Fast Quasi-Newton Algorithms for Penalized Reconstruction in Emission Tomography and Further Improvements via Preconditioning

Yu Jung Tsai; Alexandre Bousse; Matthias J. Ehrhardt; Charles W. Stearns; Sangtae Ahn; Brian F. Hutton; Simon Arridge; Kris Thielemans

doi:10.1109/TMI.2017.2786865

Fast Quasi-Newton Algorithms for Penalized Reconstruction in Emission Tomography and Further Improvements via Preconditioning

Yu Jung Tsai, Alexandre Bousse, Matthias J. Ehrhardt, Charles W. Stearns, Sangtae Ahn, Brian F. Hutton, Simon Arridge, Kris Thielemans

Institute for Mathematical Innovation (IMI)

Research output: Contribution to journal › Article › peer-review

18 Citations (SciVal)

Abstract

This paper reports on the feasibility of using a quasi-Newton optimization algorithm, limited-memory Broyden-Fletcher-Goldfarb-Shanno with boundary constraints (L-BFGS-B), for penalized image reconstruction problems in emission tomography (ET). For further acceleration, an additional preconditioning technique based on a diagonal approximation of the Hessian was introduced. The convergence rate of L-BFGS-B and the proposed preconditioned algorithm (L-BFGS-B-PC) was evaluated with simulated data with various factors, such as the noise level, penalty type, penalty strength and background level. Data of three ¹⁸F-FDG patient acquisitions were also reconstructed. Results showed that the proposed L-BFGS-B-PC outperforms L-BFGS-B in convergence rate for all simulated conditions and the patient data. Based on these results, L-BFGS-B-PC shows promise for clinical application.

Original language	English
Pages (from-to)	1000-1010
Number of pages	11
Journal	IEEE Transactions on Medical Imaging
Volume	37
Issue number	4
Early online date	25 Dec 2017
DOIs	https://doi.org/10.1109/TMI.2017.2786865
Publication status	Published - 1 Apr 2018

Funding

Manuscript received September 25, 2017; revised December 11, 2017; accepted December 18, 2017. Date of publication December 25, 2017; date of current version April 2, 2018. This work was supported in part by GE Healthcare and in part by the NIHR-funded UCLH Biomedical Research Centre. The work of M. J. Ehrhardt was supported in part by the Leverhulme Trust Project—Breaking the non-convexity barrier, EPSRC, under Grant EP/M00483X/1 and Grant EP/N014588/1, in part by the Cantab Capital Institute for the Mathematics of Information, and in part by CHiPS (Horizon 2020 RISE Project Grant). (Corresponding author: Yu-Jung Tsai.) Y.-J. Tsai, A. Bousse, and K. Thielemans are with the Institute of Nuclear Medicine, University College London, London NW1 2BU, U.K. (e-mail: [email protected]).

Keywords

Emission tomography
L-BFGS-B
penalized reconstruction
preconditioning

ASJC Scopus subject areas

Software
Radiological and Ultrasound Technology
Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/TMI.2017.2786865Licence: CC BY

Cite this

@article{593437aece3349078035b4cb185bbfbf,

title = "Fast Quasi-Newton Algorithms for Penalized Reconstruction in Emission Tomography and Further Improvements via Preconditioning",

abstract = "This paper reports on the feasibility of using a quasi-Newton optimization algorithm, limited-memory Broyden-Fletcher-Goldfarb-Shanno with boundary constraints (L-BFGS-B), for penalized image reconstruction problems in emission tomography (ET). For further acceleration, an additional preconditioning technique based on a diagonal approximation of the Hessian was introduced. The convergence rate of L-BFGS-B and the proposed preconditioned algorithm (L-BFGS-B-PC) was evaluated with simulated data with various factors, such as the noise level, penalty type, penalty strength and background level. Data of three 18F-FDG patient acquisitions were also reconstructed. Results showed that the proposed L-BFGS-B-PC outperforms L-BFGS-B in convergence rate for all simulated conditions and the patient data. Based on these results, L-BFGS-B-PC shows promise for clinical application.",

keywords = "Emission tomography, L-BFGS-B, penalized reconstruction, preconditioning",

author = "Tsai, {Yu Jung} and Alexandre Bousse and Ehrhardt, {Matthias J.} and Stearns, {Charles W.} and Sangtae Ahn and Hutton, {Brian F.} and Simon Arridge and Kris Thielemans",

year = "2018",

month = apr,

day = "1",

doi = "10.1109/TMI.2017.2786865",

language = "English",

volume = "37",

pages = "1000--1010",

journal = "IEEE Transactions on Medical Imaging",

issn = "0278-0062",

publisher = "IEEE",

number = "4",

}

TY - JOUR

T1 - Fast Quasi-Newton Algorithms for Penalized Reconstruction in Emission Tomography and Further Improvements via Preconditioning

AU - Tsai, Yu Jung

AU - Bousse, Alexandre

AU - Ehrhardt, Matthias J.

AU - Stearns, Charles W.

AU - Ahn, Sangtae

AU - Hutton, Brian F.

AU - Arridge, Simon

AU - Thielemans, Kris

PY - 2018/4/1

Y1 - 2018/4/1

N2 - This paper reports on the feasibility of using a quasi-Newton optimization algorithm, limited-memory Broyden-Fletcher-Goldfarb-Shanno with boundary constraints (L-BFGS-B), for penalized image reconstruction problems in emission tomography (ET). For further acceleration, an additional preconditioning technique based on a diagonal approximation of the Hessian was introduced. The convergence rate of L-BFGS-B and the proposed preconditioned algorithm (L-BFGS-B-PC) was evaluated with simulated data with various factors, such as the noise level, penalty type, penalty strength and background level. Data of three 18F-FDG patient acquisitions were also reconstructed. Results showed that the proposed L-BFGS-B-PC outperforms L-BFGS-B in convergence rate for all simulated conditions and the patient data. Based on these results, L-BFGS-B-PC shows promise for clinical application.

AB - This paper reports on the feasibility of using a quasi-Newton optimization algorithm, limited-memory Broyden-Fletcher-Goldfarb-Shanno with boundary constraints (L-BFGS-B), for penalized image reconstruction problems in emission tomography (ET). For further acceleration, an additional preconditioning technique based on a diagonal approximation of the Hessian was introduced. The convergence rate of L-BFGS-B and the proposed preconditioned algorithm (L-BFGS-B-PC) was evaluated with simulated data with various factors, such as the noise level, penalty type, penalty strength and background level. Data of three 18F-FDG patient acquisitions were also reconstructed. Results showed that the proposed L-BFGS-B-PC outperforms L-BFGS-B in convergence rate for all simulated conditions and the patient data. Based on these results, L-BFGS-B-PC shows promise for clinical application.

KW - Emission tomography

KW - L-BFGS-B

KW - penalized reconstruction

KW - preconditioning

UR - http://www.scopus.com/inward/record.url?scp=85039799233&partnerID=8YFLogxK

U2 - 10.1109/TMI.2017.2786865

DO - 10.1109/TMI.2017.2786865

M3 - Article

AN - SCOPUS:85039799233

SN - 0278-0062

VL - 37

SP - 1000

EP - 1010

JO - IEEE Transactions on Medical Imaging

JF - IEEE Transactions on Medical Imaging

IS - 4

ER -

Fast Quasi-Newton Algorithms for Penalized Reconstruction in Emission Tomography and Further Improvements via Preconditioning

Abstract

Funding

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this