Multimodal Registration of the Brain's Cortical Surface

大脑皮质表面的多模态配准

• 批准号: 6924475
• 负责人: Michael Ian Miga
• 金额: $ 30.99万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-04-01 至 2008-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6924475
• 关键词: biomedical automation; brain imaging /visualization /scanning; brain mapping; brain morphology; brain neoplasms; clinical research; computed axial tomography; cues; digital imaging; human subject; image enhancement; image guided surgery /therapy; lasers; magnetic resonance imaging; method development; nervous system disorder therapy; neurosurgery; patient oriented research; preoperative state; visual fields

DESCRIPTION (provided by applicant): Within the backdrop of a cost-conscious healthcare system, the need for providing quantitative relationships between the abundance of preoperative imaging information and the physical patient is clear. This is particularly important within the neurosurgical context where there is a distinct lack of anatomical landmarks. Developing a cost-effective method to improve a neurosurgeon's recognition of cortical brain structures during surgery as well as dynamically tracking these regions intraoperatively would be an important contribution to image-guided procedures. Laser range scanning (LRS) is a candidate technology that has the advantage of providing a wealth of intraoperative data with minimal space requirements. The work proposed herein involves a clinically translatable use of LRS technology that could have direct impact on surgical therapy for patients afflicted with brain tumors. With current technology, neurosurgeons often develop a surgical treatment plan by studying the patient's MR tomogram as a segmented reconstructed gray-scale encoded volume rendering. By visualizing the segmented brain in its three-dimensional state with the MR gray-scale providing anatomical landmarks, the surgeon can identify sensitive regions which may be important in the delivery of therapy. For example, gyri that are associated with primary, motor, somesthetic, auditory and visual functions can be identified usually on these renderings or with the assistance of cortical stimulation and/or functional magnetic resonance (MR) imaging. Unfortunately, these regions can often be difficult to recognize intraoperatively due to the lack of landmark recognition within the surgeon's field of view (FOV), i.e. the intraoperative presentation of the cortical surface. One fundamental hypothesis encompassing this proposed research is that textured (texture is from a digital image of the FOV) laser-range scan (tLRS) data can be used to correlate preoperative image data to the surgeon's field of view using a multi-modal registration approach. Specifically, the aims within this proposal are focused at: (1) enhancing surgical guidance by registering textured point clouds to the MR tomogram, (2) providing new anatomical cues to the surgeon, and (3) measuring cortical brain shift during surgical procedures. Although these enhancements are critical for correlating the cortical surface to its MR counterpart, the need to characterize subsurface tissue motion is equally important. As a result, this proposal outlines a strategy to directly address the use of tLRS data as a means to predict subsurface shift within patients undergoing a neurosurgical intervention. In summary, if image-guided procedures are to advance surgical therapy, the next evolution of these systems will require methods to account for soft tissue deformation. The tLRS studies proposed herein represent a clinically translatable strategy for documenting brain shift which could be pivotal in developing affordable image-guided platforms capable of compensating for soft tissue deformation.

描述（由申请人提供）： 在具有成本意识的医疗保健系统的背景下，很明显地在术前成像信息和身体患者之间提供定量关系的需求是显而易见的。这在神经外科背景下尤其重要，因为神经外科背景明显缺乏解剖标志。开发一种具有成本效益的方法来改善神经外科医生对手术过程中皮质脑结构的识别，并在术中动态跟踪这些区域将是对图像引导程序的重要贡献。激光范围扫描（LRS）是一种候选技术，其优势是提供大量术中数据，具有最小的空间要求。本文提出的工作涉及LRS技术的临床翻译使用，该技术可能会直接影响患有脑肿瘤的患者手术疗法。借助当前技术，神经外科医生通常通过研究患者的MR断层图作为分段的重建灰度编码体积渲染来制定手术治疗计划。通过与MR灰度提供解剖学地标的三维状态的分段大脑，外科医生可以识别敏感区域，这对治疗的提供可能很重要。例如，可以在这些效果图上或在皮质刺激和/或功能性磁共振（MR）成像的帮助下鉴定与原代，运动，躯体，听觉和视觉功能相关的回旋。不幸的是，由于外科医生的视野（FOV）（即皮质表面的术中表现），这些区域通常很难在术中识别。一个涵盖这项拟议研究的基本假设是，纹理（纹理来自FOV的数字图像）激光范围扫描（TLRS）数据可用于使用多模式注册方法将术前图像数据与外科医生的视野相关联。具体而言，该提案中的目的集中在：（1）通过向MR Tomogram注册纹理点云来增强手术指导，（2）（2）向外科医生提供新的解剖学提示，以及（3）在外科手术过程中测量皮质脑移位。尽管这些增强对于将皮质表面与其MR对应物相关联至关重要，但是表征地下组织运动的需求同样重要。结果，该提案概述了直接解决TLRS数据作为预测神经外科干预患者内表面转移的手段的一种策略。总而言之，如果图像引导的程序是为了进步手术治疗，那么这些系统的下一个演变将需要解决软组织变形的方法。本文提出的TLRS研究代表了记录大脑转移的临床翻译策略，这对于能够补偿软组织变形的可负担得起的图像引导的平台可能是关键的。