Efficient framework for model-based tomographic image reconstruction using wavelet packets

IEEE Transactions on Medical Imaging
Amir RosenthalVasilis Ntziachristos

Abstract

The use of model-based algorithms in tomographic imaging offers many advantages over analytical inversion methods. However, the relatively high computational complexity of model-based approaches often restricts their efficient implementation. In practice, many modern imaging modalities, such as computed-tomography, positron-emission tomography, or optoacoustic tomography, normally use a very large number of pixels/voxels for image reconstruction. Consequently, the size of the forward-model matrix hinders the use of many inversion algorithms. In this paper, we present a new framework for model-based 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 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 2-D o...Continue Reading

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Citations

Jan 7, 2014·Medical Physics·Christian LutzweilerDaniel Razansky
Oct 16, 2015·Optics Letters·Pouyan MohajeraniVasilis Ntziachristos
Oct 16, 2015·IEEE Transactions on Medical Imaging·Christian LutzweilerDaniel Razansky
Sep 14, 2016·IEEE Transactions on Medical Imaging·Gilad Drozdov, Amir Rosenthal
Nov 22, 2018·Biomedical Optics Express·Amir Rosenthal
Jul 26, 2019·Physics in Medicine and Biology·Xosé Luís Deán-Ben, Daniel Razansky
Dec 20, 2019·Tissue Engineering. Part B, Reviews·Binita ShresthaEric M Brey
Sep 28, 2018·Biomedical Optics Express·Roya ParidarMahdi Orooji

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