A deep learning based vascular wall motion tracking for ultrasound imaging

Jeongwung Seo, Leang Sim Nguon, Suhyun Park

Research output: Contribution to journalConference articlepeer-review


Cardiovascular diseases (CVDs) can be found in early stage by discovering unhealthy vascular characteristics such as arterial stiffness and aging. Vascular wall motion is related to these characteristics. A deep learning model is proposed to track the vascular wall motion in the ultrasound image. Ultrasound data were acquired using ultrasound imaging system. To train the deep learning model, data were generated from simulation by modeling the ultrasound data reflecting the geometry of the carotid artery, its motion (e.g., cardiac, respirational, and body movement), and noise on the signal. The trained deep learning model is tested on the data acquired from simulation and tissue-mimicking phantom experiment. The performance of the proposed method on the vascular wall motion tracking is compared with the traditional motion tracking algorithm using cross-correlation. The proposed model yielded better performance than the cross-correlation based method in simulation data and comparable performance in tissue-mimicking phantom experiment. This study presents the feasibility of the deep learning approach applied for the ultrasound vascular imaging application.

Original languageEnglish
JournalProceedings of the International Congress on Acoustics
StatePublished - 2022
Event24th International Congress on Acoustics, ICA 2022 - Gyeongju, Korea, Republic of
Duration: 24 Oct 202228 Oct 2022

Bibliographical note

Funding Information:
This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT under grant number NRF-2020R1A2C1011889.

Publisher Copyright:
© ICA 2022.All rights reserved


  • RNN-based deep learning model
  • ultrasound vascular imaging
  • vascular wall motion tracking


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