University of Grenoble Alpes, CNRS, Grenoble INP, TIMC-IMAG, F-38000 Grenoble, France; University of Strasbourg, CNRS, AVR-ICube, F-67000 Strasbourg, France.
University of Strasbourg, CNRS, AVR-ICube, F-67000 Strasbourg, France; MIMESIS, INRIA Nancy, F-67000 Strasbourg, France.
Med Image Anal. 2017 Aug;40:133-153. doi: 10.1016/j.media.2017.06.003. Epub 2017 Jun 15.
During brain tumor surgery, planning and guidance are based on preoperative images which do not account for brain-shift. However, this deformation is a major source of error in image-guided neurosurgery and affects the accuracy of the procedure. In this paper, we present a constraint-based biomechanical simulation method to compensate for craniotomy-induced brain-shift that integrates the deformations of the blood vessels and cortical surface, using a single intraoperative ultrasound acquisition.
Prior to surgery, a patient-specific biomechanical model is built from preoperative images, accounting for the vascular tree in the tumor region and brain soft tissues. Intraoperatively, a navigated ultrasound acquisition is performed directly in contact with the organ. Doppler and B-mode images are recorded simultaneously, enabling the extraction of the blood vessels and probe footprint, respectively. A constraint-based simulation is then executed to register the pre- and intraoperative vascular trees as well as the cortical surface with the probe footprint. Finally, preoperative images are updated to provide the surgeon with images corresponding to the current brain shape for navigation.
The robustness of our method is first assessed using sparse and noisy synthetic data. In addition, quantitative results for five clinical cases are provided, first using landmarks set on blood vessels, then based on anatomical structures delineated in medical images. The average distances between paired vessels landmarks ranged from 3.51 to 7.32 (in mm) before compensation. With our method, on average 67% of the brain-shift is corrected (range [1.26; 2.33]) against 57% using one of the closest existing works (range [1.71; 2.84]). Finally, our method is proven to be fully compatible with a surgical workflow in terms of execution times and user interactions.
In this paper, a new constraint-based biomechanical simulation method is proposed to compensate for craniotomy-induced brain-shift. While being efficient to correct this deformation, the method is fully integrable in a clinical process.
在脑肿瘤手术中，规划和指导是基于术前图像进行的，而这些图像并未考虑脑移位。然而，这种变形是影像引导神经外科手术中误差的主要来源，并影响手术的准确性。在本文中，我们提出了一种基于约束的生物力学模拟方法，用于补偿开颅引起的脑移位，该方法结合了血管和皮质表面的变形，仅使用单次术中超声采集。
在手术前，从术前图像构建患者特定的生物力学模型，考虑肿瘤区域的血管树和脑软组织。术中，直接在器官上进行导航超声采集。同时记录多普勒和 B 模式图像，分别提取血管和探头足迹。然后执行基于约束的模拟，以将术前和术中的血管树以及皮质表面与探头足迹进行配准。最后，更新术前图像，为外科医生提供与当前大脑形状对应的导航图像。
首先使用稀疏和嘈杂的合成数据评估我们方法的稳健性。此外，还提供了五个临床病例的定量结果，首先使用血管上的标志点，然后使用医学图像中描绘的解剖结构。在补偿之前，配对血管标志点之间的平均距离在 3.51 到 7.32（mm）之间。使用我们的方法，平均有 67%（范围[1.26; 2.33]）的脑移位得到纠正，而使用现有最接近方法的平均纠正率为 57%（范围[1.71; 2.84]）。最后，就执行时间和用户交互而言，我们的方法被证明与手术工作流程完全兼容。
在本文中，提出了一种新的基于约束的生物力学模拟方法，用于补偿开颅引起的脑移位。该方法在有效纠正这种变形的同时，完全可以集成到临床过程中。
