18 August 1997 Performance of a nullspace-map image reconstruction algorithm
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Abstract
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.
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Ivo W. Kwee, Ivo W. Kwee, Yukari Tanikawa, Yukari Tanikawa, Sergei G. Proskurin, Sergei G. Proskurin, Simon Robert Arridge, Simon Robert Arridge, David T. Delpy, David T. Delpy, Yukio Yamada, Yukio Yamada, "Performance of a nullspace-map image reconstruction algorithm", Proc. SPIE 2979, Optical Tomography and Spectroscopy of Tissue: Theory, Instrumentation, Model, and Human Studies II, (18 August 1997); doi: 10.1117/12.280245; https://doi.org/10.1117/12.280245
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