Developing a deformable model of liver tumor during breathing to improve targeting accuracy in image-guided therapy using finite element simulation

New advances in computer technology for biomechanical numerical modeling of human body are the basis for the improvement of targeting accuracy. This is especially important for guiding surgeons during interventional procedures and locating of liver tumor for radiotherapy. This paper deals with investigating the motion and deformation of a tumor, embedded into liver, during respiration. Here, a 3D FE model of human liver as a whole is developed to simulate liver behavior during respiration. First, the cloud of point according to CT image data was imported into CATIA software. Then a spherical tumor was embedded into the different segments of liver tissue in ABAQUS. A quasi-linear hyperviscoelastic constitutive model and an elastic behavior were used to define the liver and tumor properties, respectively. Boundary conditions were defined based on the difference between end-exhale and end-inhale states of liver tissue. Deformation and motion of liver tumor was then determined at intermediate states of breathing. Finally, the new position and the deformed shape of the tumor were investigated, considering the increase of tumor stiffness. The results show that if the tumor is located in the segment VII, then maximum displacement in the y-direction is observed.

[1]  Govindarajan Srimathveeravalli,et al.  A study of porcine liver motion during respiration for improving targeting in image-guided needle placements , 2012, International Journal of Computer Assisted Radiology and Surgery.

[2]  Mahdi Moghimi Zand,et al.  Analytical solution and simulation of the liver tissue behavior under uniaxial compression test , 2016 .

[3]  Rüdiger Dillmann,et al.  Quadratic Corotated Finite Elements for Real-Time Soft Tissue Registration , 2012 .

[4]  B. Taouli,et al.  Value of tumor stiffness measured with MR elastography for assessment of response of hepatocellular carcinoma to locoregional therapy , 2017, Abdominal Radiology.

[5]  Lucian Gheorghe Gruionu,et al.  Development of a Finite Element Model for Lung Tumor Displacements During Breathing , 2016 .

[6]  A. Mescher Junqueira's Basic Histology , 2009 .

[7]  Sophia Mã ¶ ller,et al.  Biomechanics — Mechanical properties of living tissue , 1982 .

[8]  Jef Vandemeulebroucke,et al.  The long- and short-term variability of breathing induced tumor motion in lung and liver over the course of a radiotherapy treatment. , 2017, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[9]  Alireza Mirbagheri,et al.  A Mass-Spring-Damper Model for Real Time Simulation of the Frictional Grasping Interactions between Surgical Tools and Large Organs , 2015 .

[10]  M M Zand,et al.  Proposing a new nonlinear hyperviscoelastic constitutive model to describe uniaxial compression behavior and dependence of stress-relaxation response on strain levels for isotropic tissue-equivalent material , 2018, Scientia Iranica.

[11]  Cagatay Basdogan,et al.  A robotic indenter for minimally invasive measurement and characterization of soft tissue response , 2007, Medical Image Anal..

[12]  Johannes Sobotta,et al.  Sobotta Atlas der Anatomie des Menschen , 1988 .

[13]  Farzam Farahmand,et al.  A meshless method to simulate interactions between large soft tissue and a surgical grasper , 2016 .

[14]  Reza Faghihi,et al.  A FAST MODEL FOR PREDICTION OF RESPIRATORY LUNG MOTION FOR IMAGE-GUIDED RADIOTHERAPY: A FEASIBILITY STUDY , 2012 .

[15]  Y. Fung,et al.  Biomechanics: Mechanical Properties of Living Tissues , 1981 .

[16]  C. Basdogan,et al.  Correlation between the mechanical and histological properties of liver tissue. , 2014, Journal of the mechanical behavior of biomedical materials.

[17]  Y. Payan,et al.  FINITE ELEMENT MODEL OF THE LIVER FOR COMPUTER-ASSISTED HEPATIC TUMOR ABLATION , 2003 .

[18]  Alessandro Nava,et al.  In vivo mechanical characterization of human liver , 2008, Medical Image Anal..

[19]  J. Baxa,et al.  PET/MRI of the thorax, abdomen and retroperitoneum: Benefits of the breathing-synchronized scanning. , 2017, European journal of radiology.

[20]  Steve B. Jiang,et al.  The management of respiratory motion in radiation oncology report of AAPM Task Group 76. , 2006, Medical physics.