Algorithm Development for Magnetic Resonance Images (MRI)

磁共振图像 (MRI) 算法开发

• 批准号: RGPIN-2015-05493
• 负责人: Khademi, April
• 金额: $ 1.75万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=590464
• 关键词: Algorithm; Development; Magnetic; Resonance; Images

The visual analysis of medical images is subjective (observer-dependent), qualitative, error prone, and is inefficient, which ultimately effects diagnostic accuracy and the ability to conduct large research studies. Image analysis techniques offer a better alternative since they automatically measure image properties in a quantitative, objective, efficient, and reproducible manner. The purpose of this program is to advance the state-of-the-art in image analysis algorithms that operate only on Fluid-Attenuation Inversion Recovery (FLAIR) Magnetic Resonance Images (MRI) to quantify neurological disease in a novel and efficient way. Although FLAIR MRI are now the preferred modality for neurodegenerative disease detection and analysis, only few image processing methods exist and novel image processing algorithms are much needed.

医学图像的视觉分析是主观的（依赖于观察者）、定性的、容易出错的，而且效率低下，最终会影响诊断的准确性和进行大型研究的能力。图像分析技术提供了一个更好的选择，因为它们以定量、客观、有效和可重复的方式自动测量图像属性。该计划的目的是推进最先进的图像分析算法，仅对流体衰减反演恢复（FLAIR）磁共振图像（MRI）进行操作，以一种新颖有效的方式量化神经系统疾病。虽然FLAIR MRI现在是神经退行性疾病检测和分析的首选方式，但目前的图像处理方法很少，迫切需要新的图像处理算法。