Investigation of limited-view image reconstruction in optoacoustic tomography employing a priori structural information

Chao Huang, Alexander A. Oraevsky, Mark A. Anastasio

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


Optoacoustic tomography (OAT) is an emerging ultrasound-mediated biophotonic imaging modality that has exciting potential for many biomedical imaging applications. There is great interest in conducting B-mode ultrasound and OAT imaging studies for breast cancer detection using a common transducer. In this situation, the range of tomographic view angles is limited, which can result in distortions in the reconstructed OAT image if conventional reconstruction algorithms are applied to limited-view measurement data. In this work, we investigate an image reconstruction method that utilizes information regarding target boundaries to improve the quality of the reconstructed OAT images. This is accomplished by developing boundary-constrained image reconstruction algorithm for OAT based on Bayesian image reconstruction theory. The computer-simulation studies demonstrate that the Bayesian approach can effectively reduce the artifact and noise levels and preserve the edges in reconstructed limited-view OAT images as compared to those produced by a conventional OAT reconstruction algorithm.

Original languageEnglish (US)
Title of host publicationImage Reconstruction from Incomplete Data VI
StatePublished - 2010
Externally publishedYes
EventImage Reconstruction from Incomplete Data VI - San Diego, CA, United States
Duration: Aug 2 2010Aug 3 2010

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X


ConferenceImage Reconstruction from Incomplete Data VI
Country/TerritoryUnited States
CitySan Diego, CA


  • Bayesian image reconstruction
  • limited-view tomography
  • optoacoustic tomography
  • photoacoustic tomography

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


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