Literature DB >> 33258091

Analyzing Liver Surface Indentation for In Vivo Refinement of Tumor Location in Minimally Invasive Surgery.

Yingqiao Yang1, Kai-Leung Yung2, Tin Wai Robert Hung2, Kai-Ming Yu2.   

Abstract

Manual palpation to update the position of subsurface tumor(s) is a normal practice in open surgery, but is not possible through the small incisions of minimally invasive surgery (MIS). This paper proposes a method that has the potential to use a simple constant-force indenter and the existing laparoscopic camera for tumor location refinement in MIS. The indenter floats with organ movement to generate a static surface deformation on the soft tissue, resolving problems of previous studies that require complicated measurement of force and displacement during indentation. By analyzing the deformation profile, we can intraoperatively update the tumor's location in real-time. Indentation experiments were conducted on healthy and "diseased" porcine liver specimens to obtain the deformation surrounding the indenter site. An inverse finite element (FE) algorithm was developed to determine the optimal material parameters of the healthy liver tissue. With these parameters, a computational model of tumorous tissue was constructed to quantitatively evaluate the effects of the tumor location on the induced deformation. By relating the experimental data from the "diseased" liver specimen to the computational results, we estimated the radial distance between the tumor and the indenter, as well as the angular position of the tumor relative to the indenter.

Entities:  

Keywords:  Inverse finite element analysis; Robotic-assisted minimally invasive surgery; Soft tissue modeling; Surgical indentation; Tumor locating

Year:  2020        PMID: 33258091     DOI: 10.1007/s10439-020-02698-4

Source DB:  PubMed          Journal:  Ann Biomed Eng        ISSN: 0090-6964            Impact factor:   3.934


  1 in total

1.  Rolling mechanical imaging for tissue abnormality localization during minimally invasive surgery.

Authors:  Hongbin Liu; David P Noonan; Benjamin J Challacombe; Prokar Dasgupta; Lakmal D Seneviratne; Kaspar Althoefer
Journal:  IEEE Trans Biomed Eng       Date:  2009-09-29       Impact factor: 4.538

  1 in total
  1 in total

1.  Application of U-Net with Global Convolution Network Module in Computer-Aided Tongue Diagnosis.

Authors:  Meng-Yi Li; Ding-Ju Zhu; Wen Xu; Yu-Jie Lin; Kai-Leung Yung; Andrew W H Ip
Journal:  J Healthc Eng       Date:  2021-11-18       Impact factor: 2.682

  1 in total

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