Scalable FBP decomposition for cone-beam CT reconstruction

Conference · 01 November 2021

DOI:https://doi.org/10.1145/3458817.3476139· OSTI ID:1862116

Chen, Peng ^[1]; Biguri, Ander ^[2]; Wang, Xiao ^[3]; Boardman, Richard ^[4]; Blumensath, Thomas ^[5]; Matsuoka, Satoshi ^[6]; Wahib, Mohamed ^[1]; Hirofuchi, Takahiro ^[1]; Ogawa, Hirotaka ^[1]

---

1. AIST, Japan
2. University College London (UCL), UK
3. ORNL
4. University of Southhampton
5. University of Southampton, UK
6. RIKEN Laboratory

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).

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1862116
• Country of Publication: United States
• Language: English