This subproject is one of many research subprojects utilizing theresources provided by a Center grant funded by NIH/NCRR. The subproject andinvestigator (PI) may have received primary funding from another NIH source,and thus could be represented in other CRISP entries. The institution listed isfor the Center, which is not necessarily the institution for the investigator.The Insight Toolkit (ITK) has become the de facto standard platform for advancedsegmentation and registration research at many laboratories. At the same time, there isan increasing trend to deploy grid-computing infrastructures to support computations on extremely large data sets like those associated with the Visible Human Project. Thearchitecture of ITK is not designed to support such efforts. Therefore, the research in thisSupplement was intended to revisit and refine critical aspects of the architecture of ITKto support the emerging standards in the grid-computing community and to developexample applications to demonstrate the power of the ITK/grid combination in real-worldresearch computing scenarios.We identified two driving applications to provide real-world examples of the biomedicalcomputing demands and constraints for this architecture engineering effort: namely,image-guided neurosurgery and image-guided radiofrequency ablation (RFA). Bothapplications rely heavily on computations that occur during interventional procedures,with hard real-time constraints. We are designing, implementing and evaluatingmodifications to the Insight Toolkit architecture and platform to better leverage gridcomputing in these scenarios. So far, we have achieved a 400% increase in speed inkey automated registration algorithms and have made significant progress in improvingour capacity to exploit CPU-specific code and instruction set optimizations.
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