Image Features for Brain Phenotypes

项目摘要/摘要TR&D 3脑表型的图像特征 现代大脑成像提供了比以前更多的信息。虽然这些信息是巨大的 为了使患者受益，放射科医生的工作量随着更高的分辨率和越来越多的 图像解读此外，罕见的脑畸形，罕见的疾病和脑异常， 弥散性表现或对称于半球可能对放射科医师提出特殊的挑战。 尽管医院图像档案的信息内容丰富，但临床上还没有可用的方法来 自动利用同一医院的大量先前病例来辅助放射学读取。 为了实现为临床辅助读取开发机器学习系统的长期目标，Image 脑表型特征TR&D将研究3D脑图像特征，用于描述健康与 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：它们结构紧凑，信息丰富，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的时代，这些优势也能解决临床问题。 解决由于问题的大小和复杂性而在实践中不可行的问题。 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， 这可以用来建立新的工具来支持放射学决策。该项目分为三个部分 具体目标：（1）大脑表型的稳健图像特征，其将开发特征和相关的 3D结构图像的算法，（2）基于连接性的大脑表型，将研究新的 扩散MRI图像的特征，以及（3）将部署的诊所的基于脑的表型， 在临床MRI数据库中验证方法。我们将重点介绍放射性图像的具体应用， 我们将利用波士顿医院的三个大型临床MRI数据库。 总的来说，我们提出的基于特征的图像分析技术有潜力识别图像 描述特定畸形的表型，用于临床辅助读取或放射学教学，以识别 类似的情况下，以帮助病人咨询，并使检测人口结构的疾病。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具有重要的成功经验 并为此类研究传播开放软件。拟议项目的预期成果是 用于临床辅助读取的最先进的开源试点系统。

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.