J Shanghai Jiaotong Univ Sci ›› 2025, Vol. 30 ›› Issue (4): 733-743.doi: 10.1007/s12204-024-2576-8

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脑肿瘤切除术中的肿瘤移位预测和增强现实可视化

  

  1. 1.上海理工大学 健康科学与工程学院，上海 200093；2.上海交通大学医学院附属瑞金医院 神经外科，上海 200025；3.上海交通大学 土木工程系，海洋工程国家重点实验室，上海 200030
  • 收稿日期:2023-09-07 接受日期:2023-12-14 出版日期:2025-07-31 发布日期:2025-07-31

Tumor Displacement Prediction and Augmented Reality Visualization in Brain Tumor Resection Surgery

王佳瑜 1，王殊轶 1，卫永旭 2，廖晨聪 3，尚寒冰 2，王雪 1，康宁 1   

  1. 1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China; 2. Department of Neurosurgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China; 3. State Key Laboratory of Ocean Engineering, Department of Civil Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
  • Received:2023-09-07 Accepted:2023-12-14 Online:2025-07-31 Published:2025-07-31

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摘要： 本研究的目的是建立多元非线性回归数学模型预测脑肿瘤切除手术中肿瘤的移位，并与增强现实技术结合实现三维可视化，提高肿瘤全切率和手术成功率。根据患者术前 MRI 影像数据重建 3D 虚拟模型并 3D 打印，以凝胶注塑的方式制作大脑仿生模型。以对术中病变移位影响较大的术中脑脊液流失量和肿瘤囊液流失量 2 个因素为自变量，确定在肿瘤手术体位后的在垂直骨窗方向上的最高点的移位量为因变量。在仿生模型上进行正交实验建立预测模型，并将该预测模型写入增强现实导航系统中。为验证该预测模型，5 名受试者佩戴HoloLens2，将患者 3D 虚拟模型叠加在真实的头部模型上；分别测试真实模型上肿瘤最高点移位后的空间坐标（实测坐标）及虚拟模型上肿瘤最高点移位后的空间坐标（预测坐标），两者的差值即为模型的预测误差。结果显示：肿瘤最高点在 X 轴和 Y 轴上的移位量实测值与预测值误差波动范围分别为−0.6787~0.2957 mm 和−0.4314~0.2253 mm；每组实验的相对误差都在 10%以内，该模型具有较好的拟合程度。这种建立回归模型的方法是为预测特定情况下脑肿瘤移位进行的初步尝试，也提供了一种新的思路。通过结合增强现实可视化，以简单经济的方式满足外科医生对预测肿瘤移位和对大脑解剖结构精确定位的需求。

关键词: 脑肿瘤, 术中移位, 仿生模型, 多元非线性回归模型, 增强现实, 预测误差

Abstract: The purpose of this study is to establish a multivariate nonlinear regression mathematical model to predict the displacement of tumor during brain tumor resection surgery. And the study will be integrated with augmented reality technology to achieve three-dimensional visualization, thereby enhancing the complete resection rate of tumor and the success rate of surgery. Based on the preoperative MRI data of the patients, a 3D virtual model is reconstructed and 3D printed. A brain biomimetic model is created using gel injection molding. By considering cerebrospinal fluid loss and tumor cyst fluid loss as independent variables, the highest point displacement in the vertical bone window direction is determined as the dependent variable after positioning the patient for surgery. An orthogonal experiment is conducted on the biomimetic model to establish a predictive model, and this model is incorporated into the augmented reality navigation system. To validate the predictive model, five participants wore HoloLens2 devices, overlaying the patient’s 3D virtual model onto the physical head model. Subsequently, the spatial coordinates of the tumor’s highest point after displacement were measured on both the physical and virtual models (actual coordinates and predicted coordinates, respectively). The difference between these coordinates represents the model’s prediction error. The results indicate that the measured and predicted errors for the displacement of the tumor’s highest point on the X and Y axes range from .0.678 7mm to 0.295 7mm and .0.431 4mm to 0.225 3mm, respectively. The relative errors for each experimental group are within 10%, demonstrating a good fit of the model. This method of establishing a regression model represents a preliminary attempt to predict brain tumor displacement in specific situations. It also provides a new approach for surgeons. By combining augmented reality visualization, it addresses the need for predicting tumor displacement and precisely locating brain anatomical structures in a simple and cost-effective manner.

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