A Hybrid Architecture for Compressive Sensing 3-D CT Reconstruction

作者：Chen Jianwen^*; Cong Jason; Vese Luminita A; Villasenor John; Yan Ming; Zou Yi

来源：IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2012, 2(3): 616-625.

DOI：10.1109/JETCAS.2012.2221530

• Compressive sensing
• computerized tomography (CT) image reconstruction
• expectation maximization (EM)
• field-programmable gate array (FPGA)
• graphics processing unit (GPU)
• iterative reconstruction
• total variation (TV)

The radiation dose associated with computerized tomography (CT) is significant. Compressive sensing (CS) methods provide mathematical approaches to reduce the radiation exposure without sacrificing reconstructed image quality. However, the computational requirements of these algorithms is much higher than conventional image reconstruction approaches such as filtered back projection (FBP). This paper describes a new compressive sensing 3-D image reconstruction algorithm based on expectation maximization and total variation, termed EM+TV, and also introduces a promising hybrid architecture implementation for this algorithm involving the combination of a CPU, GPU, and FPGA. An FPGA is used to speed up the major computation kernel (EM), and a GPU is used to accelerate the TV operations. The performance results indicate that this approach provides lower energy consumption and better reconstruction quality, and illustrates an example of the advantages that can be realized through domain-specific computing.

• 出版日期2012-9