首页> 外文会议>Conference on Medical Imaging: Visualization, Image-Guided Procedures, and Modeling >Mutual-Information-Corrected Tumor Displacement Using Intraoperative Ultrasound for Brain Shift Compensation in Image- Guided Neurosurgery
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Mutual-Information-Corrected Tumor Displacement Using Intraoperative Ultrasound for Brain Shift Compensation in Image- Guided Neurosurgery

机译:使用术中超声检查图像引导神经外科术中跨越超声的互信息矫正肿瘤位移

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Intraoperative ultrasound (iUS) has emerged as a practical neuronavigational tool for brain shift compensation in image-guided tumor resection surgeries. The use of iUS is optimized when coregistered with preoperative magnetic resonance images (pMR) of the patient's head. However, the fiducial-based registration alone does not necessarily optimize the alignment of internal anatomical structures deep in the brain (e.g., tumor) between iUS and pMR. In this paper, we investigated and evaluated an image-based re-registration scheme to maximize the normalized mutual information (nMI) between iUS and pMR to improve tumor boundary alignment using the fiducial registration as a starting point for optimization. We show that this scheme significantly (p < 0.001) reduces tumor boundary misalignment pre-durotomy. The same technique was employed to measure tumor displacement post-durotomy, and the locally measured tumor displacement was assimilated into a biomechanical model to estimate whole-brain deformation. Our results demonstrate that the nMI re-registration pre-durotomy is critical for obtaining accurate measurement of tumor displacement, which significantly improved model response at the craniotomy when compared with stereopsis data acquired independently from the tumor registration. This automatic and computationally efficient (<2min) re-registration technique is feasible for routine clinical use in the operating room (OR).
机译:术中超声(IUS)已经成为用于在图像引导的肿瘤切除手术脑移位补偿的实际neuronavigational工具。当与患者头部的术前的磁共振图像(PMR)配准使用IUS的最优化。然而,单独的基于基准登记不一定优化深在IUS和PMR之间脑(例如,肿瘤)的内部解剖结构的取向。在本文中,我们调查和评估基于图像的重新登记方案最大化IUS和PMR之间的归一化互信息(NMI),使用所述基准注册作为用于优化的起始点,以改善肿瘤边界对齐。我们表明,这种方案显著(P <0.001)减少肿瘤边界未对准预硬脊膜切开。物所用的相同的技术来测量肿瘤位移后硬脊膜切开,并且本地测量肿瘤位移吸收到生物力学模型来估计全脑变形。我们的研究结果表明,NMI重新注册预硬脊膜切开是用于获得肿瘤位移,当与从肿瘤登记独立采集的立体视觉数据进行了比较,其显著改善在开颅模型响应的精确测量是至关重要的。这种自动和计算有效的(<2分钟)重新登记的技术是在手术室(OR)常规临床使用是可行的。

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