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Title page for ETD etd-03192015-103733

Type of Document Master's Thesis
Author Conley, Rebekah Helene
Author's Email Address rebekah.h.conley@vanderbilt.edu
URN etd-03192015-103733
Title A Comprehensive Framework for Image Guided Breast Surgery
Degree Master of Science
Department Biomedical Engineering
Advisory Committee
Advisor Name Title
Michael I. Miga Committee Chair
Robert L. Galloway Committee Member
  • nonrigid registration
  • tracked ultrasound
  • biomechanical models
  • image guided surgery
  • lumpectomy
  • breast conservation therapy
Date of Defense 2015-03-18
Availability unrestricted
Unfortunately, the current re-excision rates for breast conserving surgeries due to positive margins average 20-40%. The high re-excision rates arise from difficulty in localizing tumor boundaries intraoperatively and lack of real time information on the presence of residual disease. The work presented here introduces the use of supine magnetic resonance (MR) images, digitization technology, and biomechanical models to investigate the capability of using an image guidance system to localize tumors intraoperatively. Two studies are presented in which preoperative supine MR images were registered to a mock intraoperative setup. In the mock intraoperative setup, a laser range scanner was used to digitize the breast surface and tracked ultrasound was used to digitize the chest wall and tumor. In the first study, a rigid registration was performed and validated using subsurface error metrics between the registered preoperative tumor and the intraoperative tumor as identified by tracked ultrasound. In the second study, a novel nonrigid correction technique was employed to correct for deformations occurring between the preoperative and intraoperative states. Tumor localizations by tracked ultrasound were again used to evaluate the fidelity of aligning preoperative MR tumor contours to physical patient space. Using our prototype image guided surgery platform, we were able to align intraoperative data with preoperative patient specific models with clinically relevant accuracy.
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