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Title page for ETD etd-03272014-152811


Type of Document Master's Thesis
Author Wu, Yifei
Author's Email Address yifei.wu@vanderbilt.edu
URN etd-03272014-152811
Title Registration of Liver Images to Minimally Invasive Intraoperative Surface and Subsurface Data
Degree Master of Science
Department Biomedical Engineering
Advisory Committee
Advisor Name Title
Michael Miga Committee Chair
Robert Galloway Committee Member
Keywords
  • hepatic
  • subsurface
  • deformation
  • liver
  • nonrigid
  • registration
  • Minimally invasive
  • surgery
  • surface
Date of Defense 2014-03-29
Availability unrestricted
Abstract
Laparoscopic liver resection is increasing accepted as a standard of care with results comparable to open cases while incurring less trauma and reducing recovery time. The tradeoff is increased difficulty due to limited visibility and restricted freedom of movement. Image-guided surgical navigation systems can help localize anatomical features to improve patient safety and achieve negative surgical margins.

Previous research has demonstrated that intraoperative surface data can be used to drive a finite element tissue mechanics organ model such that high resolution preoperative scans are registered and visualized in the context of the current surgical pose. In this paper we present an investigation of using sparse data as imposed by laparoscopic limitations to drive a registration model. Surface swabs and subsurface data were used in tandem to reconstruct a displacement field on the posterior of the organ to optimize the fit between the intraoperative data and the preoperative liver model. Tests based on laboratory phantoms were used to validate the potential of this approach. Experimental results based on a liver phantom demonstrate that Target Registration Errors (TRE) on the order of 5mm were achieving using only surface swab data, while use of only subsurface data yielded errors of about 6mm. Registrations using a combination of both datasets achieved TRE on the order or 2.4mm and represent a sizeable improvement over either dataset alone.

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