An Interactive, Multi-Modal Approach to Analysing High-Resolution Image Mass Spectrometry Data
VMV(2010)
Abstract
The output resolution of imaging mass spectrometers is increasing rapidly due to advances in engineering and the use of tiling. Imaging-MS data is often displayed as a total-ion-count (TIC) image; however, anatomical structures are not easily identifiable from TIC images. For this purpose, additional high-resolution images that originate from different imaging modalities, such as stained histological data, are preferred. These modalities are most useful when fused; i.e., when the corresponding images are spatially aligned with respect to each other. The viewing and analysis of such data is ideally performed in real-time and at the highest possible resolution, allowing users to interactively query the combination of all fused data at the highest detail. However, proper alignment between modalities and interactively presenting large volumes of data is as of yet a challenge. We present a system for the simultaneous viewing and analysis of high-resolution data from different imaging modalities. Fusion is provided in such a way that interaction in one modality can be mapped to different modalities. For example, anatomical structures can be identified from histological data and their spatial extent mapped to a corresponding region-of-interest in the image MS data, allowing the analysis of its chemical compounds. In turn, the MS data can be analysed and filtered, for example using multi-variate analysis such as PCA, and the result mapped back to structures in other modalities. Level-of-detail, region-of-interest and asynchronous data processing algorithms ensure that the system can be operated interactively at the highest resolution.
MoreTranslated text
Key words
mass spectrometry
AI Read Science
Must-Reading Tree
Example
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
Generate MRT to find the research sequence of this paper
Chat Paper
Summary is being generated by the instructions you defined