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模型更新的图像引导:一种解决重力诱导脑移位的统计方法。

Model-Updated Image Guidance: A Statistical Approach to Gravity-Induced Brain Shift.

作者信息

Dumpuri Prashanth, Miga Michael I

机构信息

Vanderbilt University, Department of Biomedical Engineering, Nashville, TN 37235.

出版信息

Med Image Comput Comput Assist Interv. 2003 Nov;2878:375-382. doi: 10.1007/978-3-540-39899-8_47.

Abstract

Compensating for intraoperative brain shift using computational models has been used with promising results. Since computational time is an important factor during neurosurgery, a prior knowledge of a patient's orientation and changes in tissue buoyancy force would be valuable information to aid in predicting shift due to gravitational forces. Since the latter is difficult to quantify intraoperatively, a statistical model for predicting intraoperative brain deformations due to gravity is reported. This statistical model builds on a computational model developed earlier. For a given set of patient's orientation and amount of CSF drainage, the intraoperative brain shift is calculated using the computational model. These displacements are then validated against measured displacements to predict the intraoperative brain shift. Though initial results are promising, further study is needed before the statistical model can be used for model-updated image-guided surgery.

摘要

使用计算模型来补偿术中脑移位已取得了有前景的结果。由于计算时间是神经外科手术中的一个重要因素,患者的方位以及组织浮力变化的先验知识将是有助于预测重力导致的移位的有价值信息。由于术中难以对后者进行量化,因此报告了一种用于预测重力导致的术中脑变形的统计模型。该统计模型基于早期开发的计算模型构建。对于给定的患者方位和脑脊液引流量,使用计算模型计算术中脑移位。然后将这些位移与测量的位移进行验证,以预测术中脑移位。尽管初步结果很有前景,但在该统计模型可用于模型更新的图像引导手术之前,还需要进一步研究。

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