3D photoacoustic imaging

Jeffrey J.L. Carson, Michael Roumeliotis, Govind Chaudhary, Robert Z. Stodilka, Mark A. Anastasio

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


Our group has concentrated on development of a 3D photoacoustic imaging system for biomedical imaging research. The technology employs a sparse parallel detection scheme and specialized reconstruction software to obtain 3D optical images using a single laser pulse. With the technology we have been able to capture 3D movies of translating point targets and rotating line targets. The current limitation of our 3D photoacoustic imaging approach is its inability ability to reconstruct complex objects in the field of view. This is primarily due to the relatively small number of projections used to reconstruct objects. However, in many photoacoustic imaging situations, only a few objects may be present in the field of view and these objects may have very high contrast compared to background. That is, the objects have sparse properties. Therefore, our work had two objectives: (i) to utilize mathematical tools to evaluate 3D photoacoustic imaging performance, and (ii) to test image reconstruction algorithms that prefer sparseness in the reconstructed images. Our approach was to utilize singular value decomposition techniques to study the imaging operator of the system and evaluate the complexity of objects that could potentially be reconstructed. We also compared the performance of two image reconstruction algorithms (algebraic reconstruction and l1-norm techniques) at reconstructing objects of increasing sparseness. We observed that for a 15-element detection scheme, the number of measureable singular vectors representative of the imaging operator was consistent with the demonstrated ability to reconstruct point and line targets in the field of view. We also observed that the l1-norm reconstruction technique, which is known to prefer sparseness in reconstructed images, was superior to the algebraic reconstruction technique. Based on these findings, we concluded (i) that singular value decomposition of the imaging operator provides valuable insight into the capabilities of a 3D photoacoustic imaging system, and (ii) that reconstruction algorithms which favor sparseness can significantly improve imaging performance. These methodologies should provide a means to optimize detector count and geometry for a multitude of 3D photoacoustic imaging applications.

Original languageEnglish (US)
Title of host publicationPhotonics North 2010
StatePublished - Nov 10 2010
Externally publishedYes
EventPhotonics North 2010 - Niagara Falls, ON, Canada
Duration: Jun 1 2010Jun 3 2010

Publication series

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


ConferencePhotonics North 2010
CityNiagara Falls, ON


  • 3D
  • aliasing
  • compressed sensing
  • crosstalk
  • iterative image reconstruction
  • Photoacoustic imaging
  • photoacoustic tomography
  • singular value decomposition

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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