Haouchine Nazim, Juvekar Parikshit, Wells William M, Cotin Stephane, Golby Alexandra, Frisken Sarah
Harvard Medical School, Brigham and Women's Hospital, Boston, MA, USA.
Massachusetts Institute of Technology, Cambdridge, MA, USA.
Med Image Comput Comput Assist Interv. 2020 Oct;12264:735-744. doi: 10.1007/978-3-030-59719-1_71. Epub 2020 Sep 29.
Intra-operative brain shift is a well-known phenomenon that describes non-rigid deformation of brain tissues due to gravity and loss of cerebrospinal fluid among other phenomena. This has a negative influence on surgical outcome that is often based on pre-operative planning where the brain shift is not considered. We present a novel brain-shift aware Augmented Reality method to align pre-operative 3D data onto the deformed brain surface viewed through a surgical microscope. We formulate our non-rigid registration as a Shape-from-Template problem. A pre-operative 3D wire-like deformable model is registered onto a single 2D image of the cortical vessels, which is automatically segmented. This 3D/2D registration drives the underlying brain structures, such as tumors, and compensates for the brain shift in sub-cortical regions. We evaluated our approach on simulated and real data composed of 6 patients. It achieved good quantitative and qualitative results making it suitable for neurosurgical guidance.
术中脑移位是一种众所周知的现象,它描述了由于重力、脑脊液流失等现象导致的脑组织非刚性变形。这对手术结果有负面影响,而手术结果通常基于术前规划,其中未考虑脑移位。我们提出了一种新颖的脑移位感知增强现实方法,将术前3D数据与通过手术显微镜观察到的变形脑表面对齐。我们将非刚性配准公式化为从模板生成形状的问题。将术前3D线状可变形模型配准到自动分割的皮质血管的单个2D图像上。这种3D/2D配准驱动潜在的脑结构,如肿瘤,并补偿皮质下区域的脑移位。我们在由6名患者组成的模拟和真实数据上评估了我们的方法。它取得了良好的定量和定性结果,使其适用于神经外科手术导航。