MULTIMODALITY RESEARCH IMAGE PROCESSING SYSTEM

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This project is to develop an extensible image processing system (MRIPS) to study multidimensional (2D to 10D) data from multiple imaging modalities. The system is based on a common hardware and software environment across NIH and is to be the standard for macroscopic image processing at NIH. The primary use will be the visualization and analysis of medical images. The system consists of data/computation servers, workstations, off-the-shelf system software, and customized image processing software suitable for both the development of new image visualization and analysis tools as well as the use of existing image processing software packages. This system will be used by trainees associated with the DRRP and other NIH scientist for the analysis of medical images obtained by computerized tomography (CT), magnetic resonance imaging (MRI), magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS), positron emission tomography (PET), single photon emission tomography (SPECT), echo, etc. Many of the studies will involve determination of the relationship between anatomic and physiologic image data obtained from various tissue and organ systems. Of particular importance in this regard is the need to accurately and efficiently obtain spatial registration and segmentation of data collected with these different modalities. Therefore, central to the design of MRIPS is the creation of an image registry for short-term storage of images from all supported modalities to facilitate selection of data from multiple modalities. The hardware environment of the MRIPS is one of network-connected workstations and file servers. The servers will provide access to data through importation from either tape- or network-based scanners at NIH. The workstations and servers will use the Network File System or the Andrew File System to provide a homogeneous file system to all users. MRIPS's software framework is general purpose in which most of the major 2D and 3D medical imaging applications may be handled for the near future. This framework can be easily tailored to the specific acquisition and visualization medium to facilitate data exchange, storage, retrieval and multimodality comparisons. In contrast to dissimilar systems now in use at NIH, this common system will also promote shared development, testing and exchange of new algorithms.