High Speed 3D Tomography on CPU, GPU, and FPGA

Back-projection (BP) is a costly computational step in tomography image reconstruction such as positron emission tomography (PET). To reduce the computation time, this paper presents a pipelined, prefetch, and parallelized architecture for PET BP (3PA-PET). The key feature of this architecture is its original memory access strategy, masking the high latency of the external memory. Indeed, the pattern of the memory references to the data acquired hinders the processing unit. The memory access bottleneck is overcome by an efficient use of the intrinsic temporal and spatial locality of the BP algorithm. A loop reordering allows an efficient use of general purpose processor's caches, for software implementation, as well as the 3D predictive and adaptive cache (3D-AP cache), when considering hardware implementations. Parallel hardware pipelines are also efficient thanks to a hierarchical 3D-AP cache: each pipeline performs a memory reference in about one clock cycle to reach a computational throughput close to 100%. The 3PA-PET architecture is prototyped on a system on programmable chip (SoPC) to validate the system and to measure its expected performances. Time performances are compared with a desktop PC, a workstation, and a graphic processor unit (GPU).

[1]  Michael Knaup,et al.  Hyperfast parallel-beam and cone-beam backprojection using the cell general purpose hardware. , 2007, Medical physics.

[2]  Mark T Madsen,et al.  EMISSION TOMOGRAPHY: THE FUNDAMENTALS OF PET AND SPECT , 2005 .

[3]  A. Rahmim,et al.  Data Processing Methods for a High Throughput Brain Imaging PET Research Center , 2006, 2006 IEEE Nuclear Science Symposium Conference Record.

[4]  Iain Goddard,et al.  High-speed cone-beam reconstruction: an embedded systems approach , 2002, SPIE Medical Imaging.

[5]  Xiang Li,et al.  P2P-enhanced Distributed Computing in EM Medical Image Reconstruction , 2004, PDPTA.

[6]  Jun Ni,et al.  Analysis of Performance Evaluation of Parallel Katsevich Algorithm for 3-D CT Image Reconstruction , 2006, First International Multi-Symposiums on Computer and Computational Sciences (IMSCCS'06).

[7]  Stéphane Mancini,et al.  An IIR based 2D adaptive and predictive cache for image processing , 2004 .

[8]  R M Leahy,et al.  Internet2-based 3D PET image reconstruction using a PC cluster. , 2002, Physics in medicine and biology.

[9]  C. Tsoumpas,et al.  STIR: software for tomographic image reconstruction release 2 , 2012, 2006 IEEE Nuclear Science Symposium Conference Record.

[10]  J. Hornegger,et al.  Fast GPU-Based CT Reconstruction using the Common Unified Device Architecture (CUDA) , 2007, 2007 IEEE Nuclear Science Symposium Conference Record.

[11]  Michel Desvignes,et al.  Hardware/software 2D-3D backprojection on a SoPC platform , 2006, SAC.

[12]  Klaus Mueller,et al.  IOP PUBLISHING PHYSICS IN MEDICINE AND BIOLOGY , 2007 .

[13]  Jun Ni,et al.  A Heterogeneous Windows Cluster System for Medical Image Reconstruction , 2006, First International Multi-Symposiums on Computer and Computational Sciences (IMSCCS'06).

[14]  Rolf Clackdoyle,et al.  CHAPTER 20 – Analytic Image Reconstruction Methods , 2004 .

[15]  Eric L. Miller,et al.  Parallel-Beam Backprojection: An FPGA Implementation Optimized for Medical Imaging , 2002, FPGA '02.

[16]  Torsten Möller,et al.  Rapid emission tomography reconstruction , 2003, VG.

[17]  M. Schellmann,et al.  Parallelization and Runtime Prediction of the ListMode OSEM Algorithm for 3D PET Reconstruction , 2006, 2006 IEEE Nuclear Science Symposium Conference Record.

[18]  Rüdiger Westermann,et al.  A fast and high-quality cone beam reconstruction pipeline using the GPU , 2007, SPIE Medical Imaging.

[19]  F. Habte,et al.  Fully 3-D List-Mode OSEM Accelerated by Graphics Processing Units , 2006, 2006 IEEE Nuclear Science Symposium Conference Record.

[20]  Günter Lauritsch,et al.  On-the-fly-Reconstruction in Exact Cone-Beam CT using the Cell Broadband Engine Architecture , 2007 .

[21]  Dmitri Riabkov,et al.  Accelerated cone-beam backprojection using GPU-CPU hardware , 2022 .