Managing large scale data in medical visualization
The data sets produced by state-of-the-art medical imaging modalities can be extremely large, and there is a clear trend that they are rapidly increasing even further. Such data sets cannot be handled by traditional viewing systems. This project aims to develop new visualization and data management techniques that enables efficient human anlysis of large medical data sets in the clinical environment.
- Former Staff:
- Project Description:
Background: The population pyramid and increased life span in the western world will lead to greatly increased need of health care. The society needs to make health care much more efficient than today to cope with this financial burdon. The field of medical imaging is currently facing a great challenge, the data explosion. During the 20th century, the focus was on retrieving as much medical image data as possible. Today, the task is to navigate within an abundance of data, to pinpoint and highlight the diagnostically important features while discarding irrelevant data. An example examination that soon will be a standard procedure is a volume of a beating heart. A few seconds of such a sequence can easily amount to 1 TeraByte of data! The technical development is not rapid enough to match the constantly increasing data set sizes, there is, and will be, severe bottlenecks that need to be handled.
Objective: This project aims to find new and combine known techniques in an innovative visualization pipeline for medical imaging. This system should account for the limitations and opportunities provided by the clinical environment. One part of the project will develop data management schemes for efficient data prioritization and reduction, in order to handle the issue of technical bottlenecks. Another part is to address the human limitations concerning these data set sizes, to develop visualization techniques for clinical use that enables the physician to efficiently navigate to the interesting information.
Publications related to the project can be found at Claes Lundström's research publications.