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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.

这个建议的目标是带来常规的好处，准确的 3D体积估计和显示，目前仅在高对比度边界，低对比度，软组织病变，机关我们将开发、改进和应用新的、坚固的、自动化的表面寻找需要最少操作员干预的算法，低对比度、软组织病变和器官在x- X线计算机断层扫描（CT）和磁共振成像（MRI）。提出的方法涉及使用分割，和鲁棒梯度用于一般病变/器官表面检测的技术，以及几何用于特定器官表面检测的模型引导黑板技术。算法开发将包括新3D分割的适应性算法最初是在计算机视觉的资助下开发的美国国家科学基金会的3D医学数据集分割。由于这种分割算法是第一次执行功能在3D数据上的分割，它可能代表了一种革命性的突破了由在逐切片的基础上使用2D技术。此外，我们会应用新的鲁棒边缘段检测和样条连接算法，使用3D从原始数据集计算的梯度幅值图像梯度算子双变量张量样条将产生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%的检测准确率，我们还将实现基于知识黑板系统来指导表面检测使用先验的几何形状模型。而人工智能技术已经应用于医学成像数据，过去，据我们所知，这个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以便几乎消除操作员辅助或编辑。我们能够将这种技术应用于肝脏（器官）一般来说，系统），因为它们具有正常的形状和位置。算法的评价将主要使用临床数据集来自因局灶性肝脏接受放疗和化疗的患者癌、成骨肉瘤和软组织肉瘤，以及物理和计算机生成的幻影的主要临床受益者在这些算法开发中取得实质性成功是3D辐射治疗计划和常规和廉价的定量评估肿瘤对治疗的反应。