A Deep Learning Method for Motion Artifact Correction in Intravascular Photoacoustic Image Sequence-Reference-Cited by-同舟云学术

A Deep Learning Method for Motion Artifact Correction in Intravascular Photoacoustic Image Sequence

Published:2023-01 Issue:1 Volume:42 Page:66-78
ISSN:0278-0062
Container-title:IEEE Transactions on Medical Imaging
language:
Short-container-title:IEEE Trans. Med. Imaging

Author:

Zheng Sun¹^ORCID,Jiejie Du²,Yue Yao²,Qi Meng²,Huifeng Sun²

Affiliation:

1. Department of Electronic and Communication Engineering, Hebei Key Laboratory of Power Internet of Things Technology, North China Electric Power University, Baoding, China

2. Department of Electronic and Communication Engineering, North China Electric Power University, Baoding, China

Funder

National Natural Science Foundations of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Radiological and Ultrasound Technology,Software

Link

http://xplorestaging.ieee.org/ielx7/42/10003062/09869860.pdf?arnumber=9869860

Reference52 articles.

1. Suppression of motion artifacts in intravascular photoacoustic image sequences

2. Self-Gated Respiratory Motion Rejection for Optoacoustic Tomography

3. Accuracy in Prediction of Catheter Rotation in IVUS With Feature-Based Optical Flow—A Phantom Study

4. A study of coronary artery rotational motion with dense scale-space optical flow in intravascular ultrasound

5. Correction of motion artifacts in endoscopic optical coherence tomography and autofluorescence images based on azimuthal en face image registration

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