Image Features for Brain Phenotypes

项目总结/摘要研发3脑表型图像特征 现代大脑成像提供了比以前多得多的信息。虽然这些信息具有巨大的价值 为了给患者带来好处，放射科医生的工作量随着分辨率的提高和数量的增加而增加 要解读的图像。此外，罕见的脑畸形、罕见的疾病和 弥漫性表现或在两个半球之间对称，会给放射科医生带来特别的挑战。 尽管医院影像档案的信息内容丰富，但临床上还没有可用的方法来 自动利用同一家医院的大量先前病例，以帮助进行放射检查。 为了实现为临床辅助阅读开发机器学习系统的长期目标，The Image 脑表型特征研发将研究3D脑图像特征，用于描述健康和 不健康的大脑表型。图像要素是具有以下特征的图像内容的表示形式 优点：它们紧凑、信息量大、便于快速搜索，并支持机器学习分析 大型数据集。即使在大规模并行计算机时代，这些优势也使临床问题成为可能 否则，由于问题的规模和复杂性，解决这一问题在实践中是不可行的。 我们假设图像特征的相似性与潜在疾病的相似性密切相关，并且 这可以被用来建立新的工具来支持放射决策。这个项目被组织成三个部分 具体目标：(1)大脑表型的强健图像特征，将形成特征和相关 3D结构图像的算法，(2)基于连通性的大脑表型将研究新的 用于扩散MRI图像的特征，以及(3)基于特征的临床表型，将部署和 在临床MRI数据库中验证方法。我们将重点介绍放射影像的具体应用。 我们将利用来自波士顿医院的三个大型临床核磁共振数据库。 总的来说，我们提出的基于特征的图像分析技术具有识别图像的潜力 为临床辅助阅读或放射学教学描述特定畸形的表型，以识别 类似的案例，以帮助患者咨询，并使能够检测人口亚结构在疾病。我们的团队 在从临床获取的数据源中提取有价值的知识方面拥有丰富的成功经验 以及传播用于此类研究的开放软件。拟议项目的预期结果是 最先进的临床辅助阅读开源试点系统。

Project Summary/Abstract TR&D 3 Image Features for Brain Phenotypes Modern brain imaging provides vastly more information than before. While this information is of tremendous benefit to patients, the radiologist’s workload has increased with higher resolutions and increasing numbers of images to interpret. Furthermore, rare brain malformations, rare diseases, and brain abnormalities that are diffusely manifested or are symmetric across hemispheres can pose particular challenges to the radiologist. Despite the rich information content of hospital image archives, there is no clinically available way to automatically leverage the large volume of prior cases at the same hospital to aid radiological reads. Toward the long-term goal of developing machine learning systems for clinically assisted reads, the Image Features for Brain Phenotypes TR&D will investigate 3D brain image features for description of healthy versus non-healthy brain phenotypes. Image features are representations of image contents that have the following advantages: they are compact, informative, facilitate fast search, and enable machine learning analyses of large datasets. Even in an era of massively parallel computers, these advantages enable clinical problem solving that would otherwise be infeasible in practice due to problem size and complexity. We hypothesize that similarity in image features is strongly correlated with similarity in underlying disease, and that this can be used to build novel tools to support radiological decisions. The project is organized into three specific aims: (1) Robust image features for brain phenotypes that will develop features and associated algorithms for 3D structural images, (2) Connectivity-based brain phenotypes that will investigate novel features for diffusion MRI images, and (3) Feature-based brain phenotypes for the clinic that will deploy and validate methods in clinical MRI databases. We will focus on the specific application of radiological images of children 4 to 18 years old and we will leverage three large clinical MRI databases from Boston hospitals. Overall, our proposed feature-based image analysis technologies have the potential to identify image phenotypes describing particular malformations for clinically aided reads or radiological teaching, to identify similar cases to aid patient counseling, and to enable detection of population substructure in disease. Our team has significant successful experience in extracting valuable knowledge from clinically acquired data sources and in disseminating open software for such research. The expected outcome of the proposed project is a state-of-the-art open source pilot system for clinically aided reads.