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Gauss–Newton method for image reconstruction in diffuse optical tomography

Martin Schweiger1, Simon R Arridge1 and Ilkka Nissilä2

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We present a regularized Gauss–Newton method for solving the inverse problem of parameter reconstruction from boundary data in frequency-domain diffuse optical tomography. To avoid the explicit formation and inversion of the Hessian which is often prohibitively expensive in terms of memory resources and runtime for large-scale problems, we propose to solve the normal equation at each Newton step by means of an iterative Krylov method, which accesses the Hessian only in the form of matrix–vector products. This allows us to represent the Hessian implicitly by the Jacobian and regularization term. Further we introduce transformation strategies for data and parameter space to improve the reconstruction performance. We present simultaneous reconstructions of absorption and scattering distributions using this method for a simulated test case and experimental phantom data.


PACS

87.63.L- Visual imaging

02.10.Yn Matrix theory

42.30.Wb Image reconstruction; tomography

87.57.N- Image analysis

02.60.-x Numerical approximation and analysis

Subjects

Mathematical physics

Computational physics

Optics, quantum optics and lasers

Medical physics

Dates

Issue 10 (21 May 2005)

Received 21 January 2005, in final form 30 March 2005

Published 5 May 2005



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