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    • 2. 发明授权
    • Anatomic and functional imaging of tagged molecules in animals
    • 动物中标记分子的解剖和功能成像
    • US07209579B1
    • 2007-04-24
    • US10341715
    • 2003-01-14
    • Andrew G. WeisenbergerStanislaw MajewskiMichael J. PaulusShaun S. Gleason
    • Andrew G. WeisenbergerStanislaw MajewskiMichael J. PaulusShaun S. Gleason
    • G06K9/00
    • A61B6/032A61B5/1077A61B5/1127A61B6/037A61B6/08A61B6/5235A61B6/5247A61B2503/40
    • A novel functional imaging system for use in the imaging of unrestrained and non-anesthetized small animals or other subjects and a method for acquiring such images and further registering them with anatomical X-ray images previously or subsequently acquired. The apparatus comprises a combination of an IR laser profilometry system and gamma, PET and/or SPECT, imaging system, all mounted on a rotating gantry, that permits simultaneous acquisition of positional and orientational information and functional images of an unrestrained subject that are registered, i.e. integrated, using image processing software to produce a functional image of the subject without the use of restraints or anesthesia. The functional image thus obtained can be registered with a previously or subsequently obtained X-ray CT image of the subject. The use of the system described herein permits functional imaging of a subject in an unrestrained/non-anesthetized condition thereby reducing the stress on the subject and eliminating any potential interference with the functional testing that such stress might induce.
    • 用于无限制和非麻醉的小动物或其他受试者的成像中的新型功能成像系统以及用于获取这些图像并进一步将它们与先前或随后获得的解剖X射线图像对准的方法。 该装置包括IR激光轮廓测量系统和全部安装在旋转台架上的伽马,PET和/或SPECT成像系统的组合,其允许同时获取被登记的无限制对象的位置和定向信息和功能图像, 即集成的,使用图像处理软件来产生受试者的功能图像而不使用约束或麻醉。 这样获得的功能图像可以与先前或随后获得的受试者的X射线CT图像对齐。 本文描述的系统的使用允许受试者在无限制/非麻醉状态下进行功能成像,从而减少受试者的压力并消除对这种应激可能诱发的功能测试的任何潜在干扰。
    • 3. 发明授权
    • Ultra-high resolution computed tomography imaging
    • 超高分辨率计算机断层成像
    • US06421409B1
    • 2002-07-16
    • US09496879
    • 2000-02-02
    • Michael J. PaulusHamed Sari-SarrafKenneth William Tobin, Jr.Shaun S. GleasonClarence E. Thomas, Jr.
    • Michael J. PaulusHamed Sari-SarrafKenneth William Tobin, Jr.Shaun S. GleasonClarence E. Thomas, Jr.
    • A61B603
    • G01T1/29Y10S378/901
    • A method for ultra-high resolution computed tomography imaging, comprising the steps of: focusing a high energy particle beam, for example x-rays or gamma-rays, onto a target object; acquiring a 2-dimensional projection data set representative of the target object; generating a corrected projection data set by applying a deconvolution algorithm, having an experimentally determined a transfer function, to the 2-dimensional data set; storing the corrected projection data set; incrementally rotating the target object through an angle of approximately 180°, and after each the incremental rotation, repeating the radiating, acquiring, generating and storing steps; and, after the rotating step, applying a cone-beam algorithm, for example a modified tomographic reconstruction algorithm, to the corrected projection data sets to generate a 3-dimensional image. The size of the spot focus of the beam is reduced to not greater than approximately 1 micron, and even to not greater than approximately 0.5 microns.
    • 一种用于超高分辨率计算机断层摄影成像的方法,包括以下步骤:将高能粒子束(例如x射线或γ射线)聚焦到目标物体上; 获取代表目标对象的二维投影数据集; 通过将具有实验确定的传递函数的去卷积算法应用于所述二维数据集来生成校正投影数据集; 存储校正的投影数据集; 逐渐旋转目标物体大约180°的角度,并且在每次增量旋转之后,重复辐射,获取,产生和存储步骤; 并且在旋转步骤之后,将锥束算法(例如修改的断层摄影重建算法)应用于校正的投影数据集以生成三维图像。 光束的焦点尺寸减小到不大于约1微米,甚至不大于约0.5微米。
    • 10. 发明授权
    • Context-based automated defect classification system using multiple morphological masks
    • 基于上下文的自动缺陷分类系统使用多种形态掩模
    • US06456899B1
    • 2002-09-24
    • US09454568
    • 1999-12-07
    • Shaun S. GleasonMartin A. HuntHamed Sari-Sarraf
    • Shaun S. GleasonMartin A. HuntHamed Sari-Sarraf
    • G06F1900
    • G03F7/7065G05B19/41875G05B2219/32186G05B2219/32196G05B2219/45031Y02P90/22Y10S706/90
    • Automatic detection of defects during the fabrication of semiconductor wafers is largely automated, but the classification of those defects is still performed manually by technicians. This invention includes novel digital image analysis techniques that generate unique feature vector descriptions of semiconductor defects as well as classifiers that use these descriptions to automatically categorize the defects into one of a set of pre-defined classes. Feature extraction techniques based on multiple-focus images, multiple-defect mask images, and segmented semiconductor wafer images are used to create unique feature-based descriptions of the semiconductor defects. These feature-based defect descriptions are subsequently classified by a defect classifier into categories that depend on defect characteristics and defect contextual information, that is, the semiconductor process layer(s) with which the defect comes in contact. At the heart of the system is a knowledge database that stores and distributes historical semiconductor wafer and defect data to guide the feature extraction and classification processes. In summary, this invention takes as its input a set of images containing semiconductor defect information, and generates as its output a classification for the defect that describes not only the defect itself, but also the location of that defect with respect to the semiconductor process layers.
    • 在制造半导体晶片期间自动检测缺陷大部分是自动化的,但是这些缺陷的分类仍由技术人员手动执行。 本发明包括生成半导体缺陷的唯一特征向量描述的新型数字图像分析技术以及使用这些描述来自动将缺陷分类为一组预定义类别的分类器。 使用基于多焦点图像,多缺陷掩模图像和分段半导体晶片图像的特征提取技术来创建关于半导体缺陷的独特的基于特征的描述。 这些基于特征的缺陷描述随后由缺陷分类器分类为依赖于缺陷特性和缺陷上下文信息的类别,即与缺陷接触的半导体处理层。 系统的核心是存储和分发历史半导体晶圆和缺陷数据的知识数据库,用于指导特征提取和分类过程。 总而言之,本发明将其包含半导体缺陷信息的图像作为其输入,并且作为其输出生成不仅描述缺陷本身的缺陷的分类,还产生相对于半导体处理层的该缺陷的位置 。