source: © 2011 Medical Physics
Purpose:
Optoacoustic imaging is an emerging noninvasive imaging modality that can resolve optical contrast through several millimeters to centimeters of tissue with diffractionâlimited resolution of ultrasound. Yet, quantified reconstruction of tissue absorption maps requires optoacoustic signals to be collected from as many locations around the object as possible. In many tomographic imaging scenarios, however, only limitedâview or partial projection data are available, which has been shown to generate image artifacts and overall loss of quantification accuracy.
Methods:
In this article, the recently introduced interpolatedâmatrixâmodel optoacoustic inversion method is tested in different limitedâview scenarios and compared to the standard backprojection algorithm. Both direct (TGSVD) and iterative (PLSQR) regularization methods are proposed to improve the accuracy of image reconstructions with their performance tested on simulated and experimental data.
Fig.1 (a) Reconstructable (bright solid line) and âinvisibleâ (bright dashed line) boundaries of a round object with a square insertion partially lying in the âvisibility regionâ (shaded area) and the âinvisibility domainâ for a detector moving along the dark solid arc. Dashed boundaries blur away since they do not fulfill detection criterion, i.e., they do not have a normal passing at least through one detector position. (b) Backprojection reconstruction of the phantom shown in (a). Boundaries that do not fulfill the detection criterion blur away. (c) The condition number of the matrix urn:x-wiley:00942405:mp6916:equation:mp6916-math-0138 as a function of the detection arc. The higher the condition number, the more errorâprone the inversion of urn:x-wiley:00942405:mp6916:equation:mp6916-math-0139 becomes. (d) IMMI reconstruction of the phantom. IMMI reconstructions show stripe artifacts in the invisibility domain.
Results:
While for fullâview tomographic data the modelâbased inversion has been generally shown to attain higher reconstruction accuracy compared to backprojection algorithms, the incomplete tomographic datasets lead to illâconditioned forward matrices and, consequently, to errorâprone inversions, with strong artifacts following a distinct rippleâtype pattern. The proposed regularization techniques are shown to stabilize the inversion and eliminate the artifacts.
Conclusions:
Overall, it has been determined that the regularized interpolatedâmatrixâmodelâbased optoacoustic inversions show higher accuracy than reconstructions with the standard backprojection algorithm. Finally, the combination of modelâbased inversion with PLSQR or TGSVD regularization methods can lead to an accurate reconstruction of limitedâprojection angle optoacoustic data and practical systems for optoacoustic imaging in many realistic cases where the fullâview dataset is unavailable.Â
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