The proliferation and increased reliance on high-resolution, multimodality biomedical images/videos present significant challenges for a broad spectrum of clinical practitioners and investigators throughout the clinical and research communities. A growing spectrum of new and improved imaging modalities and new image processing techniques can significantly affect diagnostic and prognostic accuracy and facilitates progress in the areas of biomedical research and discovery. However, the impact of these new technologies in both time-critical clinical applications and high-throughput research pursuits depends, in large part, on the speed and reliability with which the imaging data can be visualized, analyzed and interpreted. Conventional serial computation is grossly inadequate and inefficient for managing these increasing amounts of data and the employment of the new advances in medical imaging is often limited by insufficient compute and storage resources.
High-performance computing (HPC) and distributed computing infrastructures (DCI) ranging from multi-core CPUs and GPU-based processing to parallel machines, Grids and Clouds are effective mechanisms for overcoming such limitations. They allow for significant reduction of computational time, running large experiments campaigns, and speed-up the development time for new algorithms while increasing the availability of new methods for the research community, and supporting large-scale multi-centric collaborations.
The workshop will build on existing collaborative efforts in understanding current trends in HPC/DCI medical imaging research. It will demonstrate and encourage open discussion regarding the current status and latest developments in the field; explore new ideas/motifs, identify the challenges which currently impeded wider adoption of these technologies in image-assisted translational research, clinical intervention and decision-making, and present innovative solutions to the challenges.
The workshop addresses researchers who are already employing HPC/DCI techniques, as well as scientists who are developing large-scale imaging applications including multi-data studies, large-scale parameter scans, integrative and comparative analyses, or complex analysis pipelines.