Omni-modality medical image analysis and visualisation

“全方位”模式成像（OMI）一词被用来描述多种互补医学成像模式的整合。但是，目前缺乏一种适当的方法来吸收和最大限度地利用这些综合数据。该项目旨在为OMI数据分析和可视化提供一种新方法，通过从数据中重叠的解剖和病理结构中获得一种新的“相关性水平”，该数据将用于抑制多余的数据并突出显示最相关的数据，以最大限度地从OMI数据中获得信息。在此基础上，提出了OMI可视化技术，以实现对重叠数据的高效导航。

The term ‘Omni’-modality imaging (OMI) has been coined to describe the integration of multiple, complementary medical imaging modalities. However, there is currently a lack of an appropriate means to assimilate and derive maximum benefit from these integrated data. This project aims to provide a new approach to OMI data analysis and visualisation, by deriving a novel ‘level of relevance’ from the overlapping anatomical and pathological structures in the data which will be used to suppress superfluous data and highlight the most relevant data to maximise the information gained from the OMI data. Further, OMI visualisation is proposed to efficiently navigate through the overlapping data.