ADVANCED ULTRASOUND IN DIAGNOSIS AND THERAPY >

2024 , Vol. 8 >Issue 3: 86 - 92

DOI: https://doi.org/10.37015/AUDT.2024.240023

Review Articles

Deep Learning in Ultrasound Localization Microscopy

  • Yuhang Zheng, BS ,
  • Jianqiao Zhou, MD
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  • aDepartment of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
    bCollege of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, 197 Ruijin Er Road, 200025 Shanghai, China. e-mail: zhousu30@126.com

Received date: 2024-06-03

  Revised date: 2024-07-10

  Accepted date: 2024-08-15

  Online published: 2024-10-16

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Abstract

Ultrasound imaging holds a significant position in medical diagnostics due to its non-invasive and real-time capabilities. However, traditional ultrasound is constrained by the diffraction limit, making it challenging to capture fine blood vessels. Ultrasound localization microscopy (ULM) overcomes this limitation by achieving super-resolution imaging through tracking the trajectories of microbubbles (MBs) within microvasculature. This review summarizes the applications of deep learning (DL) techniques in ULM post-processing algorithms, including key steps such as beamforming, clutter filtering and denoising, localization, and tracking. Although DL shows great potential in improving ULM imaging quality and efficiency, current research mainly focuses on imaging algorithmic improvements rather than in-depth image analysis. In the future, with the accumulation of ULM image data, the powerful feature extraction capability of DL is expected to further advance ULM applications in disease prediction and diagnosis.

Key words： Ultrasound localization microscopy; Deep learning; Ultrasound microvasculomics

Cite this article

Yuhang Zheng, BS , Jianqiao Zhou, MD . Deep Learning in Ultrasound Localization Microscopy[J]. ADVANCED ULTRASOUND IN DIAGNOSIS AND THERAPY, 2024 , 8(3) : 86 -92 . DOI: 10.37015/AUDT.2024.240023

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