Title :
Statistical Approach for Optoacoustic Image Reconstruction in the Presence of Strong Acoustic Heterogeneities
Author :
Déan-Ben, X. Luís ; Ma, Rui ; Razansky, Daniel ; Ntziachristos, Vasilis
Author_Institution :
Inst. for Biol. & Med. Imaging, Tech. Univ. of Munich, Neuherberg, Germany
Abstract :
A method is presented to reduce artefacts produced in optoacoustic tomography images due to internal reflection or scattering of the acoustic waves. It is based on weighting the tomographic contribution of each detector with the probability that a signal affected by acoustic mismatches is measured at that position. The correction method does not require a priori knowledge of the acoustic or optical properties of the imaged sample. Performance tests were made with agar phantoms that included air gaps for mimicking strong acoustic reflections as well as with an acoustically heterogeneous adult Zebrafish. The results obtained with the method proposed show a clear reduction of the artefacts with respect to the original images reconstructed with filtered back-projection algorithm. This performance is directly related to in vivo small animal imaging applications involving imaging in the presence of bones, lungs, and other highly mismatched organs.
Keywords :
acoustic signal processing; acoustic wave reflection; acoustic wave scattering; biomedical optical imaging; image reconstruction; medical image processing; optical tomography; phantoms; photoacoustic effect; statistical analysis; acoustic mismatches; acoustic wave internal reflection; acoustic wave scattering; acoustically heterogeneous adult Zebrafish; agar phantoms; correction method; filtered back projection algorithm; optoacoustic image reconstruction; optoacoustic tomography artefact reduction; statistical approach; strong acoustic heterogeneities; tomographic contribution weighting; Absorption; Acoustics; Image reconstruction; Optical imaging; Optical scattering; Phantoms; Transducers; Acoustic mismatch; back-projection algorithm; optoacoustic tomography; statistical correction; Algorithms; Animals; Artifacts; Image Processing, Computer-Assisted; Models, Statistical; Phantoms, Imaging; Tomography; Zebrafish;
Journal_Title :
Medical Imaging, IEEE Transactions on
DOI :
10.1109/TMI.2010.2081683