Multimodal Registration of the Brain's Cortical Surface

描述（由申请人提供）：在今年确诊的20,500人中，有12,740人死于中枢神经系统癌症，其中约85-90%的患者患有脑肿瘤。5年生存率约为34-37%，尽管在过去20年中增加了20%。根据国家癌症研究所，手术切除是大多数脑肿瘤的推荐治疗方法，其目标是在保留神经功能的同时尽可能完整地切除。就国家神经疾病和中风研究所的使命而言，更完全的切除可以通过恢复功能、延长寿命和提高生活质量来减轻神经疾病的负担。在外科治疗方面，将患者在手术室（OR）内暴露的大脑与术前获得的神经解剖图像联系起来的视觉显示器的部署已经变得司空见惯。更具体地说，外科医生可以使用笔状的触笔指向患者脑组织的特定部位，并通过交互式显示器查看该组织在神经解剖图像上的位置。这一过程的一个不利因素是，当病人的大脑由于普通的外科操作而变形时。结果，图像和病人的大脑之间的一致性受到损害，手术错误可能随之而来。在最近的工作中，激光距离扫描（LRS）技术已被证明可以改善皮质表面的排列，并在手术中测量大脑变形。在这个应用中，LRS技术的使用将与计算机模型相结合，以纠正手术中变形引起的错位。待验证的假设是，计算机模型、术中环境的激光距离数据和跟踪触控笔数字化技术相结合，可以有效补偿图像引导脑肿瘤手术过程中的变形。研究这一假设的具体目标包括：(1)可以定位于无菌领域的综合数字化平台，(2)基于数字化平台收集的数据补偿变形的计算机算法，(3)可以处理数据并将数据可视化地呈现给外科医生以进行反馈的OR计算节点，以及(4)一系列临床研究来验证该方法。这项应用将是第一个完全实现的图像引导手术校正系统。该策略通过使用预计算和直接模拟来生成鲁棒、准确和快速的补偿方法，具有特别的创新性。考虑到验证的困难，我们还生成了一个3组件的验证方法，当它们结合在一起时，将提供对技术的良好评估。考虑到这项工作的重要性，在很大程度上，使用图像引导手术切除软组织器官主要局限于颅脑环境。有了本文提出的形变分辨率，将图像引导手术转化为其他软组织器官的能力成为可能。此外，与术中成像方法（例如MR）相比，该方法也便宜，并且可扩展，即能够广泛采用。这个应用程序的重点是产生下一个进化的图像制导。公共卫生相关性：在今年确诊的20,500人中，有12,740人死于中枢神经系统癌症，其中约85-90%的患者患有脑肿瘤。根据国家癌症研究所，手术切除是大多数脑肿瘤的推荐治疗方法，其目标是在保留神经功能的同时尽可能完整地切除。更完整的切除可以恢复功能，延长寿命，提高生活质量。这个项目的目标是帮助外科医生更完整地切除脑肿瘤。另一个重要的方面是，我们引进的技术相对便宜，而且可以被全国各地的医疗中心广泛采用。

DESCRIPTION (provided by applicant): Of the 20,500 people diagnosed, and the 12,740 deaths from cancer of the central nervous system this year, approximately 85-90% of these patients are afflicted with brain tumors. The 5-year survival rate is approximately 34-37% despite an increase by 20% over the past 20 years. According to the National Cancer Institute surgical removal is the recommended treatment for most brain tumors with the goal of the most complete resection possible while preserving neurological function. With respect to the mission of the National Institute for Neurological Disorders and Stroke, more complete resection reduces the burden of neurological disease by restoring function, extending life, and improving the quality of that life. With respect to surgical therapy, the deployment of visual displays that relate the patient's exposed brain within the operating room (OR) to the pre-operatively acquired neuroanatomical images has become commonplace. More specifically, surgeon's can use a pen-like stylus to point at a specific piece of the patient's brain tissue and see where that tissue resides on the neuroanatomical images as facilitated by an interactive display. One detriment to this process is when the patient's brain deforms due to common surgical manipulations. As a result, the alignment between images and the patient's physical brain becomes compromised and surgical error could ensue. In recent work, laser range scanning (LRS) technology has been demonstrated to both improve the alignment of the cortical surface and measure brain deformations during surgery. In this application, the use of LRS technology will be extended in conjunction with computer models to correct the deformation-induced misalignment during surgery. The hypothesis to be tested is that computer models, laser range data of the intra-operative environment, and tracked stylus digitization technology can be combined to effectively compensate for deformation during image-guided brain tumor surgery. The specific aims to investigate this hypothesis involve the development of: (1) a comprehensive digitization platform that can be positioned in the sterile field, (2) a computer algorithm to compensate for deformations based on data collected with digitization platform, (3) an OR compute node that can process data and present the data visually to the surgeon for feedback, and (4) a series of clinical studies to validate the approach. The work in this application will be the first fully realized correction system for image-guided surgery. The strategy is particularly innovative by using both pre-computation and direct simulation to generate a robust, accurate, and fast approach to compensation. Given the difficulty in validation, we have also generated a 3-component approach to validation, which when take together, will provide a good assessment of the techniques. With respect to the importance of this work, in large part, the use of image-guided surgery for surgical resection in soft-tissue organs has been confined primarily to the cranial environment. With the resolution of deformations as proposed herein, the ability to translate image-guided surgery to other soft-tissue organs becomes possible. Furthermore, the approaches herein are also inexpensive when compared to intra-operative imaging methods (e.g. MR), and scalable, i.e. capable of widespread adoption. This application is focused at producing the next evolution in image guidance. PUBLIC HEALTH RELEVANCE: Of the 20,500 people diagnosed, and the 12,740 deaths from cancer of the central nervous system this year, approximately 85-90% of these patients are afflicted with brain tumors. According to the National Cancer Institute surgical removal is the recommended treatment for most brain tumors with the goal of the most complete resection possible while preserving neurological function. More complete resection restores function, extends life, and improves the quality of that life. The goal of this project is to assist the surgeon in producing a more complete resection of brain tumors. The other important aspect is the technology we are introducing is relatively inexpensive and is amenable to widespread adoption by medical centers all across the country.