BIOMEDICAL IMAGING GROUP (BIG)
Laboratoire d'imagerie biomédicale (LIB)

Optical Tomography Based on a Nonlinear Model that Handles Multiple Scattering

M.H. Shoreh, A. Goy, J. Lim, U. Kamilov, M. Unser, D. Psaltis

Proceedings of the Forty-Second IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'17), New Orleans LA, USA, March 5-9, 2017, pp. 6220–6224.

Learning Tomography (LT) is a nonlinear optimization algorithm for computationally imaging three-dimensional (3D) distribution of the refractive index in semi-transparent samples. Since the energy function in LT is generally nonconvex, the solution it obtains is not guaranteed to be globally optimal. In this paper, we describe linear and nonlinear tomographic reconstruction methods and compare them numerically. We present a review of the LT and, in addition, we investigate the influence of the initialization and exemplify the effect of regularization on the convergence of the algorithm. In particular, we show that both are essential for high-quality imaging in strongly scattering scenarios. 

@INPROCEEDINGS(http://bigwww.epfl.ch/publications/shoreh1701.html,
AUTHOR="Shoreh, M.H. and Goy, A. and Lim, J. and Kamilov, U. and Unser,
	M. and Psaltis, D.",
TITLE="Optical Tomography Based on a Nonlinear Model that Handles
	Multiple Scattering",
BOOKTITLE="Proceedings of the Forty-Second IEEE International Conference
	on Acoustics, Speech, and Signal Processing ({ICASSP'17})",
YEAR="2017",
editor="",
volume="",
series="",
pages="6220--6224",
address="New Orleans LA, USA",
month="March 5-9,",
organization="",
publisher="",
note="")
© 2017 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.