Sridharan Ramesh, Dalca Adrian V, Fitzpatrick Kaitlin M, Cloonan Lisa, Kanakis Allison, Wu Ona, Furie Karen L, Rosand Jonathan, Rost Natalia S, Golland Polina
Computer Science and Artificial Intelligence Lab, MIT.
Department of Neurology, Massachusetts General Hospital, Harvard Medical School.
Multimodal Brain Image Anal (2013). 2013;8159:18-30. doi: 10.1007/978-3-319-02126-3_3.
We present an analysis framework for large studies of multimodal clinical quality brain image collections. Processing and analysis of such datasets is challenging due to low resolution, poor contrast, mis-aligned images, and restricted field of view. We adapt existing registration and segmentation methods and build a computational pipeline for spatial normalization and feature extraction. The resulting aligned dataset enables clinically meaningful analysis of spatial distributions of relevant anatomical features and of their evolution with age and disease progression. We demonstrate the approach on a neuroimaging study of stroke with more than 800 patients. We show that by combining data from several modalities, we can automatically segment important biomarkers such as white matter hyperintensity and characterize pathology evolution in this heterogeneous cohort. Specifically, we examine two sub-populations with different dynamics of white matter hyperintensity changes as a function of patients' age. Pipeline and analysis code is available at http://groups.csail.mit.edu/vision/medical-vision/stroke/.
我们提出了一个用于对多模态临床高质量脑图像集进行大规模研究的分析框架。由于分辨率低、对比度差、图像未对齐以及视野受限，对此类数据集进行处理和分析具有挑战性。我们采用现有的配准和分割方法，并构建了一个用于空间归一化和特征提取的计算管道。由此得到的对齐数据集能够对相关解剖特征的空间分布及其随年龄和疾病进展的演变进行具有临床意义的分析。我们在一项对800多名患者的中风神经影像学研究中展示了该方法。我们表明，通过结合来自多种模态的数据，我们可以自动分割诸如白质高信号等重要生物标志物，并在这个异质性队列中表征病理演变。具体而言，我们研究了两个白质高信号变化动态随患者年龄而异的亚组。管道和分析代码可在http://groups.csail.mit.edu/vision/medical-vision/stroke/获取。
