No abstract

Medical Image Computing researchers often face the problem of moving promising new algorithms from the proof of concept stage into a form compatible with clinical use. Algorithm developers lack the time and resources to engineer their code for robustness and compatibility, while end-users are anxious to try new techniques but require well designed and tested user interfaces to make practical use of them. The NA-MIC Kit is a collection of software and methodology specifically designed to address these problems and facilitate the rapid advancement of the field.

The aggregation of imaging, clinical, and behavioral data from multiple independent institutions and researchers presents both a great opportunity for biomedical research as well as a formidable challenge. Many research groups have well-established data collection and analysis procedures, as well as data and metadata format requirements that are particular to that group. Moreover, the types of data and metadata collected are quite diverse, including image, physiological, and behavioral data, as well as descriptions of experimental design, and preprocessing and analysis methods. Each of these types of data utilizes a variety of software tools for collection, storage, and processing. Furthermore sites are reluctant to release control over the distribution and access to the data and the tools. To address these needs, the Biomedical Informatics Research Network (BIRN) has developed a federated and distributed infrastructure for the storage, retrieval, analysis, and documentation of biomedical imaging data. The infrastructure consists of distributed data collections hosted on dedicated storage and computational resources located at each participating site, a federated data management system and data integration environment, an Extensible Markup Language (XML) schema for data exchange, and analysis pipelines, designed to leverage both the distributed data management environment and the available grid computing resources.

MR imaging at 3.0- and 4.0-T yielded higher reproducibility across sites and significantly better results than 1.5-T imaging. The effects of subject, k-space, and field strength on examination reproducibility were significant.

Background Diffusion Tensor Imaging (DTI) infers the trajectory and location of large white matter tracts by measuring anisotropic diffusion of water. DTI data may then be analyzed and presented as tractography for visualization of the tracts in three dimensions. Despite the important information contained in tractography images, usefulness for neurosurgical planning has been limited by the inability to define which are critical structures within the mass of demonstrated fibers and to clarify their relationship to the tumor. Objective Our goal was to develop a method to allow the interactive querying of tractography datasets for surgical planning and to provide a working software package for the research community. Methods Tool was implemented within open-source software project. Echoplanar DTI at 3T was performed on five patients, followed by tensor calculation. Technical Development Software was developed allowing placement of dynamic seedpoint for local selection of fibers, and for fiber display around a segmented structure - both with tunable parameters. A neurosurgeon was trained in use of software in less than one hour and used to review cases. Results Tracts near tumor and critical structures were interactively visualized in three dimensions to determine spatial relationships to lesion. Tracts were selected using 3 methods: (1) anatomical and fMRI-defined regions of interest (ROIs), (2) distance from the segmented tumor volume, (3) dynamic seedpoint spheres. Conclusion Interactive tractography successfully enabled inspection of white matter structures that were in proximity to lesions, critical structures, and functional cortical areas allowing the surgeon to explore the relationships between them.