QUANTITATIVE IMAGE MODELING FOR BRAIN TUMOR ANALYSIS AND TRACKING

用于脑肿瘤分析和跟踪的定量图像建模

• 批准号: 9706156
• 负责人: Khan M Iftekharuddin
• 金额: $ 4.65万
• 依托单位: OLD DOMINION UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9706156
• 关键词: Brain Neoplasms; Cause of Death; Classification; Clinical; Computer software; Detection; Early Diagnosis; Grant; Image; Image Analysis; Literature; Machine Learning; Magnetic Resonance Imaging; Malignant Neoplasms; Measures; Medical; Methods; Modeling; Monitor; Parents; Patients; Publishing; Research; System; Technology; Time; Tissues; Work; base; design; magnetic field; quantitative imaging; response; statistics; success

MRI images have been used for a wide variety of medical applications for a long time because they are safe and highly sensitive at detecting of tissue abnormalities that indicate cancer. MRI is generated by measuring the response of tissue components to a magnetic field. Also, based on the published statistics, brain tumor is one of the most common causes of death and early detection and monitoring is crucial for treatment. Literature and market review suggests that although extensive research exists on brain tumor detection using MRI images, MRI–based systems designed for brain tumor detection that have ultimate clinical value and use are lacking. Accordingly, we propose a new software technology that effectively detect, segment, classify and monitor brain tumor in MRI images.

长期以来，MRI图像已用于多种医疗应用 它们在检测表明癌症的组织异常时是安全且高度敏感的。 MRI是 通过测量组织成分对磁场的响应而产生。另外，基于 已发表的统计数据，脑肿瘤是死亡和早期检测的最常见原因之一 监测对于治疗至关重要。文学和市场审查表明，尽管 对使用MRI图像，基于MRI的系统进行了有关脑肿瘤检测的广泛研究 缺乏最终临床价值和使用的脑肿瘤检测。 根据，我们提出了一种有效检测，细分，分类和分类的新软件技术 在MRI图像中监测脑肿瘤。