Department of Biomedical Engineering, Vanderbilt University, Nashville, TN 37235, USA.
Prog Biophys Mol Biol. 2010 Dec;103(2-3):197-207. doi: 10.1016/j.pbiomolbio.2010.09.014. Epub 2010 Sep 30.
The current protocol for image guidance in open abdominal liver tumor removal surgeries involves a rigid registration between the patient's operating room space and the pre-operative diagnostic image-space. Systematic studies have shown that the liver can deform up to 2 cm during surgeries in a non-rigid fashion thereby compromising the accuracy of these surgical navigation systems. Compensating for intra-operative deformations using mathematical models has shown promising results. In this work, we follow up the initial rigid registration with a computational approach that is geared towards minimizing the residual closest point distances between the un-deformed pre-operative surface and the rigidly registered intra-operative surface. We also use a surface Laplacian equation based filter that generates a realistic deformation field. Preliminary validation of the proposed computational framework was performed using phantom experiments and clinical trials. The proposed framework improved the rigid registration errors for the phantom experiments on average by 43%, and 74% using partial and full surface data, respectively. With respect to clinical data, it improved the closest point residual error associated with rigid registration by 54% on average for the clinical cases. These results are highly encouraging and suggest that computational models can be used to increase the accuracy of image-guided open abdominal liver tumor removal surgeries.
目前，在开放性腹部肝脏肿瘤切除手术中进行图像引导所采用的方法是在患者的手术室空间和术前诊断图像空间之间进行刚性配准。系统研究表明，肝脏在非刚性手术过程中会变形达 2 厘米，从而影响这些手术导航系统的准确性。使用数学模型来补偿术中变形已经显示出了有希望的结果。在这项工作中，我们在初始刚性配准之后采用了一种计算方法，旨在最小化未变形术前表面和刚性配准术中表面之间的残余最近点距离。我们还使用了基于曲面拉普拉斯方程的滤波器来生成真实的变形场。使用体模实验和临床试验对所提出的计算框架进行了初步验证。对于体模实验，所提出的框架平均将刚性配准误差提高了 43%，对于部分和完整表面数据，分别提高了 74%。对于临床数据，对于刚性配准相关的最近点残差误差，它平均提高了 54%。这些结果非常令人鼓舞，表明计算模型可用于提高图像引导的开放性腹部肝脏肿瘤切除手术的准确性。
