Evaluation of state-of-the-art hardware architectures for fast cone-beam CT reconstruction

  • Authors:
  • Holger Scherl;Markus Kowarschik;Hannes G. Hofmann;Benjamin Keck;Joachim Hornegger

  • Affiliations:
  • Siemens AG, Healthcare Sector, CV Division, Medical Electronics and Imaging Solutions, Mozartstr. 57, 91052 Erlangen, Germany;Siemens AG, Healthcare Sector, Angiography and Interventional X-Ray Systems, Siemensstr. 1, 91301 Forchheim, Germany;Friedrich-Alexander-University Erlangen-Nuremberg, Department of Computer Science, Pattern Recognition Lab (LME), Martensstr. 3, 91058 Erlangen, Germany;Friedrich-Alexander-University Erlangen-Nuremberg, Department of Computer Science, Pattern Recognition Lab (LME), Martensstr. 3, 91058 Erlangen, Germany;Friedrich-Alexander-University Erlangen-Nuremberg, Department of Computer Science, Pattern Recognition Lab (LME), Martensstr. 3, 91058 Erlangen, Germany

  • Venue:
  • Parallel Computing
  • Year:
  • 2012

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Abstract

We present an evaluation of state-of-the-art computer hardware architectures for implementing the FDK method, which solves the 3-D image reconstruction task in cone-beam computed tomography (CT). The computational complexity of the FDK method prohibits its use for many clinical applications unless appropriate hardware acceleration is employed. Today's most powerful hardware architectures for high-performance computing applications are based on standard multi-core processors, off-the-shelf graphics boards, the Cell Broadband Engine Architecture (CBEA), or customized accelerator platforms (e.g., FPGA-based computer components). For each hardware platform under consideration, we describe a thoroughly optimized implementation of the most time-consuming parts of the FDK algorithm; the filtering step as well as the subsequent back-projection step. We further explain the required code transformations to parallelize the algorithm for the respective target architecture. We compare both the implementation complexity and the resulting performance of all architectures under consideration using the same two medical datasets which have been acquired using a standard C-arm device. Our optimized back-projection implementations achieve at least a speedup of 6.5 (CBEA, two processors), 22.0 (GPU, single board), and 35.8 (FPGA, 9 chips) compared to a standard workstation equipped with a quad-core processor.