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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Vydáno v:IEEE transactions on medical imaging Ročník 32; číslo 5; s. 957 - 967
Hlavní autoři: Jingyu Cui, Pratx, G., Bowen Meng, Levin, C. S.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.05.2013
Témata:
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
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Shrnutí: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.
Bibliografie:ObjectType-Article-1
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ISSN:0278-0062
1558-254X
1558-254X
DOI:10.1109/TMI.2013.2252913