Analysis of Performance Evaluation of Parallel Katsevich Algorithm for 3-D CT Image Reconstruction

Ni, Jun; Deng, Junjun; Yu, Hengyong; He, Tao; Wang, Ge

doi:10.1109/imsccs.2006.38

Cited by 1 publication

(1 citation statement)

References 17 publications

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“…for 3D PET iterative reconstruction, Jones et al [2] succeeded to get an acceleration factor of about 30 with 32 processors. Ni et al [21] achieved an excellent acceleration factor of 300, when they parallelized the Katsevitch algorithm, an exact cone beam BP with 300 CPUs.…”

Section: B Acceleration Of Reconstructionmentioning

confidence: 99%

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

et al. 2009

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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, Pre-fetch 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 hinder 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 GPU (Graphic Processor Unit).

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Section: B Acceleration Of Reconstructionmentioning

confidence: 99%