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Block stochastic gradient descent for large-scale tomographic reconstruction in a parallel network

Block stochastic gradient descent for large-scale tomographic reconstruction in a parallel network
Block stochastic gradient descent for large-scale tomographic reconstruction in a parallel network
Iterative algorithms have many advantages for linear tomographic image reconstruction when compared to back-projection based methods. However, iterative methods tend to have significantly higher computational complexity. To overcome this, parallel processing schemes that can utilise several computing nodes are desirable. Popular methods here are row action methods, which update the entire image simultaneously and column action methods, which require access to all measurements at each node. In large scale tomographic reconstruction with limited storage capacity of each node, data communication overheads between nodes becomes a significant performance limiting factor. To reduce this overhead, we proposed a row action method BSGD. The method is based on the stochastic gradient descent method but it does not update the entire image at each iteration, which reduces between node communication. To further increase convergence speeds, an importance sampling strategy is proposed. We compare BSGD to other existing stochastic methods and show its effectiveness and efficiency. Other properties of BSGD are also explored, including its ability to incorporate total variation (TV) regularization and automatic parameter tuning.
CT image reconstruction,, parallel computing, gradient descent, coordinate descent, ,linear inverse problems.
1045-9219
Gao, Yushan
3037efe6-c1b0-411e-9606-5cf901555d96
Biguri, Ander
738d1b66-9a99-446f-805d-032dd12445e3
Blumensath, Thomas
470d9055-0373-457e-bf80-4389f8ec4ead
Gao, Yushan
3037efe6-c1b0-411e-9606-5cf901555d96
Biguri, Ander
738d1b66-9a99-446f-805d-032dd12445e3
Blumensath, Thomas
470d9055-0373-457e-bf80-4389f8ec4ead

Gao, Yushan, Biguri, Ander and Blumensath, Thomas (2019) Block stochastic gradient descent for large-scale tomographic reconstruction in a parallel network. IEEE Transactions on Parallel and Distributed Systems. (Submitted)

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Abstract

Iterative algorithms have many advantages for linear tomographic image reconstruction when compared to back-projection based methods. However, iterative methods tend to have significantly higher computational complexity. To overcome this, parallel processing schemes that can utilise several computing nodes are desirable. Popular methods here are row action methods, which update the entire image simultaneously and column action methods, which require access to all measurements at each node. In large scale tomographic reconstruction with limited storage capacity of each node, data communication overheads between nodes becomes a significant performance limiting factor. To reduce this overhead, we proposed a row action method BSGD. The method is based on the stochastic gradient descent method but it does not update the entire image at each iteration, which reduces between node communication. To further increase convergence speeds, an importance sampling strategy is proposed. We compare BSGD to other existing stochastic methods and show its effectiveness and efficiency. Other properties of BSGD are also explored, including its ability to incorporate total variation (TV) regularization and automatic parameter tuning.

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Submitted date: 27 March 2019
Additional Information: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
Keywords: CT image reconstruction,, parallel computing, gradient descent, coordinate descent, ,linear inverse problems.

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Local EPrints ID: 429723
URI: https://eprints.soton.ac.uk/id/eprint/429723
ISSN: 1045-9219
PURE UUID: 4564fdea-bb5c-4395-ba84-32ae85e780df

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Date deposited: 04 Apr 2019 16:30
Last modified: 04 Apr 2019 16:30

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