Toward patient-specific estimation of hepatic tumors respiratory motion: A finite element-based machine learning approach

Maryam Berijanian, Hamid Naghibi Beidokhti, Beril Sirmaçek, Momen Abayazid

    Research output: Contribution to conferencePaper

    Abstract

    Purpose
    To develop and evaluate an approach to estimate the respiratory-induced motion of hepatic lesions.

    Materials and methods
    In this study, a correlation between the collected surrogate signals and the liver tumor respiratory motion is obtained using learning-based algorithms. A robotic phantom is developed which simulates the respiratory motion of the liver, the diaphragm, and the abdomen skin in two directions as superior-inferior (SI) and anterior-posterior (AP). The surrogate signals are collected by means of optical markers attached to the abdomen skin and tracked by a digital camera, in addition to an inertial measurement unit (IMU) fixed to the hub of a needle that is inserted into the tissue. A finite element model (FEM) is developed to study the effect of tissue and tumor location parameters on target motion.

    Results
    The estimation error of linear regression for the SI and AP directions has been respectively 1.37% and 2.87%, and for quadratic polynomial regression have been 0.76% and 2.41% on the data from the experiments.

    Conclusion
    Using more than one surrogate signals resulted in a about 0.5-6.5% decrease of the motion estimation error compared to using only one of the surrogates.

    Keywords: hepatic tumor, motion estimation, respiratory motion, machine learning, robotic phantom, finite element analysis
    Original languageEnglish
    Publication statusPublished - 2019
    Event33rd International Conference on Computer Assisted Radiology and Surgery 2019 - Rennes Conference Center, Rennes, France
    Duration: 17 Jun 201921 Jun 2019
    Conference number: 33
    https://www.cars2019.org/

    Conference

    Conference33rd International Conference on Computer Assisted Radiology and Surgery 2019
    Abbreviated titleCARS 2019
    CountryFrance
    CityRennes
    Period17/06/1921/06/19
    Internet address

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  • Cite this

    Berijanian, M., Naghibi Beidokhti, H., Sirmaçek, B., & Abayazid, M. (2019). Toward patient-specific estimation of hepatic tumors respiratory motion: A finite element-based machine learning approach. Paper presented at 33rd International Conference on Computer Assisted Radiology and Surgery 2019, Rennes, France.