Advanced Image-Guidance and Surgical-Navigation Techniques for Real-Time Visualized Surgery.
Authors
Affiliations (5)
Affiliations (5)
- Department of General Surgery, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, 310016, China.
- Neuropsychiatry and ECT Department, The Royal Melbourne Hospital, 300 Grattan Street, Parkville, Victoria, Australia.
- State Key Laboratory of Extreme Photonics and Instrumentation, International Research Center for Advanced Photonics, Centre for Optical and Electromagnetic Research, College of Optical Science and Engineering, Zhejiang University, Hangzhou, China.
- College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China.
- Zhejiang Engineering Research Center of Cognitive Healthcare, Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, Hangzhou, 310016, China.
Abstract
Surgical navigation is a rapidly evolving multidisciplinary system that plays a crucial role in precision medicine. Surgical-navigation systems have substantially enhanced modern surgery by improving the precision of resection, reducing invasiveness, and enhancing patient outcomes. However, clinicians, engineers, and professionals in other fields often view this field from their own perspectives, which usually results in a one-sided viewpoint. This article aims to provide a thorough overview of the recent advancements in surgical-navigation systems and categorizes them on the basis of their unique characteristics and applications. Established techniques (e.g., radiography, intraoperative computed tomography [CT], magnetic resonance imaging [MRI], and ultrasound) and emerging technologies (e.g., photoacoustic imaging and near-infrared [NIR]-II imaging) are systematically analyzed, highlighting their underlying mechanisms, methods of use, and respective advantages and disadvantages. Despite substantial progress, the existing navigation systems face challenges, including limited accuracy, high costs, and extensive training requirements for surgeons. Addressing these limitations is crucial for widespread adoption of these technologies. The review emphasizes the need for developing more intelligent, minimally invasive, precise, personalized, and radiation-free navigation solutions. By integrating advanced imaging modalities, machine learning algorithms, and real-time feedback mechanisms, next-generation surgical-navigation systems can further enhance surgical precision and patient safety. By bridging the knowledge gap between clinical practice and engineering innovation, this review not only provides valuable insights for surgeons seeking optimal navigation strategies, but also offers engineers a deeper understanding of clinical application scenarios.