3D Cerebral vessel location for surgical planning

描述(逐字摘自申请者摘要)：本提案是一份竞争性提案 续签3D脑内血管成像的拨款。我们打算建造 在我们早期工作的基础上，提供了临床上有用的、象征性的描述 供应血管性中枢神经系统肿瘤的血管，包括实体瘤和 动静脉畸形(Avms)。对于外科医生和 对于干预主义者来说，提供一张 包含肿瘤边缘，显示了3D父子血管 连接性信息，它显示了动脉和 动静脉畸形中的静脉，并容易分离供应肿瘤的血管分支 来自供应正常大脑的分支。这些类型的信息是那些 临床医生最需要知道的是。此类信息不能直接提供给 临床医生通过任何现有的成像方法。 该方案的具体目标包括：1)实体肿瘤的分割 从MR，2)从磁共振血管成像(MRA)中分割AVM和 MR，3)MR与MRA数据的配准，4)血管成像方法的发展 分离动脉和静脉并勾勒出哪条动脉 分支(及其后代)只供应肿瘤，而供应大脑，以及5) 对我们方法的准确性和实用性的测试。 除了临床目标外，这项提案还旨在满足一些 计算机视觉研究中的挑战，包括使用 线索范围、基于统计图谱的命名血管的定义，以及 血管分支点的自动定义。我们建议的方法是 在其他地方是没有的，应该对临床医生有很大的帮助。我们建议 评估我们方法的准确性和实用性的多项研究，以及 我们还提出了新的可视化方法来表示我们的 图像对象的符号描述。

DESCRIPTION (Verbatim from Applicant's Abstract): This proposal is a competing renewal of a grant on 3D intracerebral vascular imaging. We intend to build upon our earlier work by providing a clinically useful, symbolic description of blood vessels supplying vascular CNS tumors, including both solid tumors and arteriovenous malformations (AVMs). For both the surgeon and the interventionalist, it would be enormously valuable to provide a 3D map that contains the tumor margins, that illustrates 3D parent-child vascular connectivity information, that shows the relationship between arteries and veins in AVMs, and that readily segregates vascular branches supplying a tumor from branches supplying normal brain. These kinds of information are those that the clinican most needs to know. Such information is not directly available to clinicians by any current imaging methodology. The specific aims of this proposal include: 1) segmentation of solid tumors from MR, 2) segmentation of AVMs from magnetic resonance angiograms (MRA) and MR, 3) registration of MR with MRA data, 4) development of methods of vascular desbription to separate arteries from veins and to delineate which arterial branches (and descendents) supply only tumor and which supply brain, and 5) tests of the accuracy and utility of our methodology. In addition to its clinical goals, this proposal aims to meet a number of challenges in computer vision research, including tumor segmentation using a range of cues, a statistical atlas based definition of named vessels, and automated definition of vessel branchpoints. The methods we propose are unavailable elsewhere and should be profoundly useful to clinicians. We propose a number of studies to evaluate the accuracy and utility of our approach, and we also propose new visualization methods to indicate the uncertainty of our symbolic descriptions of image objects.