Ultrasound based neurosurgical navigation with uncertainty visualization

手术切除是几乎所有脑肿瘤的最初治疗方法，切除范围与肿瘤的大小密切相关。 预后然而，由于脑肿瘤，特别是神经胶质瘤，与周围功能性脑组织密切相关， 组织，积极的切除必须与引起新的神经功能缺损的风险相平衡。现代进步， 解剖和功能成像以及神经导航的广泛采用现在帮助神经外科医生计划和 进行最佳手术入路不幸的是，在手术过程中，大脑的形状发生了变化， 转移，使所有商业神经导航系统的假设无效，术前数据可以映射到 使用刚性配准的患者坐标。因为在手术过程中大脑的转移是不断进行的，神经系统的严格配准- 导航系统在切除的关键最后阶段，当边缘组织被去除时，是最不精确的。 已经有超过20年的研究投资于测量，建模和补偿大脑的变化， 目标是为神经导航系统提供从术前图像数据到 病人的大脑中存在的脑转移。虽然结果是有希望的，但它们还不够准确， 纳入商业体系。非刚性配准受测量和建模不确定性的影响 在整个3D空间中变化。大多数非刚性配准方法并不试图量化这种不确定性， 据我们所知，没有人试图向外科医生提出这种不确定性。我们认为， 使外科医生意识到这种不确定性，以便他们能够做出明智的决定，特别是在 不确定性很高。在这个项目中，我们计划研究非刚性配准算法，模型配准 明确的不确定性，利用配准的半自动和全自动非刚性配准方法 迭代指导配准改进的不确定性，以及有效呈现 外科医生在手术环境中的配准不确定性。 我们假设，有效的表示和可视化的配准不确定性的脑移位校正， 神经导航将1）导致迭代的半自动和全自动非刚性配准方法， 配准准确性和2）允许神经外科医生在肿瘤切除术期间做出更明智的决定， 增加影像引导神经外科的临床影响。我们将实现以下目标：1.开发新功能- 基于图像配准算法，明确表示不确定性; 2.使用配准不确定性图指导 半自动和全自动非刚性配准; 3.通过不确定性可视化评估非刚性配准的效用 在临床环境中。

Surgical resection is the initial treatment for nearly all brain tumors and the extent of resection is strongly correlated with prognosis. However, because brain tumors, especially gliomas, are intimately involved in surrounding functioning brain tissue, aggressive resection must be balanced against the risk of causing new neurological deficits. Modern advances in anatomical and functional imaging and the widespread adoption of neuro-navigation now help neurosurgeons to plan and execute an optimal surgical approach. Unfortunately, changes in the shape of the brain during surgery, known as brain shift, invalidate the assumption of all commercial neuro-navigation systems that preoperative data can be mapped to patient coordinates using rigid registration. Because brain shift progresses during surgery, the rigid registration of neuro- navigation systems is least accurate at the critical final stages of resection when the marginal tissue is being removed. There has been more than 20 years of research invested in measuring, modeling and compensating for brain shift with the goal of providing neuro-navigation systems with an accurate nonrigid registration from preoperative image data to the patient’s brain in the presence of brain shift. While results are promising, they are not yet accurate enough to be incorporated into commercial systems. Nonrigid registration is subject to both measurement and modeling uncertainty that varies throughout 3D space. Most nonrigid registration methods do not attempt to quantify this uncertainty and, to our knowledge, there have been no attempts to present this uncertainty to the surgeon. We believe that it is important to make surgeons aware of this uncertainty so that they can make informed decisions, particularly in locations where uncertainty is high. In this project, we plan to investigate nonrigid registration algorithms that model registration uncertainty explicitly, semi-automatic and fully-automatic nonrigid registration methods that utilize registration uncertainty to iteratively guide registration improvements, and visualization paradigms for effective presentation of registration uncertainty to surgeons in the surgical environment. We hypothesize that effective representation and visualization of registration uncertainty for brain shift correction in neuro-navigation will 1) lead to iterative semi-automatic and fully-automatic nonrigid registration methods that improve registration accuracy and 2) allow neurosurgeons to make more informed decisions during tumor resections that will lead to increased clinical impact of image-guided neurosurgery. We will carry out the following Aims: 1. Develop novel feature- based image registration algorithms that represent uncertainty explicitly; 2. Use registration uncertainty maps to guide semi- and fully-automatic nonrigid registration; 3. Evaluate the utility of nonrigid registration with uncertainty visualization in a clinical setting.