Department of Head and Neck Oncology and Surgery, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
MIRA Institute of Biomedical Engineering and Technical Medicine, University of Twente, Drienerlolaan 5, 7522 NB, Enschede, The Netherlands.
Int J Comput Assist Radiol Surg. 2018 Jan;13(1):47-59. doi: 10.1007/s11548-017-1659-5. Epub 2017 Aug 31.
Functional inoperability in advanced oral cancer is difficult to assess preoperatively. To assess functions of lips and tongue, biomechanical models are required. Apart from adjusting generic models to individual anatomy, muscle activation patterns (MAPs) driving patient-specific functional movements are necessary to predict remaining functional outcome. We aim to evaluate how volunteer-specific MAPs derived from surface electromyographic (sEMG) signals control a biomechanical face model.
Muscle activity of seven facial muscles in six volunteers was measured bilaterally with sEMG. A triple camera set-up recorded 3D lip movement. The generic face model in ArtiSynth was adapted to our needs. We controlled the model using the volunteer-specific MAPs. Three activation strategies were tested: activating all muscles [Formula: see text], selecting the three muscles showing highest muscle activity bilaterally [Formula: see text]-this was calculated by taking the mean of left and right muscles and then selecting the three with highest variance-and activating the muscles considered most relevant per instruction [Formula: see text], bilaterally. The model's lip movement was compared to the actual lip movement performed by the volunteers, using 3D correlation coefficients [Formula: see text].
The correlation coefficient between simulations and measurements with [Formula: see text] resulted in a median [Formula: see text] of 0.77. [Formula: see text] had a median [Formula: see text] of 0.78, whereas with [Formula: see text] the median [Formula: see text] decreased to 0.45.
We demonstrated that MAPs derived from noninvasive sEMG measurements can control movement of the lips in a generic finite element face model with a median [Formula: see text] of 0.78. Ultimately, this is important to show the patient-specific residual movement using the patient's own MAPs. When the required treatment tools and personalisation techniques for geometry and anatomy become available, this may enable surgeons to test the functional results of wedge excisions for lip cancer in a virtual environment and to weigh surgery versus organ-sparing radiotherapy or photodynamic therapy.
在晚期口腔癌中，功能无法手术是难以在术前评估的。为了评估嘴唇和舌头的功能，需要生物力学模型。除了调整通用模型以适应个体解剖结构外，还需要驱动患者特定功能运动的肌肉激活模式 (MAPs) 来预测剩余的功能结果。我们旨在评估源自表面肌电图 (sEMG) 信号的志愿者特定 MAPs 如何控制生物力学面部模型。
使用 sEMG 双侧测量 6 名志愿者的 7 块面肌的肌肉活动。一个三相机设置记录了 3D 嘴唇运动。在 ArtiSynth 中的通用面部模型根据我们的需要进行了调整。我们使用志愿者特定的 MAPs 来控制模型。测试了三种激活策略：激活所有肌肉 [公式：见文本]、选择双侧肌肉活动最高的 3 块肌肉 [公式：见文本]-通过取左右肌肉的平均值，然后选择方差最高的 3 个肌肉-以及根据指令激活双侧最相关的肌肉 [公式：见文本]。使用 3D 相关系数 [公式：见文本]比较模型的嘴唇运动与志愿者实际进行的嘴唇运动。
使用 [公式：见文本] 进行模拟和测量，相关系数的中位数为 [Formula: see text] 0.77。[Formula: see text] 的中位数为 [Formula: see text] 0.78，而使用 [公式：见文本] 时，中位数 [Formula: see text] 降低至 0.45。
我们证明了源自非侵入性 sEMG 测量的 MAPs 可以控制通用有限元面部模型中嘴唇的运动，中位数 [Formula: see text] 为 0.78。最终，这对于使用患者自身的 MAPs 显示患者特定的残留运动很重要。当获得用于几何形状和解剖结构的治疗工具和个性化技术时，这可能使外科医生能够在虚拟环境中测试唇癌楔形切除术的功能结果，并权衡手术与器官保留放疗或光动力疗法。
