Roadmap on Digital Holography-Based Quantitative Phase Imaging

Abstract

Quantitative Phase Imaging (QPI) provides unique means for the imaging of biological or technical microstructures, merging beneficial features identified with microscopy, interferometry, holography, and numerical computations. This roadmap article reviews several digital holography-based QPI approaches developed by prominent research groups. It also briefly discusses the present and future perspectives of 2D and 3D QPI research based on digital holographic microscopy, holographic tomography, and their applications.

Keywords: 3D distribution of refractive index; biomedical analysis at cellular level; digital holographic microscopy; holographic tomography; quantitative phase imaging.

Figure 10

(a) Multi-scale HT zebrafish image [143]. (b) Refractive index (red) and polarization contrast (yellow) HT zebrafish tail image [149].

Figure 12

Neural microscopy framework for quantitative phase imaging. (a) NN1 performs reconstruction of quantitative phase image from intensity acquisition. Here, as an example, the sample is a culture of adherent cells. (b) NN2 infers a quantitative representation from the phase image. Here, as an example, NN2 maps the phase image of adherent cells into an image that encodes, simultaneously, cell positions and dry mass measurements. (c) NN3 directly infers the quantitative representation from intensity acquisition. Phase image reconstruction is discarded. Such NN could be all-optical (adapted from [176]). Light is then transmitted, through the sample, towards a fabricated NN that infers an image of the quantitative representation.

Figure 1

(a) example of optical setup for transmission Digital Holographic Microscopy DHM. (b) Optical setup for Tomographic Diffractive Microscopy: Laser source with controllable coherence length. NF neutral filter, λ/2 plate, PBS polarizing beam splitter, BS beam splitter, BE beam expander, SM steering mirror and M mirror, BF back focal plane, S specimen, C cell, O object wave, R reference wave.

Figure 2

3D representation of color-coded optical path difference (OPD) of a living neuronal network. The right part of the image is quasi speckle-free thanks to a polychromatic DHM approach [37], allowing to study neuronal processes and network connectivity.

Figure 3

Phase image by TDM of an interpenetrated bundle of neuron dendrites.

Figure 4

Image of a dendritic spine. Spatial resolution <100 nm [4].

Figure 5

(a) Schematic of the self-reference on-axis apparatus used for QPI. (b) Flowchart of the phase-shifting process (Adapted from [71]). (c) Iterative QPI approach described in the text (Adapted from [73]).

Figure 6

(a,b2) Reconstructions of thin (OT < λ) phase-only object by the iterative approach when initialized with regular intensity measurement (b1) and phase-contrast measurement (not shown), respectively (Adapted from [73]). (c) Theoretical and (d) reconstructed phase of a refractive lens (OT > λ) by using the phase-shifting method (Adapted from [71]).

Figure 7

The pump (λ₁) two-probes (λ₂) method allows the recovery of permittivity transient, separation of the phase delay, and absorbance contributions at the wavelength of probe (λ₂). Light-matter interaction from the focal region is fully defined by the instantaneous permittivity (square of the complex refractive index [n(t) + ik(t)]²). The configuration can be applied to both transmission and reflection modes.

Figure 8

AI-based analysis of 3D QPI. (A) Segmentation of immunological synapse formation. (B) Retrieving molecular information from unlabeled cells.

Figure 9

(i): Comparison of coherent transfer functions (CTF) of (a) BR, (b) SR, and (c) IDT. (ii): Comparison of slices of 3D refractive index distribution of candida rugosa and the experimentally obtained CTFs corresponds to (a) SR approach, (b) BR approach, and (c) IDT approach. (iii): 3D illustration of candida rugosa at sub-cellular structural views; the different colors represent the different organelles of the cell. Scale bars: 2 µm. Adapted from [130].

Figure 11

(a) CAD model of the 3D cell phantom. (b,c) Cross-sections of the 3D RI distribution of the phantom measured using three different holographic tomographs with a limited angle of projections. (d,e) Histograms of the ΔRI in case of full measurement volume (d) and manually segmented single nucleoli (e).