Efficient framework for optoacoustic image reconstruction using wavelet packets

The use of model-based algorithms in optoacoustic tomography
offers many advantages over analytical inversion methods. However,
the relatively high computational complexity of the model-based
approaches often restricts their efficient implementation. In practice, a
very large number of pixels/voxels is required for image reconstruction.
Consequently, size of the forward-model matrix hinders the use of many
inversion algorithms, often making model-based approaches impractical.
In this work, we present a new framework for model-based optoacoustic
tomographic reconstructions, which is based on a wavelet-packet
representation of the imaged object and the acquired projection data.
The frequency localization property of the wavelet-packet base leads
to an approximately separable model matrix, for which reconstruction
Conference 8581:
Photons Plus Ultrasound: Imaging and Sensing 2013
264A Return to Contents
at each spatial frequency band is independent and requires only a
fraction of the projection data. Thus, the large model matrix is effectively
separated into a set of smaller matrices, facilitating the use of inversion
schemes whose complexity is highly nonlinear with respect to matrix
size. The performance of the new methodology is demonstrated for the
case of two-dimensional optoacoustic tomography for both numerically
generated and experimental data. In the case of partial-tomographic
data, the wavelet-packet framework enabled employing regularization
algorithms such as singular value decomposition, which were too
resources consuming to be used on the total model matrix. Thus, the
new framework enables extending the application of model-based
techniques to image sizes which have in the past been prohibitive for this
approach.