Data structures for multimodality imaging: concepts and implementation

摘要

The integration of data coming from different imaging modalities is something to take into account, due to the importance it can have in the development of a fast and reliable diagnosis by the health staff. In the medical imaging field, computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and single photon emission computed tomography (SPECT) are examples of devices that generate 3-D data. Digital subtraction angiography (DSA) or ultrasound (US) output 2-D data, from which its possible to reconstruct 3-D data. An important fact is that 3-D space is common to all these devices and they are all capable of producing large amounts of data. Prior to display or even data integration, matching the various 3-D spaces has to be achieved with some specific technique, according to the anatomical region under examination. The augmented octree, an extension of the linear octree, is used for data integration; its properties can help to overcome some of the constraints that occur in medical imaging. To be fully accepted by the specialist, the display and manipulation of multimodality data must be interactive and done in real-time, or at least in `nearly' real- time. Parallel architectures seem to be a solution for some computation intensive applications, and so an implementation of the linear octree encoding process was developed on a 16 Transputer machine.