Distributed MLEM: An Iterative Tomographic Image Reconstruction Algorithm for Distributed Memory Architectures

The processing speed for positron emission tomography (PET) image reconstruction has been greatly improved in recent years by simply dividing the workload to multiple processors of a graphics processing unit (GPU). However, if this strategy is generalized to a multi-GPU cluster, the processing speed...

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Bibliographic Details
Published in:IEEE transactions on medical imaging Vol. 32; no. 5; pp. 957 - 967
Main Authors: Jingyu Cui, Pratx, G., Bowen Meng, Levin, C. S.
Format: Journal Article
Language:English
Published: United States IEEE 01.05.2013
Subjects:
Algorithms
Clustering algorithms
Compute unified device architecture (CUDA)
Computer Graphics
Computer Simulation
Equations
graphics processing unit (GPU)
Graphics processing units
high performance computing
Image Processing, Computer-Assisted - methods
Image reconstruction
Linear programming
list-mode
Mathematical model
maximum likelihood expectation maximization (MLEM)
parallel computing
Phantoms, Imaging
Positron emission tomography
positron emission tomography (PET) image reconstruction
Positron-Emission Tomography - methods
ISSN:0278-0062, 1558-254X, 1558-254X
Online Access:Get full text
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Summary:The processing speed for positron emission tomography (PET) image reconstruction has been greatly improved in recent years by simply dividing the workload to multiple processors of a graphics processing unit (GPU). However, if this strategy is generalized to a multi-GPU cluster, the processing speed does not improve linearly with the number of GPUs. This is because large data transfer is required between the GPUs after each iteration, effectively reducing the parallelism. This paper proposes a novel approach to reformulate the maximum likelihood expectation maximization (MLEM) algorithm so that it can scale up to many GPU nodes with less frequent inter-node communication. While being mathematically different, the new algorithm maximizes the same convex likelihood function as MLEM, thus converges to the same solution. Experiments on a multi-GPU cluster demonstrate the effectiveness of the proposed approach.
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ISSN:0278-0062
1558-254X
1558-254X
DOI:10.1109/TMI.2013.2252913