Ordered subsets convex algorithm for 3D terahertz transmission tomography

B. Recur; H. Balacey; J. Bou Sleiman; J. B. Perraud; J. P. Guillet; A. Kingston; P. Mounaix

doi:10.1364/OE.22.023299

Ordered subsets convex algorithm for 3D terahertz transmission tomography

B. Recur^*
, H. Balacey
, J. Bou Sleiman
, J. B. Perraud
, J. P. Guillet
, A. Kingston
, P. Mounaix

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

29 Citations (Scopus)

Abstract

We investigate in this paper a new reconstruction method in order to perform 3D Terahertz (THz) tomography using a continuous wave acquisition setup in transmission mode. This method is based on the Maximum Likelihood for TRansmission tomography (ML-TR) first developed for X-ray imaging. We optimize the Ordered Subsets Convex (OSC) implementation of the ML-TR by including the Gaussian propagation model of THz waves and take into account the intensity distributions of both blank calibration scan and dark-field measured on THz detectors. THz ML-TR reconstruction quality and accuracy are discussed and compared to other tomographic reconstructions.

Original language	English
Pages (from-to)	23299-23309
Number of pages	11
Journal	Optics Express
Volume	22
Issue number	19
DOIs	https://doi.org/10.1364/OE.22.023299
Publication status	Published - 22 Sept 2014

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title = "Ordered subsets convex algorithm for 3D terahertz transmission tomography",

abstract = "We investigate in this paper a new reconstruction method in order to perform 3D Terahertz (THz) tomography using a continuous wave acquisition setup in transmission mode. This method is based on the Maximum Likelihood for TRansmission tomography (ML-TR) first developed for X-ray imaging. We optimize the Ordered Subsets Convex (OSC) implementation of the ML-TR by including the Gaussian propagation model of THz waves and take into account the intensity distributions of both blank calibration scan and dark-field measured on THz detectors. THz ML-TR reconstruction quality and accuracy are discussed and compared to other tomographic reconstructions.",

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AU - Recur, B.

AU - Balacey, H.

AU - Bou Sleiman, J.

AU - Perraud, J. B.

AU - Guillet, J. P.

AU - Kingston, A.

AU - Mounaix, P.

PY - 2014/9/22

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N2 - We investigate in this paper a new reconstruction method in order to perform 3D Terahertz (THz) tomography using a continuous wave acquisition setup in transmission mode. This method is based on the Maximum Likelihood for TRansmission tomography (ML-TR) first developed for X-ray imaging. We optimize the Ordered Subsets Convex (OSC) implementation of the ML-TR by including the Gaussian propagation model of THz waves and take into account the intensity distributions of both blank calibration scan and dark-field measured on THz detectors. THz ML-TR reconstruction quality and accuracy are discussed and compared to other tomographic reconstructions.

AB - We investigate in this paper a new reconstruction method in order to perform 3D Terahertz (THz) tomography using a continuous wave acquisition setup in transmission mode. This method is based on the Maximum Likelihood for TRansmission tomography (ML-TR) first developed for X-ray imaging. We optimize the Ordered Subsets Convex (OSC) implementation of the ML-TR by including the Gaussian propagation model of THz waves and take into account the intensity distributions of both blank calibration scan and dark-field measured on THz detectors. THz ML-TR reconstruction quality and accuracy are discussed and compared to other tomographic reconstructions.

UR - https://www.scopus.com/pages/publications/84907485708

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DO - 10.1364/OE.22.023299

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VL - 22

SP - 23299

EP - 23309

JO - Optics Express

JF - Optics Express

IS - 19

ER -

Ordered subsets convex algorithm for 3D terahertz transmission tomography

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