Continuous Compensation of Brain Shift during Neurosurgery

项目摘要/摘要 美国有近70万人患有原发性脑瘤，新增病例近8万人， 预计2018年将有11,000人死亡。手术切除是对抗脑瘤的第一道防线，因为它可以缓解症状， 降低癫痫风险，延长预期寿命。最近的研究表明，肿瘤切除的程度很强。 与免于进展和生存的自由有关。然而，肿瘤通常位于危重器官及其周围。 大脑结构和破坏这些结构会导致大脑功能丧失。因此，脑外科的首要目标是 以最大限度地扩大肿瘤切除的范围，同时将对周围脑组织的损害降至最低。商业广告 神经导航系统在手术过程中向外科医生提供手术前图像数据，可以帮助他们可视化 他们的手术器械相对于这些关键结构的位置。不幸的是，商业系统不能 补偿手术期间大脑的渐进性变形，即所谓的脑移位，其大小可达1-2 因此，神经导航系统的精确度在手术过程中逐渐下降。 需要的是一种在手术过程中持续测量和补偿大脑移动的方法，以便外科医生能够 及时、最新的信息，包括切除了哪些组织、保留了哪些组织以及关键大脑附近的位置 结构是。这样的系统将使神经外科医生能够及时做出决定，延长预期寿命和 提高生活质量。虽然在手术过程中对大脑变形进行建模和一些端到端的研究是有前途的研究 结束提供大脑转移补偿的研究系统，目前的方法有一些局限性。在……里面 具体地说，他们依赖于术中数据，而这些数据只在手术中的几个时间点上可用，而且他们需要许多 在模型更新之前，可以向外科医生提交几分钟的计算。 这项建议解决了最先进的脑转移补偿研究中的三个瓶颈，这些瓶颈阻止了其 在临床实践中的应用：1)目前的脑转移模型方法不能直接测量被移除的部分 在肿瘤切除过程中，它们往往在切除边界处不准确，而这正是准确性最高的地方 需要；2)模拟大脑转移的算法需要大量的预处理和计算能力，并且速度太慢 为外科医生提供及时的反馈；以及3)术中图像采集是破坏性的、耗时的和 价格昂贵，因此更新不频繁。在本提案中，我们将通过应用开发的算法来解决这些瓶颈 用于实时计算机图形建模切除腔和脑移位，以及一种新的设备和手术流程， 将允许我们在不中断手术的情况下收集3D超声波，这样我们就可以频繁地监测大脑的变化。这 项目将解决这些瓶颈问题，具体目标如下： 目的1.研究自适应采样距离场在肿瘤切除建模中的应用 目的2.扩展和研究3D Chainmail对术中超声实时脑移位建模的应用 目的3：用一种新的手术流程和一个手术中的原型展示持续的脑转移监测 超声波仪

Project Summary / Abstract There are nearly 700,000 people living with primary brain tumors in the United States, with nearly 80,000 new cases and 11,000 deaths expected in 2018. Surgical removal is the first defense against brain tumors because it relieves symptoms, decreases seizure risks and increases life expectancy. Recent studies have shown that extent of tumor resection is strongly correlated with both freedom from progression and survival. However, tumors are often situated in and around critical brain structures and damaging these structures can cause loss of brain function. Thus, the primary goal in brain surgery is to maximize the extent of tumor resection while minimizing damage to surrounding brain tissue. Commercial neuronavigation systems present pre-operative image data to the surgeon during surgery that can help them visualize the location of their surgical instruments relative to these critical structures. Unfortunately, commercial systems do not compensate for progressive deformation of the brain during surgery, known as brain shift, which can be as large as 1-2 centimeters so the accuracy of neuronavigation systems decreases progressively during surgery. What is needed is a way to measure and compensate for brain shift continuously during surgery so that surgeons have timely, up-to-date information about what tissue has been removed, what tissue remains, and where nearby critical brain structures are. Such a system would enable neurosurgeons to make timely decisions that increase life expectancy and improve quality-of-life. While there is promising research in modeling brain deformation during surgery and a few end-to- end research systems that provide brain shift compensation, current approaches have a number of limitations. In particular, they rely on intraoperative data that is only available at a few time-points during surgery and they require many minutes of computation before model updates can be presented to the surgeon. This proposal addresses the three bottlenecks in state-of-the-art brain shift compensation research that prevent its adoption in clinical practice: 1) current method for modeling brain shift do not directly measure what has been removed during tumor resection so they tend to be inaccurate at the resection boundary, which is precisely where accuracy is most needed; 2) algorithms for modeling brain shift require significant preprocessing, computational power, and are too slow to provide timely feedback to the surgeon; and 3) intraoperative image acquisition is disruptive, time consuming and expensive so updates are infrequent. In this proposal we will address these bottlenecks by applying algorithms developed for real-time computer graphics to model the resection cavity and brain shift, and a new device and surgical workflow that will allow us to collect 3D ultrasound without disrupting surgery so we can monitor brain shift at frequent intervals. This project will address these bottlenecks with the following specific aims: Aim 1. Investigate the use of Adaptively Sampled Distance Fields for modeling of tumor resection Aim 2. Extend and investigate the use of 3D Chainmail for real-time brain shift modeling from intraoperative ultrasound Aim 3. Demonstrate continuous brain shift monitoring with a new surgical workflow and a prototype intraoperative ultrasound device