Xiong Huahui, Tong Hui, Tian Yuhang, Ji Changjin, Huang Xiaoqing, Huang Yaqi
School of Biomedical Engineering, Capital Medical University, Beijing, China.
Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China.
Heliyon. 2025 Feb 10;11(4):e42598. doi: 10.1016/j.heliyon.2025.e42598. eCollection 2025 Feb 28.
Three-dimensional (3D) reconstruction of head and neck tissues has extensive clinical applications, but due to the complexity and variability of tissue structure, there is still a lack of a complete scheme to reconstruct the head and neck tissues. This study aims to establish a tissue-specified multi-directional cross-sectional image sequence construction method to capture diverse tissue contour information.
The image sequences that are most conducive to acquiring the boundary contours of the target tissue are constructed from 3D MRI images of the head and neck in a non-traditional way based on the characteristics of each target tissue, and an effective registration strategy is used to integrate the boundaries of the target tissue segmented from multiple image sequences. The NURBS (Non-Uniform Rational B-Splines) surface modeling method is used to construct the 3D structure of the head and neck based on the segmented tissue boundaries, and then the constructed structure is used to build a fluid-structure interaction model to simulate airway collapse.
The multi-directional cross-sectional image sequences of head and neck tissues were reconstructed, which successfully supplemented the missing boundary information in unidirectional image sequences commonly used in anatomical reconstructions. The boundaries of the tongue and soft palate were obtained from three corresponding sequential images respectively, and nonlinear registration methods were developed to match the intersections of the target tissue boundaries segmented from different image sequences. The complete 3D head and neck structure, including the surrounding tissues of the upper airway, was accurately reconstructed, and then directly converted into a finite element model through a meshing procedure. The head and neck numerical models successfully simulate airway collapse in both the obstructive sleep apnea patient and the normal subject, providing detailed information on soft tissue deformation and predicting the values of the airway critical closing pressure.
A complete 3D reconstruction scheme from multi-directional image sequence construction to nonlinear boundary registration and NURBS surface generation is established. The constructed model can accurately reflect the characteristics of real anatomical structure, and can be directly used for complex numerical simulations of upper airway collapse.
头颈部组织的三维（3D）重建具有广泛的临床应用，但由于组织结构的复杂性和变异性，目前仍缺乏完整的头颈部组织重建方案。本研究旨在建立一种组织特异性的多方向横截面图像序列构建方法，以获取多样化的组织轮廓信息。
基于各目标组织的特征，以非传统方式从头部和颈部的3D MRI图像构建最有利于获取目标组织边界轮廓的图像序列，并采用有效的配准策略整合从多个图像序列中分割出的目标组织边界。利用非均匀有理B样条（NURBS）曲面建模方法，基于分割出的组织边界构建头颈部的3D结构，然后利用构建的结构建立流固耦合模型以模拟气道塌陷。
重建了头颈部组织的多方向横截面图像序列，成功补充了解剖重建中常用的单向图像序列中缺失的边界信息。分别从三个相应的连续图像中获取了舌头和软腭的边界，并开发了非线性配准方法来匹配从不同图像序列中分割出的目标组织边界的交点。精确重建了包括上气道周围组织在内的完整3D头颈部结构，然后通过网格划分程序直接将其转换为有限元模型。头颈部数值模型成功模拟了阻塞性睡眠呼吸暂停患者和正常受试者的气道塌陷，提供了软组织变形的详细信息，并预测了气道临界关闭压力值。
建立了从多方向图像序列构建到非线性边界配准以及NURBS曲面生成的完整3D重建方案。构建的模型能够准确反映真实解剖结构的特征，并可直接用于上气道塌陷的复杂数值模拟。
