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Title page for ETD etd-11142017-122151


Type of Document Master's Thesis
Author Heiselman, Jon Stanley
URN etd-11142017-122151
Title Characterization and Correction of Intraoperative Soft Tissue Deformation in Image-Guided Laparoscopic Liver Surgery
Degree Master of Science
Department Biomedical Engineering
Advisory Committee
Advisor Name Title
Michael I. Miga Committee Chair
Benoit M. Dawant Committee Member
Keywords
  • laparoscopy
  • liver
  • soft tissue deformation
  • nonrigid registration
  • insufflation
Date of Defense 2017-11-10
Availability restricted
Abstract
Laparoscopic liver surgery is challenging to perform due to a compromised ability of the surgeon to localize subsurface anatomy in the constrained environment. While image guidance has the potential to address this barrier, intraoperative factors such as insufflation and variable degrees of organ mobilization from supporting ligaments may generate substantial deformation. The severity of laparoscopic deformation in humans has not been characterized, and current laparoscopic correction methods do not account for the mechanics of how intraoperative deformation is applied to the liver. In this work, we first measure the degree of laparoscopic deformation at two insufflation pressures over the course of laparoscopic-to-open conversion in 25 patients. With this clinical data alongside a mock laparoscopic phantom setup, we report a novel biomechanical correction approach that leverages anatomically load-bearing support surfaces including ligament attachments to iteratively reconstruct and account for intraoperative deformations. Laparoscopic deformations were significantly larger than deformations associated with open surgery, and our correction approach yielded subsurface target error of 6.7±1.3 mm and surface error of 0.8±0.4 mm using only sparse surface data with realistic surgical extent. Laparoscopic surface data extents were examined and found to impact registration accuracy. Lastly, we demonstrate viability of the correction method with clinical data.
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