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使用激光测距扫描仪和可变形配准方法进行皮质移位追踪

Cortical Shift Tracking Using a Laser Range Scanner and Deformable Registration Methods.

作者信息

Sinha Tuhin K, Duay Valerie, Dawant Benoit M, Miga Michael I

出版信息

Med Image Comput Comput Assist Interv. 2003 Nov;2879:166-174. doi: 10.1007/978-3-540-39903-2_21.

Abstract

A novel brain shift tracking protocol is introduced in this paper which utilizes laser range scan (LRS) data and 2D deformable image registration. This protocol builds on previous efforts to incorporate intra-operative LRS data into a model-updated image guided surgery paradigm for brain shift compensation. The shift tracking method employs the use of a LRS system capable of capturing textures of the intra-operative scene during range data acquisition. Textures from serial range images are then registered using a 2D deformable registration approach that uses local support radial basis functions and mutual information. Given the deformation field provided by the registration, 3D points in serial LRS datasets can then be tracked. Results from this paper indicate that the error associated with tracking brain movement is 1.1 on average given brain shifts of approximately 20.5. Equally important, a strategy is presented to rapidly acquire intra-operative measurements of shift which are compatible with model-based strategies for brain deformation compensation.

摘要

本文介绍了一种新型的脑移位跟踪协议,该协议利用激光测距扫描(LRS)数据和二维可变形图像配准。该协议建立在先前将术中LRS数据纳入模型更新的图像引导手术范式以进行脑移位补偿的努力之上。移位跟踪方法采用了一种LRS系统,该系统能够在距离数据采集期间捕获术中场景的纹理。然后,使用二维可变形配准方法对序列距离图像的纹理进行配准,该方法使用局部支持径向基函数和互信息。根据配准提供的变形场,可以跟踪序列LRS数据集中的三维点。本文的结果表明,在脑移位约为20.5的情况下,与跟踪脑运动相关的误差平均为1.1。同样重要的是,本文提出了一种策略,用于快速获取与基于模型的脑变形补偿策略兼容的术中移位测量值。

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本文引用的文献

1
Image-guided therapy and intraoperative MRI in neurosurgery.
Minim Invasive Ther Allied Technol. 2000;9(3-4):277-86. doi: 10.1080/13645700009169658.
2
The adaptive bases algorithm for intensity-based nonrigid image registration.
IEEE Trans Med Imaging. 2003 Nov;22(11):1470-9. doi: 10.1109/TMI.2003.819299.
3
Cortical surface registration for image-guided neurosurgery using laser-range scanning.
IEEE Trans Med Imaging. 2003 Aug;22(8):973-85. doi: 10.1109/TMI.2003.815868.
4
Technical advances toward interactive image-guided laparoscopic surgery.
Surg Endosc. 2000 Jul;14(7):675-9. doi: 10.1007/s004640000023.
6
Intraoperatively updated neuroimaging using brain modeling and sparse data.
Neurosurgery. 1999 Nov;45(5):1199-206; discussion 1206-7.
7
Multi-modal volume registration by maximization of mutual information.
Med Image Anal. 1996 Mar;1(1):35-51. doi: 10.1016/s1361-8415(01)80004-9.
8
Error assessment during "image guided" and "imaging interactive" stereotactic surgery.
Comput Med Imaging Graph. 1994 Jul-Aug;18(4):279-87. doi: 10.1016/0895-6111(94)90052-3.
9
Stereotactic exploration of the brain in the era of computed tomography.
Surg Neurol. 1984 Sep;22(3):222-30. doi: 10.1016/0090-3019(84)90003-x.

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