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Proceedings Paper

Performance of a nullspace-map image reconstruction algorithm
Author(s): Ivo W. Kwee; Yukari Tanikawa; Sergei G. Proskurin; Simon Robert Arridge; David T. Delpy; Yukio Yamada
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Paper 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.

Paper Details

Date Published: 18 August 1997
PDF: 12 pages
Proc. SPIE 2979, Optical Tomography and Spectroscopy of Tissue: Theory, Instrumentation, Model, and Human Studies II, (18 August 1997); doi: 10.1117/12.280245
Show Author Affiliations
Ivo W. Kwee, Univ. College London (UK) and Mechanical Engineering Lab. (United Kingdom)
Yukari Tanikawa, Mechanical Engineering Lab. (Japan)
Sergei G. Proskurin, Mechanical Engineering Lab. (Japan)
Simon Robert Arridge, Univ. College London (United Kingdom)
David T. Delpy, Univ. College London (United Kingdom)
Yukio Yamada, Mechanical Engineering Lab. (Japan)


Published in SPIE Proceedings Vol. 2979:
Optical Tomography and Spectroscopy of Tissue: Theory, Instrumentation, Model, and Human Studies II
Britton Chance; Robert R. Alfano, Editor(s)

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