SC21 Proceedings

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

Scalable FBP Decomposition for Cone-Beam CT Reconstruction


Authors: Peng Chen and Mohamed Wahib (National Institute of Advanced Industrial Science and Technology (AIST), RIKEN Center for Computational Science (R-CCS)); Xiao Wang (Oak Ridge National Laboratory (ORNL), Boston Children's Hospital); Takahiro Hirofuchi and Hirotaka Ogawa (National Institute of Advanced Industrial Science and Technology (AIST)); Ander Biguri (Institute of Nuclear Medicine, University College London); Richard Boardman and Thomas Blumensath (University of Southampton, 𝜇-VIS X-Ray Imaging Centre); and Satoshi Matsuoka (RIKEN Center for Computational Science (R-CCS), Tokyo Institute of Technology)

Abstract: Filtered Back-Projection (FBP) is a fundamental compute-intense algorithm used in tomographic image reconstruction. Cone-Beam Computed Tomography (CBCT) devices use a cone-shaped X-ray beam, in comparison to the parallel beam used in older CT generations. Distributed image reconstruction of cone-beam datasets typically relies on dividing batches of images into different nodes. This simple input decomposition, however, introduces limits on input/output sizes and scalability.

We propose a novel decomposition scheme and reconstruction algorithm for distributed FPB. This scheme enables arbitrarily large input/output sizes, eliminates the redundancy arising in the end-to-end pipeline and improves the scalability by replacing two communication collectives with only one segmented reduction. Finally, we implement the proposed decomposition scheme in a framework that is useful for all current-generation CT devices (7th gen). In our experiments using up to 1024 GPUs, our framework can construct 4096^3 volumes, for real-world datasets, in under 16 seconds (including I/O).



Presentation: file


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