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Title: Hyperspectral image reconstruction for x-ray fluorescence tomography

Abstract

A penalized maximum-likelihood estimation is proposed to perform hyperspectral (spatio-spectral) image reconstruction for X-ray fluorescence tomography. The approach minimizes a Poisson-based negative log-likelihood of the observed photon counts, and uses a penalty term that has the effect of encouraging local continuity of model parameter estimates in both spatial and spectral dimensions simultaneously. The performance of the reconstruction method is demonstrated with experimental data acquired from a seed of arabidopsis thaliana collected at the 13-ID-E microprobe beamline at the Advanced Photon Source. The resulting element distribution estimates with the proposed approach show significantly better reconstruction quality than the conventional analytical inversion approaches, and allows for a high data compression factor which can reduce data acquisition times remarkably. In particular, this technique provides the capability to tomographically reconstruct full energy dispersive spectra without compromising reconstruction artifacts that impact the interpretation of results.

Authors:
; ; ; ;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
National Science Foundation (NSF) - Directorate for Geosciences Division of Earth Sciences (GEO/EAR); USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
OSTI Identifier:
1392113
DOE Contract Number:
AC02-06CH11357
Resource Type:
Journal Article
Resource Relation:
Journal Name: Optics Express; Journal Volume: 23; Journal Issue: 7
Country of Publication:
United States
Language:
English
Subject:
Fluorescence microscopy; Image reconstruction techniques; Inverse problems; Tomographic image processing

Citation Formats

Gürsoy, Doǧa, Biçer, Tekin, Lanzirotti, Antonio, Newville, Matthew G., and De Carlo, Francesco. Hyperspectral image reconstruction for x-ray fluorescence tomography. United States: N. p., 2015. Web. doi:10.1364/OE.23.009014.
Gürsoy, Doǧa, Biçer, Tekin, Lanzirotti, Antonio, Newville, Matthew G., & De Carlo, Francesco. Hyperspectral image reconstruction for x-ray fluorescence tomography. United States. doi:10.1364/OE.23.009014.
Gürsoy, Doǧa, Biçer, Tekin, Lanzirotti, Antonio, Newville, Matthew G., and De Carlo, Francesco. Thu . "Hyperspectral image reconstruction for x-ray fluorescence tomography". United States. doi:10.1364/OE.23.009014.
@article{osti_1392113,
title = {Hyperspectral image reconstruction for x-ray fluorescence tomography},
author = {Gürsoy, Doǧa and Biçer, Tekin and Lanzirotti, Antonio and Newville, Matthew G. and De Carlo, Francesco},
abstractNote = {A penalized maximum-likelihood estimation is proposed to perform hyperspectral (spatio-spectral) image reconstruction for X-ray fluorescence tomography. The approach minimizes a Poisson-based negative log-likelihood of the observed photon counts, and uses a penalty term that has the effect of encouraging local continuity of model parameter estimates in both spatial and spectral dimensions simultaneously. The performance of the reconstruction method is demonstrated with experimental data acquired from a seed of arabidopsis thaliana collected at the 13-ID-E microprobe beamline at the Advanced Photon Source. The resulting element distribution estimates with the proposed approach show significantly better reconstruction quality than the conventional analytical inversion approaches, and allows for a high data compression factor which can reduce data acquisition times remarkably. In particular, this technique provides the capability to tomographically reconstruct full energy dispersive spectra without compromising reconstruction artifacts that impact the interpretation of results.},
doi = {10.1364/OE.23.009014},
journal = {Optics Express},
number = 7,
volume = 23,
place = {United States},
year = {Thu Jan 01 00:00:00 EST 2015},
month = {Thu Jan 01 00:00:00 EST 2015}
}