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Thesis_XingxingLiu_2025Spring_050220253.96 MB
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Dissertation
Multimodal surgical navigation assisted by optical topographical imaging, optical tracking, and ultrasound imaging
University of Iowa
Doctor of Philosophy (PhD), University of Iowa
Spring 2025
DOI: 10.25820/etd.007906
Abstract
Surgical navigation is an interdisciplinary technology that has gained increasing prominence in modern surgical procedures. It integrates a diverse array of technologies, including preoperative and intraoperative medical imaging, image registration, surgical instrument tracking, and virtual/augmented reality (VAR), into a cohesive system. Surgical navigation systems have the potential to enhance surgical outcomes by providing surgeons with critical anatomical information and procedural guidance.
This dissertation focuses on the development of a multimodal surgical navigation system designed to enhance intraoperative decision-making. The system aims to provide surgeons with comprehensive anatomical structure information, precise tracking of surgical instrument pose and location, and real-time procedural guidance.
This research is structured around three primary aims:
Aim 1: (a) Develop a hardware prototype and corresponding software for the surgical navigation system, enabling the capture of 3D surface topography, registration and overlay of 3D CT images onto the topography, real-time tracking of surgical instruments, and visualization on a host computer. (b) Validate the system’s functionality in brain tumor resection and joint osteotomy procedures, demonstrating its versatility and potential clinical impact.
Aim 2: (a) Integrate ultrasound imaging into the surgical navigation system through 3D tracking and registration. (b) Reconstruct 3D volumetric data from navigated 2D ultrasound images and register them with preoperative CT scans to enhance anatomical accuracy. (c) Conduct ultrasound-guided spinal surgery to evaluate the system’s reliability in spinal procedures.
Aim 3: (a) Develop a deep learning-based CT/MRI segmentation algorithm capable of handling cross-domain segmentation tasks. (b) Perform comparative experiments to assess the proposed deep learning model against established segmentation approaches.
Overall, this dissertation presents a comprehensive integration of hardware and software technologies to advance surgical navigation and medical image analysis. By developing a novel surgical navigation system and a deep learning-based segmentation network, this work provides valuable insights into the evolution of surgical technology, contributing to the enhancement of precision and efficiency in modern surgical procedures.
Details
- Title: Subtitle
- Multimodal surgical navigation assisted by optical topographical imaging, optical tracking, and ultrasound imaging
- Creators
- Xingxing Liu
- Contributors
- Yang Liu (Advisor)Kishlay Jha (Committee Member)Ananya Sen Gupta (Committee Member)Yuliang Xie (Committee Member)Maziyar Askari (Committee Member)
- Resource Type
- Dissertation
- Degree Awarded
- Doctor of Philosophy (PhD), University of Iowa
- Degree in
- Electrical and Computer Engineering
- Date degree season
- Spring 2025
- DOI
- 10.25820/etd.007906
- Publisher
- University of Iowa
- Number of pages
- xiii, 88 pages
- Copyright
- Copyright 2025 Xingxing Liu
- Language
- English
- Date submitted
- 04/28/2025
- Description illustrations
- Illustrations, tables, graphs, charts
- Description bibliographic
- Includes bibliographical references (pages 82-88).
- Public Abstract (ETD)
Surgical navigation is an advanced technology that has gained increasing popularity in modern surgical procedures. It integrates various cutting-edge technologies, including medical imaging, optical tracking, deep learning, and virtual/augmented reality (VAR), into a unified system. These systems have the potential to improve surgical outcomes by providing surgeons with critical anatomical information and real-time guidance.
My Ph.D. research focuses on developing a multimodal surgical navigation system designed to enhance intraoperative precision. This system aims to provide surgeons with detailed anatomical information, accurate tracking of surgical instrument positions, and real-time procedural guidance during surgeries.
This dissertation can be outlined by three aims: (1) development of a multimodal surgical navigation system for brain tumor resection and joint osteotomy procedures; (2) integration of ultrasound imaging into the navigation system and development of a 3D reconstruction algorithm to generate volumetric data from ultrasound images, with a demonstration of its application in spinal procedures; (3) creation of a deep learning-based spine segmentation algorithm for CT/MRI imaging, which enhances presurgical medical image processing.
Overall, this dissertation integrates both hardware and software technologies to provide surgeons with more accurate guidance during surgical procedures. By developing a surgical navigation system and a deep learning-based model for medical image analysis, this research contributes valuable insights to the advancement of surgical technology.
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984830727302771
Metrics
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