Amir Rosenthal, Daniel Razansky, Vasilis Ntziachristos

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


Optoacoustic tomography is a powerful hybrid bioimaging method which retains rich optical contrast and diffraction-limited ultrasonic resolution at depths of varying from millimeters to several centimeters in biological tissue irrespective of photon scattering. Optoacoustic imaging is commonly performed with high power optical pulses whose absorption leads to instantaneous temperature increase, thermal expansion and, subsequently, to the generation of a pressure field proportional to the distribution of the absorbed energy. For tomographic data acquisition, the optoacoustically generated waves are detected on a surface surrounding the imaged region. Recovery of the initially generated pressure distribution from the detected tomographic projections, and hence of the optical energy deposition in the tissue, constitutes the inverse problem of optoacoustic tomography, which is often solved using closed-form inversion formulae. However, those closed-form solutions are only exact for ideal detection geometries, which often do not accurately represent the experimental conditions. Model-based image-reconstruction techniques represent an alternative approach to solving the inverse problem that can significantly reduce image artifacts associated with approximated analytical formulae and significantly enhance image quality in non-ideal imaging scenarios. In the model-based reconstruction, a linear forward model is constructed to accurately describe the experimental conditions of the imaging setup. Inversion is performed numerically and may include regularization when the projection data is insufficient. This chapter demonstrates the benefits of the model-based reconstruction approach and describes numerically efficient methods for its implementation.
Original languageEnglish
Title of host publicationComputer Vision in Medical Imaging
PublisherWorld Scientific
Number of pages18
ISBN (Print)978-9814460934
StatePublished - 2013
Externally publishedYes

Publication series

NameSeries in Computer Vision


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