SC22 Proceedings

The International Conference for High Performance Computing, Networking, Storage, and Analysis

Workshops Archive

Lightning Talk: Image-Guided Adaptive Radiation Therapy (IGART) Based on Massive Parallelism and Real-Time Scheduling

Workshop: First Combined International Workshop on Interactive Urgent Supercomputing

Authors: Vahdaneh Kiani (University of Heidelberg)

Abstract: Modern high precision radiation therapy (RT) applications require a rapid and accurate planning process. Since the anatomical changes during treatment are mostly deformable, deformable image registration (DIR) is a core process used during treatment to account for those changes in the shape and size of internal organs between the initial and adaptive planning images acquired during the treatment course. DIR methods have already obtained huge success on registration accuracy, however, they usually take a long computation time and this limits clinical applications. So, the research question (RQ) of this project is: performing an accurate deformable image registration requires a tremendous amount of computing time, how to obtain significant acceleration while maintaining registration accuracy?

Different DIR algorithms will behave differently; therefore, users need to be aware of specifics of their software before clinical use. In our project, we are evaluating a multi-GPU-based DIR framework capabilities for radiotherapy treatments, using lung data sets. It is called CLAIRE. CLAIRE aims at solving the large-scale imaging problems, while we want to provide real-time capabilities for clinically relevant problem sizes. We believe that CLAIRE can be benefited from a series of performance optimizations to improve strong scaling scenarios, since the scalability of CLAIRE is limited due to the high communication costs for small problem sizes.


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