Characterization of sparse-array detection photoacoustic tomography using the singular value decomposition

G. Chaudhary, M. Roumeliotis, P. Ephrat, R. Stodilka, J. J.L. Carson, M. A. Anastasio

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


A photoacoustic tomography (PAT) method that employs a sparse two-dimentional (2D) array of detector elements has recently been employed to reconstruct images of simple objects from highly incomplete measurement data. However, there remains an important need to understand what type of object features can be reliably reconstructed from such a system. In this work, we numerically compute the singular value decomposition (SVD) of different system matrices that are relevant to implementations of sparse-array PAT. For a given number and arrangement of measurement transducers, this will reveal the type of object features that can reliably be reconstructed as well as those that are invisible to the imaging system.

Original languageEnglish (US)
Title of host publicationPhotons Plus Ultrasound
Subtitle of host publicationImaging and Sensing 2010
StatePublished - May 3 2010
Externally publishedYes
EventPhotons Plus Ultrasound: Imaging and Sensing 2010 - San Francisco, CA, United States
Duration: Jan 24 2010Jan 26 2010

Publication series

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


ConferencePhotons Plus Ultrasound: Imaging and Sensing 2010
Country/TerritoryUnited States
CitySan Francisco, CA


  • LANCZOS algorithm
  • Photoacoustic tomography
  • Pseudo-inverse solution
  • Singular value decomposition

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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