Si Weixin, Liao Xiangyun, Wang Qiong, Heng Pheng Ann
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong.
Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 1068 Xueyuan Avenue, 503644, Shenzhen, China.
Biomed Eng Online. 2017 Feb 20;16(1):30. doi: 10.1186/s12938-017-0321-3.
Biomechanical deformable volumetric registration can help improve safety of surgical interventions by ensuring the operations are extremely precise. However, this technique has been limited by the accuracy and the computational efficiency of patient-specific modeling.
This study presents a tissue-tissue coupling strategy based on penalty method to model the heterogeneous behavior of deformable body, and estimate the personalized tissue-tissue coupling parameters in a data-driven way. Moreover, considering that the computational efficiency of biomechanical model is highly dependent on the mechanical resolution, a practical coarse-to-fine scheme is proposed to increase runtime efficiency. Particularly, a detail enrichment database is established in an offline fashion to represent the mapping relationship between the deformation results of high-resolution hexahedral mesh extracted from the raw medical data and a newly constructed low-resolution hexahedral mesh. At runtime, the mechanical behavior of human organ under interactions is simulated with this low-resolution hexahedral mesh, then the microstructures are synthesized in virtue of the detail enrichment database.
The proposed method is validated by volumetric registration in an abdominal phantom compression experiments. Our personalized heterogeneous deformable model can well describe the coupling effects between different tissues of the phantom. Compared with high-resolution heterogeneous deformable model, the low-resolution deformable model with our detail enrichment database can achieve 9.4× faster, and the average target registration error is 3.42 mm, which demonstrates that the proposed method shows better volumetric registration performance than state-of-the-art.
Our framework can well balance the precision and efficiency, and has great potential to be adopted in the practical augmented reality image-guided robotic systems.
生物力学可变形体积配准通过确保手术极其精确，有助于提高手术干预的安全性。然而，该技术一直受到患者特异性建模的准确性和计算效率的限制。
本研究提出一种基于惩罚方法的组织-组织耦合策略，以对可变形体的异质性行为进行建模，并以数据驱动的方式估计个性化的组织-组织耦合参数。此外，考虑到生物力学模型的计算效率高度依赖于力学分辨率，提出了一种实用的粗到精方案以提高运行时效率。具体而言，以离线方式建立一个细节丰富数据库，以表示从原始医学数据中提取的高分辨率六面体网格的变形结果与新构建的低分辨率六面体网格之间的映射关系。在运行时，用这种低分辨率六面体网格模拟人体器官在相互作用下的力学行为，然后借助细节丰富数据库合成微观结构。
所提出的方法在腹部体模压缩实验中的体积配准中得到验证。我们的个性化异质可变形模型能够很好地描述体模中不同组织之间的耦合效应。与高分辨率异质可变形模型相比，带有我们细节丰富数据库的低分辨率可变形模型速度可提高9.4倍，平均目标配准误差为3.42毫米，这表明所提出的方法比现有技术具有更好的体积配准性能。
我们的框架能够很好地平衡精度和效率，在实际的增强现实图像引导机器人系统中具有很大的应用潜力。
