Using needle orientation sensing as surrogate signal for respiratory motion estimation in percutaneous interventions

Momen Abayazid (Corresponding Author), Takahisa Kato, Stuart G. Silverman, Nobuhiko Hata

    Research output: Contribution to journalArticleAcademicpeer-review

    6 Citations (Scopus)
    90 Downloads (Pure)


    To develop and evaluate an approach to estimate the respiratory-induced motion of lesions in the chest and abdomen.

    Materials and methods
    The proposed approach uses the motion of an initial reference needle inserted into a moving organ to estimate the lesion (target) displacement that is caused by respiration. The needles position is measured using an inertial measurement unit (IMU) sensor externally attached to the hub of an initially placed reference needle. Data obtained from the IMU sensor and the target motion are used to train a learning-based approach to estimate the position of the moving target. An experimental platform was designed to mimic respiratory motion of the liver. Liver motion profiles of human subjects provided inputs to the experimental platform. Variables including the insertion angle, target depth, target motion velocity and target proximity to the reference needle were evaluated by measuring the error of the estimated target position and processing time.

    The mean error of estimation of the target position ranged between 0.86 and 1.29 mm. The processing maximum training and testing time was 5 ms which is suitable for real-time target motion estimation using the needle position sensor.

    The external motion of an initially placed reference needle inserted into a moving organ can be used as a surrogate, measurable and accessible signal to estimate in real-time the position of a moving target caused by respiration; this technique could then be used to guide the placement of subsequently inserted needles directly into the target.
    Original languageEnglish
    Pages (from-to)125-133
    Number of pages9
    JournalInternational journal of computer assisted radiology and surgery
    Issue number1
    Early online date1 Aug 2017
    Publication statusPublished - 1 Jan 2018


    • UT-Hybrid-D
    • Machine learning
    • Magnetic resonance imaging
    • Motion compensation
    • Percutaneous needle insertion
    • Respiratory motion
    • Interventional radiology


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