Framework for multi-feature registration of medical images

数百万美元的政府和工业支持已经被用来从数以万计的受试者的活脑中收集医学图像数据，目的是促进我们对人脑在健康和疾病中的理解。计算技术和计算机科学的最新进展使分析如此海量的数据成为可能。例如，脑分析可包括研究正常发育或异常发育中的纵向生长轨迹；研究衰老以帮助了解疾病进展；以及为诊断目的识别成像生物标记物。大多数对大脑数据的分析依赖于一个配准步骤，在这个步骤中，所有的大脑都对齐到一个标准的坐标系中，这样x，y，z坐标就可以处理所有大脑中相同的解剖位置。然而，即使在超级计算机或集群上实现，对代表数千个对象的TB级数据的分析也可能很耗时。这一限制限制了此类数据的交互分析。需要新的工具来调整这些数据集，以便从收集的TB级数据中提取有用的信息。

Millions of dollars of government and industrial support have been used to collect medical image data from the living brains of tens of thousands of subjects with the goal to advance our understanding of the human brain in health and in disease. Recent advances in computational technology and computer science made it possible to analyze such enormous quantities of data. Examples brain analysis can include the study of longitudinal growth trajectories in normal development or in abnormal development; the study of ageing to help understand disease progression; and the identification of imaging biomarkers for diagnostic purposes. Most analyses of brain data relies on a registration step where all brain are aligned into a standard coordinate system, so that an x,y,z coordinate addresses the same anatomical location in all brains. However, analysis of the terabytes of data representing thousands of subjects can be time consuming even when implemented on a supercomputer or cluster. This constraint limits the interactive analysis of such data. New tools are needed to align these datasets so that useful information can be extracted from the terabytes of data collected.