Computer Assisted Identification and Volumetric Analysis of Enhancing Components

增强成分的计算机辅助识别和体积分析

基本信息

批准号：
7816795
负责人：
Mubarak Ali Shah
金额：
$ 19.58万
依托单位：
UNIVERSITY OF CENTRAL FLORIDA
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-05-01 至 2012-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7816795
关键词：
Archives Area Brain Brain Neoplasms Brain imaging Clinical Clinical Trials Clinical Trials Cooperative Group Computer Assisted Computer Vision Systems Computers Development Diagnosis Diagnostic Dimensions Disease Double-Blind Method Evaluation Exploratory/Developmental Grant Gadolinium DTPA Goals Gold Growth Human Image Image Analysis Knowledge Label Lead Learning Magnetic Resonance Imaging Manuals Measurement Measures Medical Imaging Methodology Methods Monitor Neoplasms Neuraxis Outcome Patients Performance Pharmaceutical Preparations Physicians Pilot Projects Postdoctoral Fellow Randomized Regimen Reporting Reproducibility Research Research Project Grants Resolution Retrospective Studies Risk Scanning Scientist Services Slice Solid Structure System Techniques Technology Testing Time Tissues Training Treatment Protocols Tumor Volume base chemotherapeutic agent clinical decision-making cost design experience follow-up improved indexing knowledge base method development neoplastic novel patient population public health relevance radiologist response statistics tool treatment planning tumor

项目摘要

DESCRIPTION (provided by applicant): The assessment of volumetric change of enhancing tissue is regarded as an important parameter used by clinicians when seeking to monitor the response of brain neoplasms to therapy. Unfortunately, direct computation of enhancing volume requires the manual tracing and segmentation of areas of enhancement typically extending over multiple images, which is time consuming, labor intensive, and therefore impractical. Substitute methods are widely utilized (such as bi-directional measurements). Such surrogate measurements for tumor volume become problematic. Our long-term goal is to seek an objective, computer aided diagnostic (CAD) methodology for automatic computation of tumor (enhancing tissue) volume relevant for clinical decision making. Our hypothesis behind this project is that volume of enhancing tissue can be accurately measured through the use of advanced computer vision techniques, which will lead to an effective CAD system able to assist radiologists analyzing MR brain images. The specific aims of the proposed project are 1) to improve the accuracy of the measurement of enhancing tissue by constructing high resolution 3D MR images and labeling enhancing tissue using learning based computer vision techniques, and 2) to develop a CAD system for enhancing tissue volume assessment using the designed techniques and evaluate the performance of the system on assisting radiologists for image interpretation. We believe this system will be well suited for use in patients undergoing treatment protocols/clinical trials who require short term serial imaging. It will better enable radiologists to give accurate quantitative clinical information. If the proposed research is completed successfully, the determination of enhancing tissue volume will be significantly advanced. It will enable the radiologists to rapidly provide objective, accurate, reproducible, and easily reported assessment of the tumor status. This will lead to a more rapid and reproducible assessment of neoplasm and therefore, hopefully influence patient outcomes in a positive way. The proposed research will also be applicable for usage on archived studies thereby enabling the volume of enhancing tissue to be calculated on these images as well. PUBLIC HEALTH RELEVANCE: The goal of this research is to develop a more accurate and reproducible way to measure the amount of disease present in patients suffering from brain tumors. Measurement methods currently being used are limited in accuracy, reproducibility and efficiency and hence we propose a method, if successful, will enable the computer to identify and measure brain tumors in a more automated fashion with improved accuracy. Improvements in tumor volume assessment are important for treatment planning as well as for assessing response during clinical drug trials.

描述（由申请人提供）：增强组织的体积变化的评估被视为临床医生在寻求监测脑肿瘤对治疗的反应时使用的重要参数。不幸的是，增强体积的直接计算需要手动跟踪和分割通常延伸到多个图像的增强区域，这是耗时、劳动密集型的，因此不切实际。替代方法被广泛使用（例如双向测量）。这种肿瘤体积的替代测量变得有问题。我们的长期目标是寻求一种客观的计算机辅助诊断 (CAD) 方法，用于自动计算与临床决策相关的肿瘤（增强组织）体积。我们对该项目的假设是，通过使用先进的计算机视觉技术可以准确测量增强组织的体积，这将产生一个有效的 CAD 系统，能够帮助放射科医生分析 MR 脑图像。该项目的具体目标是 1) 通过构建高分辨率 3D MR 图像并使用基于学习的计算机视觉技术标记增强组织来提高增强组织测量的准确性，2) 开发用于增强组织体积的 CAD 系统使用设计的技术进行评估，并评估系统在协助放射科医生进行图像解释方面的性能。我们相信该系统将非常适合接受需要短期连续成像的治疗方案/临床试验的患者。它将更好地使放射科医生能够提供准确的定量临床信息。如果拟议的研究成功完成，增强组织体积的确定将显着推进。它将使放射科医生能够快速提供客观、准确、可重复且易于报告的肿瘤状态评估。这将导致对肿瘤进行更快速和可重复的评估，因此有望以积极的方式影响患者的治疗结果。拟议的研究也将适用于存档研究，从而使增强组织的体积也能够在这些图像上计算。公共健康相关性：这项研究的目标是开发一种更准确、可重复的方法来测量脑肿瘤患者的疾病数量。目前使用的测量方法在准确性、可重复性和效率方面受到限制，因此我们提出一种方法，如果成功的话，将使计算机能够以更自动化的方式识别和测量脑肿瘤，并提高准确性。肿瘤体积评估的改进对于治疗计划以及临床药物试验期间的反应评估非常重要。