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Title: Hyperspectral image reconstruction for x-ray fluorescence tomography

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:
 [1] ;  [2] ;  [3] ;  [3] ;  [1]
  1. Argonne National Lab. (ANL), Argonne, IL (United States). Advanced Photon Source
  2. Argonne National Lab. (ANL), Argonne, IL (United States). Mathematics and Computer Science Div.
  3. Univ. of Chicago, Chicago, IL (United States). Center for Advanced Radiation Sources
Publication Date:
Grant/Contract Number:
AC02-06CH11357; FG02-94ER14466; EAR1128799
Type:
Published Article
Journal Name:
Optics Express
Additional Journal Information:
Journal Volume: 23; Journal Issue: 7; Journal ID: ISSN 1094-4087
Publisher:
Optical Society of America (OSA)
Research Org:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org:
USDOE Office of Science (SC)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; 59 BASIC BIOLOGICAL SCIENCES
OSTI Identifier:
1222291
Alternate Identifier(s):
OSTI ID: 1200860