Phase imaging and tomography libraries

To determine the refractive index of single cells and cell populations, we apply quantitative phase imaging (QPI) and optical diffraction tomography (ODT) with in-house analysis libraries that are available as public repositories.

ODTbrain
Demonstration of 3D ODT in silico.

With ODTbrain, we have introduced the first publicly available library for ODT [1]. ODTbrain contains an implementation of the backpropagation algorithm in 3D. The reconstruction of the refractive index from phase and amplitude sinograms can be performed with the Born or the Rytov approximation. The library also comes with a special backpropagation algorithm for the case of a tilted rotational axis relative to the image plane.
documentation; source code

  1. P. Müller, M. Schürmann, and J. Guck, “ODTbrain: a Python library for full-view, dense diffraction tomography,” BMC Bioinformatics, vol. 16, iss. 1, pp. 1-9, 2015. doi:10.1186/s12859-015-0764-0
    [BibTeX] [Abstract] [Download PDF]

    Analyzing the three-dimensional (3D) refractive index distribution of a single cell makes it possible to describe and characterize its inner structure in a marker-free manner. A dense, full-view tomographic data set is a set of images of a cell acquired for multiple rotational positions, densely distributed from 0 to 360 degrees. The reconstruction is commonly realized by projection tomography, which is based on the inversion of the Radon transform. The reconstruction quality of projection tomography is greatly improved when first order scattering, which becomes relevant when the imaging wavelength is comparable to the characteristic object size, is taken into account. This advanced reconstruction technique is called diffraction tomography. While many implementations of projection tomography are available today, there is no publicly available implementation of diffraction tomography so far.

    @ARTICLE{Mueller15,
    author = {M{\"{u}}ller, Paul and Sch{\"{u}}rmann, Mirjam and Guck, Jochen},
    title = {{ODTbrain: a Python library for full-view, dense diffraction tomography}},
    journal = {{BMC Bioinformatics}},
    year = {2015},
    volume = {16},
    pages = {1--9},
    number = {1},
    abstract = {Analyzing the three-dimensional (3D) refractive index distribution
    of a single cell makes it possible to describe and characterize its
    inner structure in a marker-free manner. A dense, full-view tomographic
    data set is a set of images of a cell acquired for multiple rotational
    positions, densely distributed from 0 to 360 degrees. The reconstruction
    is commonly realized by projection tomography, which is based on
    the inversion of the Radon transform. The reconstruction quality
    of projection tomography is greatly improved when first order scattering,
    which becomes relevant when the imaging wavelength is comparable
    to the characteristic object size, is taken into account. This advanced
    reconstruction technique is called diffraction tomography. While
    many implementations of projection tomography are available today,
    there is no publicly available implementation of diffraction tomography
    so far.},
    doi = {10.1186/s12859-015-0764-0},
    issn = {1471-2105},
    owner = {paul},
    timestamp = {2016.08.03},
    url = {http://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-015-0764-0?site=bmcbioinformatics.biomedcentral.com}
    }