The sensitive and efficient detection of quadriceps muscle thickness changes in cross-sectional plane using ultrasonography: A feasibility investigation

Jizhou Li, Yongjin Zhou, Yi Lu, Guangquan Zhou, Lei Wang, Yongping Zheng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

22 Citations (Scopus)

Abstract

As a direct determinant parameter to quantify muscle activity, the muscle thickness (MT) has been investigated in many aspects and for various purposes. Ultrasonography (US) is a promising modality to detect muscle morphological changes during contractions since it is portable, noninvasive, and real time. However, there are few reports on sensitive and efficient estimation of changes of MT in a cross-sectional plane. In this feasibility investigation, we proposed a coarse-to-fine method based on a compressive-tracking algorithm for estimation of MT changes during an example task of isometric knee extension using ultrasound images. The sensitivity and efficiency are evaluated with 1920 US images from quadriceps muscle (QM) in eight subjects. The detection results were compared with those obtained from both traditional manual measurement and the well known normalized cross-correlation method, and the effect of the size of tracking window on detection performance was evaluated as well. It is demonstrated that the proposed method agrees well with the manual measurement. Meanwhile, it is not only sensitive to relatively small changes of MT but also computationally efficient.
Original languageEnglish
Article number6570488
Pages (from-to)628-635
Number of pages8
JournalIEEE Journal of Biomedical and Health Informatics
Volume18
Issue number2
DOIs
Publication statusPublished - 1 Jan 2014

Keywords

  • Compressive tracking
  • isometric knee extension
  • muscle thickness (MT)
  • normalized cross correlation (NCC)
  • quadriceps muscle (QM)
  • sonomyography

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

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