ACM Gordon Bell Finalists
Computed Tomography (CT) image reconstruction is an important technique used in a wide range of applications. Among reconstruction methods, Model-Based Iterative Reconstruction (MBIR) generally produces higher quality images. However, the irregular data access pattern, the difficulty of effective parallelization and slow algorithmic convergence have made MBIR impractical for many applications. This paper presents a new algorithm for MBIR, Non-Uniform Parallel Super-Voxel (NU-PSV), that regularizes the data access pattern, enables massive parallelism and ensures fast convergence. We compare the NU-PSV algorithm with two state-of-the-art implementations on a 69632-core distributed system. Results indicate that the NU-PSV algorithm has an average speedup of 1665 compared to the fastest state-of-the-art implementations.
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Xiao Wang received B.A. degrees in Mathematics and Computer Science with honor from Saint John’s University, MN, in 2012, and M.S. degree in electrical and compute engineering from Purdue University, West Lafayette, IN, in 2016. In 2017, He received a PhD degree in electrical and computer engineering from Purdue University, under the supervision of Professor Charles Bouman and Samuel Midkiff. Currently, he works as a postdoctoral research fellow both at Harvard Medical School and Boston Children's Hospital.
Xiao Wang’s research work focuses on applying high performance computing to imaging problems, especially image processing on CT and MRI. He and his advisors are selected to be the 2017 ACM Gordon Bell Prize finalist for their research work on CT image reconstructions.
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Researchers should cite this work as follows:
Xiao Wang, Amit Sabne, Putt Sakdhnagool, Sherman J. Kisner, Charles A. Bouman, and Samuel P.Midkiff, Massively Parallel 3D Image Reconstruction, Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC '17, Denver, Colorado, (2017), doi: 10.1145/3126908.3126911
SC17, Colorado Convention Center, Denver, CO