Cargando…

Nonlinear statistical iterative reconstruction for propagation-based phase-contrast tomography

Propagation-based phase-contrast tomography has become a valuable tool for visualization of three-dimensional biological samples, due to its high sensitivity and its potential in providing increased contrast between materials with similar absorption properties. We present a statistical iterative rec...

Descripción completa

Detalles Bibliográficos
Autores principales: Hehn, Lorenz, Morgan, Kaye, Bidola, Pidassa, Noichl, Wolfgang, Gradl, Regine, Dierolf, Martin, Noël, Peter B., Pfeiffer, Franz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AIP Publishing LLC 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6481703/
https://www.ncbi.nlm.nih.gov/pubmed/31069290
http://dx.doi.org/10.1063/1.4990387
_version_ 1783413777367040000
author Hehn, Lorenz
Morgan, Kaye
Bidola, Pidassa
Noichl, Wolfgang
Gradl, Regine
Dierolf, Martin
Noël, Peter B.
Pfeiffer, Franz
author_facet Hehn, Lorenz
Morgan, Kaye
Bidola, Pidassa
Noichl, Wolfgang
Gradl, Regine
Dierolf, Martin
Noël, Peter B.
Pfeiffer, Franz
author_sort Hehn, Lorenz
collection PubMed
description Propagation-based phase-contrast tomography has become a valuable tool for visualization of three-dimensional biological samples, due to its high sensitivity and its potential in providing increased contrast between materials with similar absorption properties. We present a statistical iterative reconstruction algorithm for this imaging technique in the near-field regime. Under the assumption of a single material, the propagation of the x-ray wavefield—relying on the transport-of-intensity equation—is made an integral part of the tomographic reconstruction problem. With a statistical approach acting directly on the measured intensities, we find an unconstrained nonlinear optimization formulation whose solution yields the three-dimensional distribution of the sample. This formulation not only omits the intermediate step of retrieving the projected thicknesses but also takes the statistical properties of the measurements into account and incorporates prior knowledge about the sample in the form of regularization techniques. We show some advantages of this integrated approach compared to two-step approaches on data obtained using a commercially available x-ray micro-tomography system. In particular, we address one of the most considerable challenges of the imaging technique, namely, the artifacts arising from samples containing highly absorbing features. With the use of statistical weights in our noise model, we can account for these materials and recover features in the vicinity of the highly absorbing features that are lost in the conventional two-step approaches. In addition, the statistical modeling of our reconstruction approach will prove particularly beneficial in the ongoing transition of this imaging technique from synchrotron facilities to laboratory setups.
format Online
Article
Text
id pubmed-6481703
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher AIP Publishing LLC
record_format MEDLINE/PubMed
spelling pubmed-64817032019-05-08 Nonlinear statistical iterative reconstruction for propagation-based phase-contrast tomography Hehn, Lorenz Morgan, Kaye Bidola, Pidassa Noichl, Wolfgang Gradl, Regine Dierolf, Martin Noël, Peter B. Pfeiffer, Franz APL Bioeng Articles Propagation-based phase-contrast tomography has become a valuable tool for visualization of three-dimensional biological samples, due to its high sensitivity and its potential in providing increased contrast between materials with similar absorption properties. We present a statistical iterative reconstruction algorithm for this imaging technique in the near-field regime. Under the assumption of a single material, the propagation of the x-ray wavefield—relying on the transport-of-intensity equation—is made an integral part of the tomographic reconstruction problem. With a statistical approach acting directly on the measured intensities, we find an unconstrained nonlinear optimization formulation whose solution yields the three-dimensional distribution of the sample. This formulation not only omits the intermediate step of retrieving the projected thicknesses but also takes the statistical properties of the measurements into account and incorporates prior knowledge about the sample in the form of regularization techniques. We show some advantages of this integrated approach compared to two-step approaches on data obtained using a commercially available x-ray micro-tomography system. In particular, we address one of the most considerable challenges of the imaging technique, namely, the artifacts arising from samples containing highly absorbing features. With the use of statistical weights in our noise model, we can account for these materials and recover features in the vicinity of the highly absorbing features that are lost in the conventional two-step approaches. In addition, the statistical modeling of our reconstruction approach will prove particularly beneficial in the ongoing transition of this imaging technique from synchrotron facilities to laboratory setups. AIP Publishing LLC 2018-01-23 /pmc/articles/PMC6481703/ /pubmed/31069290 http://dx.doi.org/10.1063/1.4990387 Text en © 2018 Author(s). 2473-2877/2018/2(1)/016105/12 All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Articles
Hehn, Lorenz
Morgan, Kaye
Bidola, Pidassa
Noichl, Wolfgang
Gradl, Regine
Dierolf, Martin
Noël, Peter B.
Pfeiffer, Franz
Nonlinear statistical iterative reconstruction for propagation-based phase-contrast tomography
title Nonlinear statistical iterative reconstruction for propagation-based phase-contrast tomography
title_full Nonlinear statistical iterative reconstruction for propagation-based phase-contrast tomography
title_fullStr Nonlinear statistical iterative reconstruction for propagation-based phase-contrast tomography
title_full_unstemmed Nonlinear statistical iterative reconstruction for propagation-based phase-contrast tomography
title_short Nonlinear statistical iterative reconstruction for propagation-based phase-contrast tomography
title_sort nonlinear statistical iterative reconstruction for propagation-based phase-contrast tomography
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6481703/
https://www.ncbi.nlm.nih.gov/pubmed/31069290
http://dx.doi.org/10.1063/1.4990387
work_keys_str_mv AT hehnlorenz nonlinearstatisticaliterativereconstructionforpropagationbasedphasecontrasttomography
AT morgankaye nonlinearstatisticaliterativereconstructionforpropagationbasedphasecontrasttomography
AT bidolapidassa nonlinearstatisticaliterativereconstructionforpropagationbasedphasecontrasttomography
AT noichlwolfgang nonlinearstatisticaliterativereconstructionforpropagationbasedphasecontrasttomography
AT gradlregine nonlinearstatisticaliterativereconstructionforpropagationbasedphasecontrasttomography
AT dierolfmartin nonlinearstatisticaliterativereconstructionforpropagationbasedphasecontrasttomography
AT noelpeterb nonlinearstatisticaliterativereconstructionforpropagationbasedphasecontrasttomography
AT pfeifferfranz nonlinearstatisticaliterativereconstructionforpropagationbasedphasecontrasttomography