Quantitative Phase Imaging & Optical Diffraction Tomography

Quantitative phase imaging

Quantitativ phase imaging of single cells. a) The schematic drawing of the cross-section along the optical axis illustrates the phase delay ∆Φ of light passing through a cell (wavefronts drawn as red lines). The phase delay is caused by the RI of the cell, which is larger than that of the surrounding medium n m . The images in (b) and (c) show phase and intensity images of a representative HL60/S4 cell.
Quantitative phase imaging of a single cell. a) The schematic drawing of the cross-section along the optical axis illustrates the phase delay ∆Φ of light passing through a cell (wavefronts drawn as red lines). The phase delay is caused by the RI of the cell, which is larger than that of the surrounding medium. The images in (b) and (c) show phase and intensity images of an HL60/S4 cell. Figure reproduced from [1].

Quantitative phase imaging (QPI) is a marker-free technique for quantifying the optical thickness of cells. We are interested in optical properties that can be derived from the optical thickness, including refractive index (RI) or mass density of cells. QPI is commonly performed using digital holographic microscopy (DHM) [2]. In addition, we employ commercial QPI hardware (Phasics, S.A., France) that is based on quadriwave lateral-shearing interferometry [3]. We apply these techniques, for example, to find average RI values for single cells or cell populations and we are employing QPI to study various biological questions [4, 5].

Optical diffraction tomography

3D Refractive Index of an HL60 cell
Refractive index reconstruction of an HL60/S4 cell. The 3D visualization shows the iso-surfaces at 1.339 (violet), 1.357 (yellow), 1.363 (orange) and 1.380 (red). Figure reproduced from [1].

Optical diffraction tomography (ODT) is a 3D imaging technique that is used to obtain the quantitative RI distribution within cells. ODT employs QPI techniques to record 2D phase images of a cell from multiple angles which are then used for the tomographic 3D reconstruction. The tomographic approach is crucial for 3D RI imaging, because it allows to deduce the RI from the optical thickness that is measured with QPI [6, 7].

In practice, we employ optical and microfluidic devices (see Optical Trapping) to rotate single cells while acquiring QPI and fluorescence data. Our approach to ODT allows to image single cells in suspension, but requires elaborate pre-processing steps. Thus, we are constantly developing state of the art image registration and tomographic reconstruction algorithms [8, 9]. If you are interested in our QPI and ODT techniques, please check out the software section.

[1] P. Müller, “Optical Diffraction Tomography for Single Cells,” PhD Thesis, 2016.
author = {Paul M{\"{u}}ller},
title = {{Optical Diffraction Tomography for Single Cells}},
school = {TU Dresden},
year = {2016},
file = {:http\://www.qucosa.de/fileadmin/data/qucosa/documents/20226/Mueller_ODT_SingleCells_Diss_2016_print.pdf:URL},
owner = {paul},
timestamp = {2016.08.03},
url = {http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-202261},
abstract = {Analyzing the structure of a single cell based on its refractive index (RI) distribution is a common and valued approach, because it does not require any artificial markers. The RI is an inherent structural marker that can be quantified in three dimensions with optical diffraction tomography (ODT), an inverse scattering technique. This work reviews the theory of ODT and its implementation with an emphasis on single-cell analysis, identifying the Rytov approximation as the most efficient descriptor for light propagation. The accuracy of the reconstruction method is verified with in silico data and imaging artifacts associated with the inverse scattering approach are addressed. Furthermore, an experimental ODT setup is presented that consists of a bright-field microscope, a phase-imaging camera, and an optical trap combined with a microfluidic chip.
A novel image analysis pipeline is proposed that addresses image corrections and frame alignment of the recorded data prior to the RI reconstruction.
In addition, for a rotational axis that is tilted with respect to the image plane, an improved reconstruction algorithm is introduced and applied to single, suspended cells in vitro, achieving sub-cellular resolution.}
[2] [doi] M. Schürmann, J. Scholze, P. Müller, C. J. Chan, A. E. Ekpenyong, K. J. Chalut, and J. Guck, “Refractive index measurements of single, spherical cells using digital holographic microscopy,” in Biophysical methods in cell biology, E. K. Paluch, Ed., Academic Press, 2015, vol. 125, p. 143–159.
author = {Sch{\"{u}}rmann, Mirjam and Scholze, Jana and M{\"{u}}ller, Paul
and Chan, Chii J and Ekpenyong, Andrew E and Chalut, Kevin J and
Guck, Jochen},
title = {{Refractive index measurements of single, spherical cells using digital
holographic microscopy}},
booktitle = {Biophysical Methods in Cell Biology},
publisher = {Academic Press},
year = {2015},
editor = {Paluch, Ewa K},
volume = {125},
series = {Methods in Cell Biology},
pages = {143--159},
abstract = {Abstract In this chapter, we introduce digital holographic microscopy
(DHM) as a marker-free method to determine the refractive index of
single, spherical cells in suspension. The refractive index is a
conclusive measure in a biological context. Cell conditions, such
as differentiation or infection, are known to yield significant changes
in the refractive index. Furthermore, the refractive index of biological
tissue determines the way it interacts with light. Besides the biological
relevance of this interaction in the retina, a lot of methods used
in biology, including microscopy, rely on light-tissue or light-cell
interactions. Hence, determining the refractive index of cells using
{DHM} is valuable in many biological
applications. This chapter covers the main topics that are important
for the implementation of DHM: setup, sample preparation, and analysis.
First, the optical setup is described in detail including notes and
suggestions for the implementation. Following that, a protocol for
the sample and measurement preparation is explained. In the analysis
section, an algorithm for the determination of quantitative phase
maps is described. Subsequently, all intermediate steps for the calculation
of the refractive index of suspended cells are presented, exploiting
their spherical shape. In the last section, a discussion of possible
extensions to the setup, further measurement configurations, and
additional analysis methods are given. Throughout this chapter, we
describe a simple, robust, and thus easily reproducible implementation
of DHM. The different possibilities for extensions show the diverse
fields of application for this technique.},
doi = {10.1016/bs.mcb.2014.10.016},
issn = {0091-679X},
owner = {paul},
timestamp = {2016.08.03},
[3] [doi] S. Mousset, C. Rouyer, G. Marre, N. Blanchot, S. Montant, and B. Wattellier, “Piston measurement by quadriwave lateral shearing interferometry,” Optics Letters, vol. 31, iss. 17, p. 2634–2636, 2006.
author = {Mousset, Soazic and Rouyer, Claude and Marre, Gabrielle and Blanchot,
Nathalie and Montant, S{\'{e}}bastien and Wattellier, Benoit},
title = {{Piston measurement by quadriwave lateral shearing interferometry}},
journal = {{Optics Letters}},
year = {2006},
volume = {31},
pages = {2634--2636},
number = {17},
abstract = {We present what is to our knowledge a new method for measuring the
relative piston between two independent beams separated by a physical
gap, typical of petawatt facilities. The feasibility of this measurement,
based on quadriwave lateral shearing interferometry, has been demonstrated
experimentally: piston has been measured with accuracy and sensitivity
better than 50 nm.},
doi = {10.1364/OL.31.002634},
issn = {0146-9592},
owner = {paul},
timestamp = {2016.08.03}
[4] [doi] M. Schürmann, J. Scholze, P. Müller, J. Guck, and C. J. Chan, “Cell nuclei have lower refractive index and mass density than cytoplasm,” Journal of Biophotonics, p. pulished online prior to press, 2016.
author = {Mirjam Sch{\"{u}}rmann and Jana Scholze and Paul M{\"{u}}ller and
Jochen Guck and Chii J. Chan},
title = {Cell nuclei have lower refractive index and mass density than cytoplasm},
journal = {{Journal of Biophotonics}},
year = {2016},
pages = {pulished online prior to press},
month = {mar},
doi = {10.1002/jbio.201500273},
owner = {paul},
publisher = {Wiley-Blackwell},
timestamp = {2016.08.03},
url = {http://onlinelibrary.wiley.com/doi/10.1002/jbio.201500273/pdf},
abstract = {Common perception regards the nucleus as a densely packed object with higher refractive index (RI) and mass density than the surrounding cytoplasm. Here, the volume of isolated nuclei is systematically varied by electrostatic and osmotic conditions as well as drug treatments that modify chromatin conformation. The refractive index and dry mass of isolated nuclei is derived from quantitative phase measurements using digital holographic microscopy (DHM). Surprisingly, the cell nucleus is found to have a lower RI and mass density than the cytoplasm in four different cell lines and throughout the cell cycle. This result has important implications for conceptualizing light tissue interactions as well as biological processes in cells. }
[5] [doi] P. Müller, M. Schürmann, S. Girardo, G. Cojoc, and J. Guck, “Accurate evaluation of size and refractive index for spherical objects in quantitative phase imaging,” Optics express, vol. 26, iss. 8, p. 10729–10743, 2018.
author = {Paul M\"{u}ller and Mirjam Sch\"{u}rmann and Salvatore Girardo and Gheorghe Cojoc and Jochen Guck},
title = {Accurate evaluation of size and refractive index for spherical objects in quantitative phase imaging},
journal = {Optics Express},
year = {2018},
volume = {26},
number = {8},
pages = {10729--10743},
month = {apr},
abstract = {Measuring the average refractive index (RI) of spherical objects, such as suspended cells, in quantitative phase imaging (QPI) requires a decoupling of RI and size from the QPI data. This has been commonly achieved by determining the object\&\#x02019;s radius with geometrical approaches, neglecting light-scattering. Here, we present a novel QPI fitting algorithm that reliably uncouples the RI using Mie theory and a semi-analytical, corrected Rytov approach. We assess the range of validity of this algorithm in silico and experimentally investigate various objects (oil and protein droplets, microgel beads, cells) and noise conditions. In addition, we provide important practical cues for the analysis of spherical objects in QPI.},
doi = {10.1364/OE.26.010729},
file = {https://www.osapublishing.org/oe/viewmedia.cfm?uri=oe-26-8-10729&seq=0},
keywords = {Phase shift; Propagation methods ; Cell analysis; Index measurements; Mie theory; Digital holography},
owner = {QPI},
publisher = {OSA},
[6] P. Müller, M. Schürmann, and J. Guck, “The Theory of Diffraction Tomography,” arXiv 1507.00466 [q-bio.QM], 2015.
author = {M{\"{u}}ller, Paul and Sch{\"{u}}rmann, Mirjam and Guck, Jochen},
eprint = {1507.00466},
eprintclass = {q-bio.QM},
eprinttype = {arxiv},
journal = {{arXiv 1507.00466 [q-bio.QM]}},
owner = {paul},
timestamp = {2016.08.03},
title = {{The Theory of Diffraction Tomography}},
version = {2},
year = {2015},
url = {https://arxiv.org/pdf/1507.00466},
abstract = {Tomography is the three-dimensional reconstruction of an object from images taken at different angles. The term classical tomography is used, when the imaging beam travels in straight lines through the object. This assumption is valid for light with short wavelengths, for example in x-ray tomography. For classical tomography, a commonly used reconstruction method is the filtered back-projection algorithm which yields fast and stable object reconstructions. In the context of single-cell imaging, the back-projection algorithm has been used to investigate the cell structure or to quantify the refractive index distribution within single cells using light from the visible spectrum. Nevertheless, these approaches, commonly summarized as optical projection tomography, do not take into account diffraction. Diffraction tomography with the Rytov approximation resolves this issue. The explicit incorporation of the wave nature of light results in an enhanced reconstruction of the object's refractive index distribution. Here, we present a full literature review of diffraction tomography. We derive the theory starting from the wave equation and discuss its validity with the focus on applications for refractive index tomography. Furthermore, we derive the back-propagation algorithm, the diffraction-tomographic pendant to the back-projection algorithm, and describe its implementation in three dimensions. Finally, we showcase the application of the back-propagation algorithm to computer-generated scattering data. This review unifies the different notations in literature and gives a detailed description of the back-propagation algorithm, serving as a reliable basis for future work in the field of diffraction tomography. }
[7] [doi] P. Müller, M. Schürmann, and J. Guck, “ODTbrain: a Python library for full-view, dense diffraction tomography,” BMC Bioinformatics, vol. 16, iss. 1, p. 1–9, 2015.
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}
[8] [doi] P. Müller, M. Schürmann, C. J. Chan, and J. Guck, “Single-cell diffraction tomography with optofluidic rotation about a tilted axis,” in Proceedings of the international society for optics and photonics, 2015, p. 95480U.
author = {M{\"{u}}ller, Paul and Sch{\"{u}}rmann, Mirjam and Chan, Chii J and
Guck, Jochen},
title = {{Single-cell diffraction tomography with optofluidic rotation about
a tilted axis}},
booktitle = {Proceedings of the International Society for Optics and Photonics},
year = {2015},
volume = {9548},
pages = {95480U},
abstract = {Optical diffraction tomography (ODT) is a tomographic technique that
can be used to measure the three- dimensional (3D) refractive index
distribution within living cells without the requirement of any marker.
In principle, ODT can be regarded as a generalization of optical
projection tomography which is equivalent to com- puterized tomography
(CT). Both optical tomographic techniques require projection-phase
images of cells mea- sured at multiple angles. However, the reconstruction
of the 3D refractive index distribution post-measurement differs
for the two techniques. It is known that ODT yields better results
than projection tomography, because it takes into account diffraction
of the imaging light due to the refractive index structure of the
sample. Here, we apply ODT to biological cells in a microfluidic
chip which combines optical trapping and microfluidic flow to achieve
an optofluidic single-cell rotation. In particular, we address the
problem that arises when the trapped cell is not rotating about an
axis perpendicular to the imaging plane, but instead about an arbitrarily
tilted axis. In this paper we show that the 3D reconstruction can
be improved by taking into account such a tilted rotational axis
in the reconstruction process.},
doi = {10.1117/12.2191501},
owner = {paul},
timestamp = {2016.08.03}
[9] [doi] M. Schürmann, G. Cojoc, S. Girardo, E. Ulbricht, J. Guck, and P. Müller, “Three-dimensional correlative single-cell imaging utilizing fluorescence and refractive index tomography,” Journal of biophotonics, vol. 11, iss. 3, p. e201700145, 2017.
author = {Mirjam Sch\"urmann and Gheorghe Cojoc and Salvatore Girardo and Elke Ulbricht and Jochen Guck and Paul M\"{u}ller},
title = {Three-dimensional correlative single-cell imaging utilizing fluorescence and refractive index tomography},
journal = {Journal of Biophotonics},
year = {2017},
volume = {11},
number = {3},
pages = {e201700145},
month = {aug},
doi = {10.1002/jbio.201700145},
file = {https://arxiv.org/pdf/1706.00715},
owner = {ODT},
publisher = {Wiley-Blackwell},