Algorithm Development for Magnetic Resonance Images (MRI)

医学图像的视觉分析是主观的（依赖于图像质量）、定性的、容易出错的，并且是低效的，这最终影响诊断准确性和进行大型研究的能力。图像分析技术提供了更好的选择，因为它们以定量、客观、有效和可再现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，这些算法仅在液体衰减反转恢复（FLAIR）磁共振图像（MRI）上运行，以一种新颖有效的方式量化神经系统疾病。尽管FLAIR MRI现在是用于神经退行性疾病检测和分析的优选模式，但仅存在少数图像处理方法，并且非常需要新颖的图像处理算法。

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.