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

Research output: Contribution to journalArticle

4 Citations (Scopus)


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 languageEnglish
Pages (from-to)1000-1010
Number of pages11
JournalIEEE Transactions on Medical Imaging
Issue number4
Early online date25 Dec 2017
Publication statusPublished - 1 Apr 2018


  • Emission tomography
  • L-BFGS-B
  • penalized reconstruction
  • preconditioning

ASJC Scopus subject areas

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

Cite this