Consensus Equilibrium Framework for Super-Resolution and Extreme-Scale CT Reconstruction

Authors: Xiao Wang (Harvard Medical School, Boston Children's Hospital), Venkatesh Sridhar (Purdue University), Zahra Ronaghi (Nvidia Corporation), Rollin Thomas (Lawrence Berkeley National Laboratory), Jack Deslippe (Lawrence Berkeley National Laboratory), Dilworth Parkinson (Lawrence Berkeley National Laboratory), Gregery T. Buzzard (Purdue University), Samuel P. Midkiff (Purdue University, Boston Children's Hospital), Charles A. Bouman (Purdue University), Simon K. Warfield (Harvard Medical School, Boston Children's Hospital)

Abstract: Computed tomography (CT) image reconstruction is a crucial technique for many imaging applications. Among various reconstruction methods, Model-Based Iterative Reconstruction (MBIR) enables super-resolution with superior image quality. MBIR, however, has a high memory requirement that limits the achievable image resolution, and the parallelization for MBIR suffers from limited scalability. In this paper, we propose Asynchronous Consensus MBIR (AC-MBIR) that uses Consensus Equilibrium (CE) to provide a super-resolution algorithm with a small memory footprint, low communication overhead and a high scalability. Super-resolution experiments show that AC-MBIR has a 6.8 times smaller memory footprint and 16 times more scalability, compared with the state-of-the-art MBIR implementation, and maintains a 100% strong scaling efficiency at 146880 cores. In addition, AC-MBIR achieves an average bandwidth of 3.5 petabytes per second at 587,520 cores.


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