US-Egypt Cooperative Research: Image Analysis for Identification of Renal Transplant Rejection

美埃合作研究：识别肾移植排斥的图像分析

• 批准号: 0610528
• 负责人: Aly Farag
• 金额: $ 3万
• 依托单位: University of Louisville Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-02-01 至 2009-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0610528&HistoricalAwards=false
• 关键词: US; Egypt; Cooperative; Research; Image

0610528FaragDescription: This award is to support a cooperative research by Dr. Aly Farag, Department of Electrical Engineering, Computer Vision & Image Processing Laboratory (CVIP Lab), University of Louisville, Louisville, Kentucky, and Dr. Tarek El-Diasty, Urology and Nephrology Center, Mansura University, Mansura, Egypt. They plan to investigate image registration, segmentation and analysis of the contrast enhanced MR images with specific application to kidney images. Renal imaging using Gd-DTPA Enhanced Dynamic MRI and Color Doppler Ultrasonography can be used for the early detection of rejection of kidney transplants. As rejection develops, transplanted kidneys show a noticeable increase in size and develop abnormal flow patterns that are not uniformly distributed throughout the whole kidney. Quantification of these changes, using image analysis can be used to detect rejections, thus replacing risky biopsy procedures. The PIs plan to combine the two powerful imaging techniques - Gd-DTPA Enhanced Dynamic MRI and Color Doppler Ultrasonography - in order to automatically detect the renal dysfunction of kidney transplants. The image analysis approach will be validated using manual approaches by expert physicians. Intellectual Merit: Dynamic MRI is a fast acquisition protocol that produces quite noisy and low resolution images compared to traditional MRI. Therefore, the proposed image analysis approach must deal with these restrictions and be able to distinguish the parts of the kidney (such as the cortex and medulla) that portrays its function, mainly blood purification. The focus in this project is on image registration, segmentation and analysis of the contrast enhanced MR images. While the research is of substantial importance in clinical diagnostic science it will stimulate new approaches in automated computer analysis and detection. The use of these innovative methods creates a robust and accurate registration method for various imaging conditions with varying geometrical and intensity status. The methodologies used are unique, and are highly developed at the PI's laboratory. Broader Impact: Image segmentation and registration cuts across nearly all applications of medical imaging analysis; hence, these methodologies will find greater usage in this dynamic field of research. The project will require transfer of many images over the internet; thus will benefit from information technology initiatives like the vBNS and Internet2 which have been supported by the NSF. The project will enhance collaboration between the US and developing counties, like Egypt, and promote positive exchanges of ideas and appreciation of cultures. The expected outcome is the creation of an image analysis approach for accurately tracking the kidney function after an implant. The findings will improve healthcare delivery of the increasing number of kidney transplant patients in Egypt, the US and worldwide.This project is being supported under the US-Egypt Joint Fund Program, which provides grants to scientists and engineers in both countries to carry out these cooperative activities.

描述：该奖项旨在支持肯塔基州路易斯维尔大学计算机视觉与图像处理实验室（CVIP实验室）电气工程系Aly Farag博士和埃及曼苏拉大学泌尿和肾脏学中心Tarek El-Diasty博士的一项合作研究。他们计划研究对比增强MR图像的图像配准、分割和分析，并将其具体应用于肾脏图像。Gd-DTPA增强动态MRI和彩色多普勒超声可用于早期发现肾移植排斥反应。随着排斥反应的发生，移植肾脏的体积明显增大，并出现不均匀分布于整个肾脏的异常血流模式。量化这些变化，使用图像分析可用于检测排斥反应，从而取代危险的活检程序。pi计划结合两种强大的成像技术- Gd-DTPA增强动态MRI和彩色多普勒超声-以自动检测肾脏移植的肾功能障碍。图像分析方法将由专家医师使用手动方法进行验证。知识优势：与传统MRI相比，动态MRI是一种快速采集协议，可产生相当嘈杂和低分辨率的图像。因此，所提出的图像分析方法必须处理这些限制，并能够区分肾脏的部分（如皮质和髓质）描绘其功能，主要是血液净化。本课题主要研究增强磁共振图像的配准、分割和分析。虽然这项研究在临床诊断科学中具有重要意义，但它将刺激自动化计算机分析和检测的新方法。这些创新方法的使用为具有不同几何和强度状态的各种成像条件创建了鲁棒和准确的配准方法。所使用的方法是独特的，并且在PI的实验室高度发展。更广泛的影响：图像分割和配准几乎跨越了所有医学成像分析的应用；因此，这些方法将在这个充满活力的研究领域得到更大的应用。该项目将需要在互联网上传输许多图像；因此将受益于NSF支持的信息技术倡议，如vBNS和Internet2。该项目将加强美国与埃及等发展中国家之间的合作，促进积极的思想交流和文化欣赏。预期的结果是创造一种图像分析方法，用于准确跟踪植入后的肾功能。这一发现将改善埃及、美国和全世界越来越多的肾移植患者的医疗服务。该项目得到了美国-埃及联合基金项目的支持，该项目向两国的科学家和工程师提供赠款，以开展这些合作活动。