Ji Songbai, Wu Ziji, Hartov Alex, Roberts David W, Paulsen Keith D
Thayer School of Engineering, Dartmouth College, Hanover, New Hampshire 03755, USA.
Med Phys. 2008 Oct;35(10):4612-24. doi: 10.1118/1.2977728.
An image-based re-registration scheme has been developed and evaluated that uses fiducial registration as a starting point to maximize the normalized mutual information (nMI) between intraoperative ultrasound (iUS) and preoperative magnetic resonance images (pMR). We show that this scheme significantly (p<0.001) reduces tumor boundary misalignment between iUS pre-durotomy and pMR from an average of 2.5 mm to 1.0 mm in six resection surgeries. The corrected tumor alignment before dural opening provides a more accurate reference for assessing subsequent intraoperative tumor displacement, which is important for brain shift compensation as surgery progresses. In addition, we report the translational and rotational capture ranges necessary for successful convergence of the nMI registration technique (5.9 mm and 5.2 deg, respectively). The proposed scheme is automatic, sufficiently robust, and computationally efficient (<2 min), and holds promise for routine clinical use in the operating room during image-guided neurosurgical procedures.
已开发并评估了一种基于图像的重新配准方案，该方案以基准配准为起点，以最大化术中超声（iUS）与术前磁共振图像（pMR）之间的归一化互信息（nMI）。我们表明，在六例切除手术中，该方案显著（p<0.001）减少了iUS硬膜切开术前与pMR之间的肿瘤边界错位，从平均2.5毫米降至1.0毫米。硬膜切开前校正后的肿瘤对齐为评估随后的术中肿瘤移位提供了更准确的参考，这对于手术过程中的脑移位补偿很重要。此外，我们报告了nMI配准技术成功收敛所需的平移和旋转捕获范围（分别为5.9毫米和5.2度）。所提出的方案是自动的、足够稳健且计算效率高（<2分钟），有望在图像引导神经外科手术过程中在手术室中常规临床使用。
