Development of a Volume Imaging X-Ray Micro-CT Scanner

体积成像 X 射线微型 CT 扫描仪的开发

• 批准号: 9317816
• 负责人: Erik Ritman
• 金额: $ 39.68万
• 依托单位: Mayo Clinic Rochester
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1994
• 资助国家: 美国
• 起止时间: 1994-06-01 至 1996-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9317816&HistoricalAwards=false
• 关键词: Development; Volume; Imaging; Ray; Micro

9317816 Ritman The objective of this proposal is to fabricate and evaluate a prototype volume imaging x-ray micro-CT scanner (augmented by novel tomographic image reconstruction, 3D image analysis and display software) for application to biological specimens. The scanner design is inspired by a unique micro- CT scanner developed by a research group of the Exxon company for their geological purposes. Members of the Exxon Research team will provide consultations needed for duplication of important aspects of their scanner. This new capability has great potential for providing quantative insights into how mechanisms at the cellular level integrate to express themselves in the anatomy of organs at the micro and macroscopic scale. This proposed scanner will generate volume images of at least 5123 cubic voxels (10 to 40m on a side). This scanner extends current micro-CT methods, developed primarily for material sciences and nondestructive testing purposes, by imaging larger volumes with greater spatial resolution, within a reasonable period of time. This is an improvement over optical microscopic and conventional x-ray magnification techniques in that the logistics of image data analysis are greatly improved. The reason a computed tomographic imaging approach (which eliminates re-assembly of one-at-the-time scanned slices) and because we have already developed software for quantitative analysis and display of the 3D volume images. This software can be readily expanded to handle the huge 3D images (e.g., up to 109 voxels) to be generated with the proposed scanner. Several software approaches, which we have developed in recent years for 3D whole body scanning CT image data, should greatly extend the power of the x-ray micro-CT methodology as follows: a) Ability to scan objects greater than the size of the fluorescent screen because we can use the 'local' (as compared to the conventional 'global') reconstruction algorithm that our collaborators Drs. Faridani and Smith of Oregon State University have developed. This algorithm needs to 'see' only the projection through the volume of interest within a larger specimen. Consequently organs within the intact body of small animals can be scanned without the need to scan the entire transverse anatomic extent of the body, thereby greatly reducing scanner cost. b) Another new algorithm has been developed by Dr. Faridani that may provide almost double the spatial resolution "allowed" by the traditional relationship between numbers of angles-of-view and numbers of samples along the transverse x-ray absorption profile. c) Ability to automatically segment large and complex 3D images such as the entire arterial trees with its thousands of branches, using an algorithm of the type developed by Dr. Higgins of Pennsylvania State University. d) Ability to automatically analyze the segmented vascular trees for segmental dimensions and branching angles using modified versions of the algorithms and image analysis software developed in our laboratory for analysis of macrovascular arterial tree geometry.

9317816 Ritman本提案的目的是制造和评估原型体积成像X射线显微CT扫描仪（由新型断层图像重建，3D图像分析和显示软件增强），用于生物标本。 扫描仪的设计灵感来自埃克森公司的一个研究小组为地质目的开发的独特的微型CT扫描仪。 Exxon Research团队的成员将为扫描仪的重要方面的复制提供所需的咨询。 这种新的能力具有很大的潜力，提供定量的见解如何在细胞水平上整合，以表达自己在微观和宏观尺度上的器官解剖。 该扫描仪将生成至少5123个立方体素（边长10至40米）的体积图像。 该扫描仪扩展了目前主要为材料科学和无损检测目的开发的微型CT方法，在合理的时间内以更高的空间分辨率成像更大的体积。 这是对光学显微镜和常规X射线放大技术的改进，因为图像数据分析的后勤得到了极大的改进。 原因是计算机断层成像方法（消除了一次扫描切片的重新组装），因为我们已经开发了用于定量分析和显示3D体积图像的软件。 该软件可以容易地扩展以处理巨大的3D图像（例如，多达109个体素）。 我们近年来开发的用于3D全身扫描CT图像数据的几种软件方法应该大大扩展X射线微CT方法的能力，如下所示： a）、 能够扫描大于荧光屏尺寸的物体，因为我们可以使用我们的合作者俄勒冈州州立大学的Faridani和Smith博士开发的“局部”（与传统的“全局”相比）重建算法。 该算法只需要“看到”通过较大样本内的感兴趣体积的投影。 因此，可以扫描小动物的完整身体内的器官，而不需要扫描身体的整个横向解剖范围，从而大大降低扫描仪成本。 B） Faridani博士开发了另一种新算法，该算法可以提供几乎两倍于视角数量和沿横向X射线吸收轮廓沿着的样本数量之间的传统关系所“允许”的空间分辨率。 c）、 能够使用宾夕法尼亚州立大学的希金斯博士开发的算法自动分割大型复杂的3D图像，例如具有数千个分支的整个动脉树。 d）、 能够使用我们实验室开发的用于分析大血管动脉树几何形状的算法和图像分析软件的修改版本自动分析分段血管树的分段尺寸和分支角度。