Image reconstruction for ultrasound computed tomography by use of the regularized dual averaging method

Thomas P. Matthews, Kun Wang, Cuiping Li, Neb Duric, Mark A. Anastasio

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Waveform inversion methods can produce high-resolution reconstructed sound speed images for ultrasound computed tomography; however, they are very computational expensive. Source encoding methods can reduce this computational cost by formulating the image reconstruction problem as a stochastic optimization problem. Here, we solve this optimization problem by the regularized dual averaging method instead of the more commonly used stochastic gradient descent. This new optimization method allows use of non-smooth regularization functions and treats the stochastic data fidelity term in the objective function separately from the deterministic regularization function. This allows noise to be mitigated more effectively. The method further exhibits lower variance in the estimated sound speed distributions across iterations when line search methods are employed.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2017
Subtitle of host publicationUltrasonic Imaging and Tomography
EditorsNeb Duric, Neb Duric, Brecht Heyde
ISBN (Electronic)9781510607231
StatePublished - 2017
Externally publishedYes
EventMedical Imaging 2017: Ultrasonic Imaging and Tomography - Orlando, United States
Duration: Feb 15 2017Feb 16 2017

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


OtherMedical Imaging 2017: Ultrasonic Imaging and Tomography
Country/TerritoryUnited States


  • image reconstruction
  • stochastic optimization
  • ultrasound tomography
  • waveform inversion

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging


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