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

• 批准号: 3197545
• 负责人: Charles Raymond Meyer
• 金额: $ 29.4万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-08-06 至 1994-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/3197545
• 关键词: brain; clinical biomedical equipment; computed axial tomography; computer assisted medical decision making; computer assisted sequence analysis; computer data analysis; diagnosis quality /standard; disease /disorder model; evaluation /testing; glioma; image processing; laboratory rat; liver imaging /visualization /scanning; magnetic resonance imaging; mathematical model; model design /development; neoplasm /cancer radiation therapy; phantom model

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.

这项建议的目标是带来常规、准确的好处 3D体积估计和显示，目前仅在 高对比度边界，到低对比度，软组织病变和 器官。我们将开发、改进和应用新的、坚固的、自动的曲面 查找只需最少操作员干预即可定义的算法 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)。 提出的方法涉及到分割和稳健梯度的使用 一般病变/器官表面检测和几何检测技术 模型引导的黑板技术用于特定器官表面的检测。 算法开发将包括改编一种新的3D分割 算法最初是在由 美国国家科学基金会3D医学数据集分割。 由于该分割算法是第一个执行函数 对于3D数据的分割，它潜在地代表了一种革命性的 对由以下原因引起的不同边缘生成问题的突破 在逐片的基础上使用2D技术。此外，我们还将 将新的稳健边缘检测和样条线连接算法应用于 使用3D从原始数据集计算的梯度震级图像 梯度运算符。二维张量样条线将产生3D损伤 表面。由于单一方法技术往往缺乏所需的 为了达到近100%的信息检测准确率，我们还将 实现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对肝脏进行分析。虽然是人工的 智能技术已被应用于医学成像数据中 过去，据我们所知，这块特定的几何模型指导黑板 杠杆的方法是新的，并且有很大的机会实现 足够高的准确度，几乎消除了操作员的协助 或编辑。我们能够将这项技术应用于肝脏(器官) 一般的系统)，因为它们具有正常的形状和位置。 对算法的评估将主要使用临床数据集 来自接受放化疗的局灶性肝病患者 癌症、骨肉瘤和软组织肉瘤，以及物理和 计算机生成的幻影。艾滋病的主要临床受益者 在这些算法开发中取得实质性成功的是3D辐射 治疗计划和常规廉价的量化治疗 评估肿瘤对治疗的反应。