← 研踪 PaperTrailarXiv:2610.37271 · v1
An Interactive Mobile Augmented Reality System for Efficient Virtual Model-to-Patient Registration in Thoracic Surgery
首次提交 2026-10-27 22:02
版本更新 2026-10-27 22:02
雷达观测 2026-10-29 00:34
摘要
Video-Assisted Thoracoscopic Surgery (VATS) is a minimally invasive procedure, where precise surgical port placement is vital, yet it relies heavily on surgeon experience and mental mapping of pre-operative imaging in the operating room. To address this challenge, we present an accessible, interactive mobile augmented reality (AR) framework, HILMARTS, to enable accurate and efficient pre-operative virtual model-to-patient registration for AR-guided surgical navigation. Our system runs on a commercially available LiDAR-equipped iPad and supports a user-in-the-loop workflow through a two-stage coarse-to-fine registration process. First, the user performs a manual alignment using the AR view on the iPad. This initial maneuver is then refined through automatic point-cloud registration leveraging the LiDAR sensor. We further introduce a novel occupancy-grid-based Surface Coverage Score to provide real-time, actionable feedback to the operator on the geometric completeness of LiDAR data capture for reliable point-cloud-based alignment. In a phantom study with 16 participants, the system achieved a 3.31+/-1.26mm registration accuracy in ~4 min with a mean System Usability Scale (SUS) of 84.1. These findings highlight the system's accuracy, efficiency, robustness, and potential clinical usability, and could support its applicability to other AR-based surgical navigation tasks.
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v1 · 2026-10-27 22:02 · 最新观测版本
An Interactive Mobile Augmented Reality System for Efficient Virtual Model-to-Patient Registration in Thoracic Surgery
Video-Assisted Thoracoscopic Surgery (VATS) is a minimally invasive procedure, where precise surgical port placement is vital, yet it relies heavily on surgeon experience and mental mapping of pre-operative imaging in the operating room. To address this challenge, we present an accessible, interactive mobile augmented reality (AR) framework, HILMARTS, to enable accurate and efficient pre-operative virtual model-to-patient registration for AR-guided surgical navigation. Our system runs on a commercially available LiDAR-equipped iPad and supports a user-in-the-loop workflow through a two-stage coarse-to-fine registration process. First, the user performs a manual alignment using the AR view on the iPad. This initial maneuver is then refined through automatic point-cloud registration leveraging the LiDAR sensor. We further introduce a novel occupancy-grid-based Surface Coverage Score to provide real-time, actionable feedback to the operator on the geometric completeness of LiDAR data capture for reliable point-cloud-based alignment. In a phantom study with 16 participants, the system achieved a 3.31+/-1.26mm registration accuracy in ~4 min with a mean System Usability Scale (SUS) of 84.1. These findings highlight the system's accuracy, efficiency, robustness, and potential clinical usability, and could support its applicability to other AR-based surgical navigation tasks.
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