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

0610528 Farag描述：该奖项是为了支持肯塔基州路易斯维尔大学计算机视觉图像处理实验室（CVIP实验室）电气工程系的Aly Farag博士和埃及曼苏拉曼苏拉大学泌尿学和肾脏病学中心的Tarek El-Diasty博士的合作研究。 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和彩色多普勒超声肾显像可用于早期发现肾移植排斥反应。随着排斥反应的发展，移植的肾脏显示出明显的尺寸增加，并形成不均匀分布在整个肾脏中的异常流动模式。使用图像分析对这些变化进行定量可用于检测排斥反应，从而取代有风险的活检程序。PI计划结合联合收割机两种强大的成像技术- Gd-DTPA增强动态MRI和彩色多普勒超声检查-以自动检测肾移植的肾功能障碍。图像分析方法将由专家医师使用手动方法进行确认。智力优点：动态MRI是一种快速采集协议，与传统MRI相比，可以产生噪音很大且分辨率较低的图像。因此，所提出的图像分析方法必须处理这些限制，并且能够区分描绘其功能（主要是血液净化）的肾脏部分（例如皮质和髓质）。 本项目的重点是对比增强MR图像的图像配准、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，但它将刺激自动计算机分析和检测的新方法。 这些创新方法的使用为具有变化的几何和强度状态的各种成像条件创建了鲁棒且准确的配准方法。 所使用的方法是独特的，并在PI的实验室高度发达。 更广泛的影响：图像分割和配准几乎贯穿医学成像分析的所有应用;因此，这些方法将在这个动态研究领域中找到更大的用途。该项目将需要通过互联网传输许多图像;因此将受益于信息技术倡议，如vBNS和Internet 2，这些倡议得到了NSF的支持。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。预期的结果是创建一种图像分析方法，用于准确跟踪植入后的肾功能。这些发现将改善埃及、美国和全球越来越多的肾移植患者的医疗保健服务。该项目得到了美国-埃及联合基金计划的支持，该计划为两国的科学家和工程师提供赠款，以开展这些合作活动。

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.