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Performance of a nullspace-map image reconstruction algorithm

Kwee, IW; Tanikawa, Y; Proskurin, S; Arridge, SR; Delpy, DT; Yamada, Y; (1997) Performance of a nullspace-map image reconstruction algorithm. OPTICAL TOMOGRAPHY AND SPECTROSCOPY OF TISSUE: THEORY, INSTRUMENTATION, MODEL, AND HUMAN STUDIES II, PROCEEDINGS OF , 2979 185 - 196.

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There are two reasons that might be attributed to the difficulty for the imaging problem in optical tomography, and in inverse problems in general. Firstly: the problem is mostly underdetermined. Secondly, the inverse problem is highly ill-conditioned due to the diffusive nature of the photons. We introduce Bayesian optimization that provides a method to incorporate a priori knowledge in the inversion and we show with the concept of nullspace that the Bayesian prior probability generalizes conventional regularization by introducing a prior model. Reconstruction results of test objects from simulated data and a reconstruction example on a head model show that use the nullspace gives considerable improvement.

Type: Article
Title: Performance of a nullspace-map image reconstruction algorithm
Location: SAN JOSE, CA
Keywords: optical tomography, image reconstruction, maximum a posteriori probability, ill-posed problems, regularization, Bayesian probability, prior knowledge
URI: http://discovery.ucl.ac.uk/id/eprint/179587
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