Predicting soft tissue deformations for a maxillofacial surgery planning system: From computational strategies to a complete clinical validation
Predicting soft tissue deformations for a maxillofacial surgery planning system: From computational strategies to a complete clinical validation
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DOI：
10.1016/j.media.2007.02.003
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发表时间：
2007-06-01
影响因子：
10.9
通讯作者：
Suetens, P.
中科院分区：
文献类型：
--
作者：
Mollemans, W.;Schutyser, F.;Suetens, P.
在颌面外科领域，外科医生对能够术前预测手术后新的面部外观有着巨大的需求。除了外科医生在计划过程中的极大兴趣外，它也是改善外科医生和患者之间沟通的重要工具。在这项工作中，我们比较了四种不同计算策略的使用来预测这种新的面部外观。这四种策略是：线性有限元模型、非线性有限元模型、质量弹簧模型和新型质量张量模型。为了真正验证这四个模型，我们获得了10名接受颌面外科手术的患者的数据集，包括手术前和手术后的CT数据。对于所有的患者数据，我们在定量验证中将使用四个计算模型之一获得的预测面部外观与手术后图像数据进行比较。在这一定量验证期间，预测的面部皮肤表面和手术后实际皮肤表面的相应点之间的距离测量被量化并在3D中可视化。结果表明，MTM和线性有限元预测的精度最高。对于这些模型，平均中值距离仅为0.60 mm，即使是平均90%的百分位数也保持在1.5 mm以下。此外，MTM是最快的模型，平均模拟时间仅为10 S。除了这种定量验证外，8名颌面外科医生还进行了定性验证研究，他们通过预先定义的语句对可视化预测的面部外观进行评分。这项研究证实了定量研究的积极结果，因此我们可以得出结论，快速准确地预测手术后面部结果是可能的。因此，颌面部软组织预测系统的使用是相关的，适合于日常临床实践。(C)2007 Elsevier B.V.保留所有权利。
In the field of maxillofacial surgery, there is a huge demand from surgeons to be able to pre-operatively predict the new facial outlook after surgery. Besides the big interest for the surgeon during the planning, it is also an essential tool to improve the communication between the surgeon and his patient. In this work, we compare the usage of four different computational strategies to predict this new facial outlook. These four strategies are: a linear Finite Element Model (FEM), a non-linear Finite Element Model (NFEM), a Mass Spring Model (MSM) and a novel Mass Tensor Model (MTM). For true validation of these four models we acquired a data set of 10 patients who underwent maxillofacial surgery, including pre-operative and post-operative CT data. For all patient data we compared in a quantitative validation the predicted facial outlook, obtained with one of the four computational models, with post-operative image data. During this quantitative validation distance measurements between corresponding points of the predicted and the actual post-operative facial skin surface, are quantified and visualised in 3D. Our results show that the MTM and linear FEM predictions achieve the highest accuracy. For these models the average median distance measures only 0.60 mm and even the average 90% percentile stays below 1.5 mm. Furthermore, the MTM turned out to be the fastest model, with an average simulation time of only 10 s. Besides this quantitative validation, a qualitative validation study was carried out by eight maxillofacial surgeons, who scored the visualised predicted facial appearance by means of pre-defined statements. This study confirmed the positive results of the quantitative study, so we can conclude that fast and accurate predictions of the post-operative facial outcome are possible. Therefore, the usage of a maxillofacial soft tissue prediction system is relevant and suitable for daily clinical practice. (c) 2007 Elsevier B.V. All rights reserved.