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

Research output: Contribution to conferencePaperAcademicpeer-review

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

Fingerprint

Learning systems
Tumors
Motion estimation
Liver
Skin
Robotics
Tissue
Units of measurement
Digital cameras
Diaphragms
Linear regression
Needles
Error analysis
Polynomials
Finite element method
Experiments

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.
Berijanian, Maryam ; Naghibi Beidokhti, Hamid ; Sirmaçek, Beril ; Abayazid, Momen . / 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.
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title = "Toward patient-specific estimation of hepatic tumors respiratory motion: A finite element-based machine learning approach",
abstract = "PurposeTo develop and evaluate an approach to estimate the respiratory-induced motion of hepatic lesions.Materials and methodsIn 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.ResultsThe 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.ConclusionUsing 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",
author = "Maryam Berijanian and {Naghibi Beidokhti}, Hamid and Beril Sirma{\cc}ek and Momen Abayazid",
year = "2019",
language = "English",
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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, 17/06/19 - 21/06/19, .

Toward patient-specific estimation of hepatic tumors respiratory motion: A finite element-based machine learning approach. / Berijanian, Maryam; Naghibi Beidokhti, Hamid ; Sirmaçek, Beril ; Abayazid, Momen .

2019. Paper presented at 33rd International Conference on Computer Assisted Radiology and Surgery 2019, Rennes, France.

Research output: Contribution to conferencePaperAcademicpeer-review

TY - CONF

T1 - Toward patient-specific estimation of hepatic tumors respiratory motion: A finite element-based machine learning approach

AU - Berijanian, Maryam

AU - Naghibi Beidokhti, Hamid

AU - Sirmaçek, Beril

AU - Abayazid, Momen

PY - 2019

Y1 - 2019

N2 - PurposeTo develop and evaluate an approach to estimate the respiratory-induced motion of hepatic lesions.Materials and methodsIn 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.ResultsThe 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.ConclusionUsing 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

AB - PurposeTo develop and evaluate an approach to estimate the respiratory-induced motion of hepatic lesions.Materials and methodsIn 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.ResultsThe 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.ConclusionUsing 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

M3 - Paper

ER -

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