

Author(s)

Li, Xiaobai

Issue Date

2023-05-03

Director of Research (if dissertation) or Advisor (if thesis)

Song, Pengfei

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Ultrasound
• Noise Reduction
• Deep Interpolation

Abstract

Ultrasound imaging has been proven to be a safe and effective method of detecting signals in the body related to physiological parameters such as anatomy, blood flow, and stiffness, aiding in the diagnosis of various diseases. However, the quality of these images can be compromised by the low signal-to-noise ratio (SNR) caused by electronic interference noise. Traditional methods like frame averaging can increase SNR, but they require a large number of input frames to produce a single frame of high-SNR output ultrasound images and are ineffective when motion is present as the output image might be blurry. Deep-learning-based denoising techniques have been developed throughout the years, but they either require ground truth images or identical signals in all time frames, which are unachievable in in-vivo ultrasound imaging. To address these limitations, we utilized a U Net-based encoder-decoder network called Deep Interpolation, which uses 40 input frames to reconstruct a high-quality noise-reduced output ultrasound image without ground truth and can be used for both static and in-vivo ultrasound images.

Graduation Semester

2023-05

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/120586

Copyright and License Information

Copyright 2023 Xiaobai Li

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