Algorithm Development for Magnetic Resonance Images (MRI)

医学图像的视觉分析是主观的(依赖于观察者的)、定性的、容易出错的，并且效率低下，这最终影响了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更好的替代方案，因为它们以定量、客观、高效和可重现的方式自动测量图像属性。该计划的目的是推进图像分析算法的最先进水平，这些算法只在液体衰减反转恢复(FLAIR)磁共振图像(MRI)上运行，以一种新颖而有效的方式对神经疾病进行量化。虽然FLAIR MRI是目前神经退行性疾病检测和分析的首选方法，但图像处理方法很少，迫切需要新的图像处理算法。*这项研究的目标围绕1)FLAIR MRI神经解剖学和病理学的分割算法的开发，以便从组织中提取体积、纹理和形状等特征，以及2)将FLAIR MRI注册到图谱，以便在空间归一化空间中分析脑图像，研究纵向数据中的疾病进展(即病变体积的变化)，以及识别重要的解剖标志。考虑到神经系统疾病的流行、人口老龄化以及加拿大医疗体系的相关经济负担，这样的计划有可能对许多加拿大人产生积极影响。

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. ***The objectives of this research proposal surround the development of 1) segmentation algorithms for neuroanatomy and pathology in FLAIR MRI in order to extract features from tissues such as volume, texture and shape, and 2) registration of FLAIR MRI to atlases in order to analyze brain images in a spatially normalized space, to study progression of disease in longitudinal data (i.e. change in lesion volume), and also to identify important anatomical landmarks. Considering the neurological disease prevalence, aging demographics, as well as associated economic burden on the Canadian healthcare system, such a program has the potential to positively impact many Canadians.