7131

FPGA accelerated 3D reconstruction using compressive sensing

Jianwen Chen, Jason Cong, Ming Yan, Yi Zou
Computer Science Department, University of California, Los Angeles, Los Angeles, CA 90095, USA
20th ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA 2012), 2012

@inproceedings{chen2012fpga,

   title={FPGA accelerated 3D reconstruction using compressive sensing},

   author={Chen, J. and Cong, J. and Yan, M. and Zou, Y.},

   booktitle={Proc. FPGA},

   year={2012}

}

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The radiation dose associated with computerized tomography (CT) is significant. Optimization-based iterative reconstruction approaches, e.g., compressive sensing provide ways to reduce the radiation exposure, without sacrificing image quality. However, the computational requirement such algorithms is much higher than that of the conventional Filtered Back Projection (FBP) reconstruction algorithm. This paper describes an FPGA implementation of one important iterative kernel called EM, which is the major computation kernel of a recent EM+TV reconstruction algorithm. We show that a hybrid approach (CPU+GPU+FPGA) can deliver a better performance and energy efficiency than GPU-only solutions, providing 13X boost of throughput than a dual-core CPU implementation.
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