COMPUTED 3D SURFACE AND VOLUME ESTIMATION IN CT AND MRI

CT 和 MRI 中的计算 3D 表面和体积估计

基本信息

批准号：
2094933
负责人：
Charles Raymond Meyer
金额：
$ 30.42万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
1991
资助国家：
美国
起止时间：
1991-08-06 至 1995-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/2094933
关键词：
computed axial tomography computer data analysis evaluation /testing image processing liver imaging /visualization /scanning magnetic resonance imaging mathematical model model design /development phantom model

项目摘要

The goal of this proposal is to bring the benefits of routine, accurate 3D volume estimation and display, currently implemented exclusively on high contrast boundaries, to low contrast, soft tissue lesions and organs. We will develop, refine and apply new, robust, automatic surface finding algorithms which require minimal operator intervention to define the spatial extent of low contrast, soft tissue lesions and organs in x- ray computed tomography (CT) and magnetic resonance imaging (MRI). Methods proposed involve usage of segmentation, and robust gradient techniques for general lesion/organ surface detection, and geometric model-guided blackboard techniques for specific organ surface detection. Algorithm development will include adaptation of a new 3D segmentation algorithm originally developed for computer vision under funding from the National Science Foundation for segmentation of 3D medical data sets. Since this segmentation algorithm is the first to perform functional segmentation on 3D data, it potentially represents a revolutionary breakthrough to the problem of disparate edge generation arising from the use of 2D techniques on a slice-by-slice basis. In addition, we will apply new robust edge segment detection and spline linking algorithms to gradient magnitude images computed from the original data set using 3D gradient operators. Bivariate tensor splines will yield 3D lesion surfaces. Since single approach techniques tend to lack the required information to achieve nearly 100% detection accuracy, we will also implement a knowledge-based blackboard system to guide surface detection of the liver using an a priori, geometric shape model. While artificial intelligence techniques have been applied to medical imaging data in the past, to our knowledge this specific geometric model-guided blackboard approach to lever is new, and has a significant chance of achieving a sufficiently high accuracy so as to nearly eliminate operator assistance or editing. We are able to apply this technique to the liver (organ systems in general) because they have a normal shape and location. Evaluation of the algorithms will make major use of clinical data sets from patients undergoing radiation and chemo therapy for focal liver carcinoma, osteogenic and soft tissue sarcomas, as well as physical and computer generated phantoms. The major clinical beneficiaries of substantial success in these algorithm developments are 3D radiation therapy treatment planning and routine and inexpensive quantitative assessment of tumor response to therapy.

该提案的目的是带来常规，准确的好处 3D卷估计和显示，目前仅在高对比度的边界，低对比度，软组织病变和器官。我们将开发，完善并应用新的，健壮的自动表面查找需要最小操作员干预以定义的算法 X-中低对比度，软组织病变和器官的空间范围射线计算机断层扫描（CT）和磁共振成像（MRI）。提出的方法涉及分割和鲁棒梯度的使用通用病变/器官表面检测的技术和几何技术用于特定器官表面检测的模型引导的黑板技术。算法开发将包括适应新的3D细分最初是为计算机视觉开发的算法国家科学基金会3D医学数据集的细分基金会。由于该分割算法是第一个执行功能的算法对3D数据的细分，它有可能代表革命性的突破性的边缘生成问题的突破逐片使用2D技术。此外，我们将应用新的强大边缘段检测和样条将算法链接到使用3D从原始数据集计算出的梯度幅度图像梯度操作员。双变量张量条将产生3D病变表面。由于单一方法技术往往缺乏所需的信息以达到近100％的检测准确性，我们也将实施基于知识的黑板系统来指导表面检测使用先验的几何形状模型使用肝脏。而人造智能技术已应用于医学成像数据过去，据我们所知，这种特定的几何模型引导黑板杠杆的方法是新的，并且有很大的机会实现足够高的精度，几乎消除了操作员的帮助或编辑。我们能够将此技术应用于肝脏（器官系统通常），因为它们具有正常的形状和位置。算法的评估将主要利用临床数据集来自接受辐射的患者和局灶性肝脏化学疗法癌，成骨和软组织肉瘤以及身体和计算机生成的幻影。主要的临床受益者在这些算法开发中取得了巨大成功是3D辐射治疗治疗计划和常规和廉价定量评估肿瘤对治疗的反应。