Integrated Intraoperative Guidance for Neurosurgery: Develop Surgical Planning To

我们的总体目标是显著提高影像引导神经外科的疗效和效率， 通过建立一个系统来改善术中可视化、导航和监测，的 系统将创建患者大脑术中配置的增强现实可视化 与高分辨率术前成像数据合并，包括弥散张量和功能磁共振成像， 核磁共振成像（DTI和fMRI），以便更好地定位肿瘤和关键的健康组织。在 在当前实践中，神经外科医生获取新的术中图像，以研究当前的 患者的大脑的配置和监测肿瘤切除的进展。第一个关键限制是 神经外科医生需要做出主观判断，以确定何时需要获得新的 体积采集这涉及对可用导航信息质量的主观估计 根据外科医生评估的现有数据， 自上次核磁共振成像以来发生的。此外，现有国家的第二个关键限制- ART系统无法呈现与患者术中数据融合的关键术前采集数据， 当大脑经历了显著的变形。相反，外科医生必须在精神上融合 术前fMRI和DTI的信息，通过3D空间和时间的心理投影， 病人大脑发生的变化我们建议开发、实施和评估新的 改进术中导航、定位和可视化系统的算法， 神经外科我们将从目前罕见的主观计时全脑的做法， 术中MRI采集到客观定时的全脑MRI， 稀疏成像提供术前fMRI和DT-MRI的近实时更新可视化 随着大脑转移的发生，与患者的大脑融合，预计将大大提高患者的能力。 神经外科医生在最大限度地切除的同时， 肿瘤组织。我们将改进我们的大脑变形模型，以便能够实现良好的对齐， 更长，包含更大的大脑变形。这将有助于克服内部的重大限制， 手术MRI：需要中断手术、移动患者和延长手术时间，以便获得 更新图像。 该项目将通过开发一个系统，使人们能够在 神经外科这将为神经外科医生提供更完整的信息，有助于完成以下任务： 尽可能多地切除肿瘤，同时尽量减少健康组织的损失。

Our overall objective is to significantly improve the efficacy and efficiency of image-guided neurosurgery for brain tumors by building a system to improve intraoperative visualization, navigation and monitoring. The system will create an augmented reality visualization of the intraoperative configuration of the patient's brain merged with high resolution preoperative imaging data, including diffusion tensor- and functional magnetic resonance imaging (DTIand fMRI), in order to better localize the tumor and critical healthy tissues. In current practice, the neurosurgeon acquires new intraoperative images in order to study the current configuration of the patient's brain and to monitor the progress of tumor resection. The first key limitation is that the neurosurgeon is required to make a subjective judgment as to when it is necessary to obtain a new volumetric acquisition. This involves a subjective estimate of the quality of navigation information available from the existing data based on the surgeon's assessment the amount of brain shift and tumor resection that has occurred since the last MRI was acquired. Furthermore, a second key limitation of existing state-of-the- art systems is an inability to present key preoperatively acquired data fused with the patient's intraoperative position when the brain has undergone significant deformation. Rather, the surgeon must mentally fuse the information from preoperative fMRI and DTI by mentally projecting it through the 3D spatial and temporal changes the patient's brain has undergone. We propose to develop, implement and evaluate novel algorithms to improve intraoperative navigation, targeting and visualization system for image-guided neurosurgery. We will shift from the current practice of infrequent subjectively timed whole brain intraoperative MRI acquisition to an objectively timed whole brain MRI supplemented with near real-time sparse imaging. The provision of near real-time updated visualizations of preoperative fMRI and DT-MRI fused with the patient's brain as brain shift occurs is expected to dramatically improve the capacity of the neurosurgeon to preserve eloquent cortex and functionally critical connectivity while maximizing the removal of tumor tissue. We will improve our model of brain deformation so that good alignment can be achieved for longer, encompassing larger brain deformations. This will help to overcome a significant limitation of intra- operative MRI: the need to interrupt surgery, move the patient, and extend operative time in order to acquire updated images. This project will contribute to public health by developing a system enabling accurate navigation during neurosurgery. This will provide more complete information to the neurosurgeon, aiding in the task of removing as much tumor as possible while minimizing the loss of healthy tissue.