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Publication

Scalable FBP decomposition for cone-beam CT reconstruction

Publication Type
Conference Paper
Journal Name
Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis
Book Title
SC '21: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis
Publication Date
Page Numbers
1 to 16
Issue
1
Publisher Location
New York, New York, United States of America
Conference Name
International Conference for High Performance Computing, Networking, Storage and Analysis (SC '21)
Conference Location
St. Louis, Missouri, United States of America
Conference Sponsor
ACM IEEE
Conference Date
-

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 40963 volumes, for real-world datasets, in under 16 seconds (including I/O).