Abstract

Label-free imaging approaches seek to simplify and augment histopathologic assessment by replacing the current practice of staining by dyes to visualize tissue morphology with quantitative optical measurements. Quantitative phase imaging (QPI) operates with visible/UV light and thus provides a resolution matched to current practice. Here we introduce and demonstrate confocal QPI for label-free imaging of tissue sections and assess its utility for manual histopathologic inspection. Imaging cancerous and normal adjacent human breast and prostate, we show that tissue structural organization can be resolved with high spatial detail comparable to conventional hematoxylin and eosin (H&E) stains. Our confocal QPI images are found to be free of halo, solving this common problem in QPI. We further describe a virtual imaging system based on finite-difference time-domain (FDTD) calculations and combine it with numerical tissue phantoms to quantitatively show the absence of halo and the improved clarity in resolving subcellular features with confocal QPI compared to wide-field QPI. Confocal QPI bears the potential to become a common tool for label-free disease diagnosis, while the presented FDTD method provides a flexible platform to evaluate the diagnostic potential of QPI methods.

© 2020 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

1. INTRODUCTION

The microscopic examination of stained tissue for structural and functional changes is the standard method for detecting and grading most forms of human cancer [1]. For example, the hematoxylin and eosin (H&E) stain allows a trained observer to differentiate epithelial cells from the surrounding stroma. Morphological features of epithelial cells and their organization, further, are the basis for cancer diagnoses and studying progression. Scientific advances in the fields of microscopy could lead to augmenting and automation of histopathologic studies by combining label-free, quantitative imaging approaches with machine learning [2,3]. These techniques can provide the consistency in measurement of optical properties of tissue that cannot be manually assessed, nor quantified by using stains. Together, this can obviate the need for staining, allowing for objective decision-making based on pattern recognition, and can challenge current histopathologic practice.

Quantitative phase imaging (QPI) is a particularly promising label-free method. It combines wide-field (WF) optical microscopy with interferometry to reveal the structure of unlabeled, transparent samples [414]. The underlying principle is that spatial variations in the refractive index introduce changes in the relative phase of a light wave as it passes through the sample. QPI measures the phase of the transmitted light quantitatively and can provide a viable alternative to established label-based imaging methods such as fluorescence imaging of cells. Applied to tissue, QPI has shown to reveal the microstructural organization without staining and at the resolution of optical microscopy. Demonstrations of QPI-based histopathologic investigations [2] include prostate [15,16], colon [17,18], and breast [19,20]. The label-free approach promises a simpler pathology work flow by removing the need for sample staining while providing quantitative morphologic parameters to detect and grade cancer [2,16,17].

Although QPI data show correspondence to reference images of H&E-stained tissue, a detailed comparison of image quality and optimization of image contrast to mimic and enhance that of stained tissue images is still widely unaddressed. This might be understood by considering that QPI faces several fundamental and technical challenges, which require a compromise to be made between image quality, usability, and speed [14]. For example, some QPI implementations are affected by mechanical vibrations and air density fluctuations that cause temporal path length variations and lead to errors in the phase measurement. Hence, design of QPI systems and their operation in a suitable environment may not be compatible with the rugged environment of a pathology laboratory. To reduce stability requirements, common-path QPI methods were developed that largely cancel the effect of these temporal instabilities on the phase measurement. However, common-path methods are challenged by halo and shade-off appearing in the phase images, which are object-dependent effects that appear as negative phase contrast at edges of objects and reduced contrast in the center of large area objects [2123]. Further challenges were found with shot noise and speckle, which could be addressed by using high full well camera technology [24] and low coherence light sources [2527], respectively.

In the search for highest image quality, speed, and ease of use, confocal microscopy has received less attention than its WF counterparts. This is surprising because confocal microscopy is a widely used method in biomedical imaging. With fluorescence contrast, for example, it provides an unobscured view on the details of biological structure owing to the rejection of out-of-focus light provided by spatial pinhole filtering. When combined with QPI, it offers halo- and speckle-free phase imaging with standard monochromatic laser sources. Interferometric confocal microscopy has been employed for static [2830] and dynamic [3133] optical metrology and later to label-free imaging of cells [3436]. Optical coherence tomography has been applied to phase imaging of single cells and depth-resolved phase imaging of tissue [37,38]. Recently, synthetic optical holography (SOH) [39] was demonstrated as a holographic approach to confocal QPI [4042]. Implemented in a commercial confocal instrument, SOH allowed confocal QPI based on a beam scanning approach with frame acquisition times on the order of seconds, thus mitigating the slow scanning speed of initial demonstrations of confocal QPI based on sample scanning [43,44]. All these advances indicate that confocal QPI has become a promising candidate for practical, rapid, and high-resolution label-free imaging of biological specimen.

Here, we demonstrate confocal QPI of tissue sections as a means to provide label-free, high-resolution morphology images close to the level of detail of H&E. We first show the potential of confocal QPI in a theoretical study where we apply a virtual imaging system based on finite-difference time-domain (FDTD) and compare image contrast with established QPI methods. We then provide a first experimental demonstration of confocal QPI by imaging malignant and normal-adjacent tissue sections of breast, prostate, stomach, and cerebrum. Spatial detail and contrast of tissue morphology is compared with gold-standard H&E images obtained from adjacent tissue sections.

2. VIRTUAL IMAGING SYSTEM ON A COMPUTER

We first present a theoretical study to obtain a prediction for the spatial resolution and image contrast that can be achieved with confocal QPI. To this end, we applied the numerical algorithm described by Çapoğlu et al. [45] for implementing a virtual imaging system on a computer, which is based on rigorously solving Maxwell’s equations using the FDTD method. We adopted this algorithm to model three relevant QPI methods: first, we assumed transmission-mode confocal QPI as a representative modality for the here presented method. Second, we considered traditional WF QPI where the reference field is a replica of the illumination field. Third, we examined the widely employed method of common-path WF QPI where the reference field is derived by spatially filtering of the sample field. Using a commercial FDTD solver (Lumerical, Vancouver, CA), we synthesized and quantitatively compared phase images for a set of test samples. In the following, we detail the specific adoptions made to the original algorithm in order to model QPI.

Figure 1(a) shows a schematic representation of a virtual imaging system of confocal microscopy with simulation segmented into scattering, collection, and refocusing subsystems. Light scattering is calculated in the FDTD computational domain. The sample is illuminated with a Gaussian focus (${{\rm NA}_{{\rm cond}}} = 0.8$) that is injected by source ${S}$ located at $z = - 1\;\unicode{x00B5}{\rm m}$, focused on the sample surface ($z = 0$), and polarized along the $x$-axis. The scattered field at the sample plane, ${{\boldsymbol E}^{{\rm NF}}}(x,y)$, is detected with monitor ${M}$ at $z = 1\;\unicode{x00B5}{\rm m}$. A collection algorithm based on Fourier analysis produces the field at the far zone, ${{\boldsymbol E}^{{\rm FF}}}({u_x},{u_y})$, where $({u_x},{u_y})$ are the corresponding diffraction orders. Diffraction orders $({u_x},{u_y})$ falling outside the numerical aperture ${{\rm NA}_{{\rm obj}}} = 0.8$ of the collecting microscope objective are truncated. The refocusing algorithm Fourier transforms the field passing the aperture and constructs the field distribution at the image plane, ${{\boldsymbol E}^{{\rm IM}}}(x^\prime ,y^\prime)$. Spatial filtering at the image plane with an infinitesimal pinhole is implemented by recording the refocused field at a single position located on the optical axis of the system, ${{\boldsymbol E}^{{\rm IM}}}(0,0)$. To synthesize a confocal phase image, a set of simulations is run: while the sample is raster-scanned in $(x,y)$ with a step size of 100 nm, the field ${{\boldsymbol E}^{{\rm OBJ}}}(x,y) = {{\boldsymbol E}^{{\rm IM}}}{(0,0)|_{(x,y)}}$ is computed for each sample position $(x,y)$. To generate the reference field, ${{\boldsymbol E}^{{\rm REF}}}$, an empty sample is assumed; that is, the structure is replaced by substrate material. Sample field, ${{\boldsymbol E}^{{\rm OBJ}}}$ ($x,y$), and reference field, ${{\boldsymbol E}^{{\rm REF}}}$, are interfered to obtain the confocal interference image,

$$I_{\Delta \varphi}^{{\rm CF}}(x,y) = \sum\limits_{n = x,y,z} \big|E_n^{{\rm OBJ}}(x,y) + {e^{i\Delta \varphi}}E_n^{{\rm REF}}\big|^2,$$
where index $n$ runs over the electric field components $({E_x}\!,{E_y}\!,{E_z})$. The global phase between sample and reference field can be adjusted with $\Delta \varphi$.
 figure: Fig. 1.

Fig. 1. Performance prediction of an idealized confocal QPI microscope and comparison to wide-field QPI methods. (a) Virtual imaging system based on FDTD describing transmission-mode confocal QPI. (b) Virtual wide-field QPI. (c) Equally spaced plane waves describing Köhler illumination. (d)–(f) Synthesized quantitative phase images of a phase-object resolution test target at wavelength $\lambda = 561\;{\rm nm}$ for confocal QPI (${{\rm NA}_{{\rm cond}}} = {{\rm NA}_{{\rm obj}}} = 0.8$), traditional wide-field QPI, and common-path wide-field QPI (${{\rm NA}_{{\rm cond}}} = 0.09$ and ${{\rm NA}_{{\rm obj}}} = 0.8$). (g), (h) Vertical line profiles across the square of group 9 and the bars of group 10, respectively, as indicated by the arrows in (d)–(f). PML, perfectly matched layer boundary; Bloch, Bloch periodic boundary.

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Figure 1(b) shows the virtual imaging system used to model WF QPI. Bloch boundaries are assumed. The sample is illuminated with a single plane wave that is injected by source ${S}$ located at $z = - 1\;\unicode{x00B5}{\rm m}$ and at an angle as given by direction cosines, $({s_{\!x}}\!,{s_{\!y}})$. The scattered field from the sample, ${{\boldsymbol E}^{{\rm NF}}}(x,y)$, is collected with monitor M at $z = 1\;\unicode{x00B5}{\rm m}$ and subsequently refocused at the image plane, yielding ${{\boldsymbol E}^{{\rm OBJ}}} \equiv {{\boldsymbol E}^{{\rm IM}}}$. To implement Köhler illumination, it is necessary to run a series of simulations where the angle of the plane wave illumination is varied. More precisely, the direction cosines $({s_{\!x}}\!,{s_{\!y}})$ are assumed to be equally spaced and only those are considered that fall within the numerical aperture of the condenser ${{\rm NA}_{{\rm cond}}} = 0.09$, i.e., $s_x^2 + s_y^2 \le {{\rm NA}^2}$ as illustrated in Fig. 1(c). This effort—using many illuminating plane waves rather than a single plane wave—is needed because biological samples typically have key structural details comparable to the size of the wavelength and weak scattering may not be assumed (for details, see Ref. [45]). The spacing of the direction cosines, $\Delta s = 0.009\lt 2\pi /kW$, is chosen to be small enough to avoid aliasing, where $k = 2\pi /\lambda$ is the wave vector of the laser wavelength and $W$ is the size of the FDTD box. Two simulations are run for each of the direction cosines: one simulation that considers the sample and a reference simulation, as described above. For common-path WF QPI, the reference field is obtained by spatial filtering of the far-zone collected light from the sample, ${{\boldsymbol E}^{{\rm FF}}}({u_x}\!,{u_y})$, with a circular pinhole, ${F^{{\rm PH}}}({u_x},{u_y}) = \left\{{\begin{array}{*{20}{c}}1&{\sqrt {u_x^2 + u_y^2} \le \Delta s}\\0&{{\rm other}}\end{array}}\right.$, which corresponds to a pinhole size of 3 pixels (in units of diffraction orders). For traditional WF QPI, the reference field, ${{\boldsymbol E}^{{\rm REF}}}(x,y)$, is directly obtained from the reference simulation (empty sample). Sample field, ${{\boldsymbol E}^{{\rm OBJ}}}(x,y)$, and reference field, ${{\boldsymbol E}^{{\rm REF}}}(x,y)$, are interfered for each plane wave $({s_{\!x}}\!,{s_{\!y}})$ individually, and the resulting interference images are added up incoherently to obtain the WF interference image,

$$\begin{split}I_{\Delta \varphi}^{{\rm WF}}(x,y) = \sum\limits_{{s_{\!x}}\!,{s_{\!y}}} \sum\limits_{n = x,y,z} \big|E_{n,{s_{\!x}}\!,{s_{\!y}}}^{{\rm OBJ}}(x,y) + {e^{i\Delta \varphi}}E_{n,{s_{\!x}}\!,{s_{\!y}}}^{{\rm REF}}(x,y)\big|^2,\end{split}$$
where the indices $({s_{\!x}}\!,{s_{\!y}})$ run over the direction cosines of the plane wave illumination and index $n$ runs over the electric field components $({E_x}\!,{E_y}\!,{E_z})$.

We retrieve the phase image of the sample, $\varphi (x,y)$, by applying four-step phase shifting interferometry where we vary the global phase difference, $\Delta \varphi$, between the sample and reference field, ${{\boldsymbol E}^{{\rm OBJ}}}(x,y)$ and ${{\boldsymbol E}^{{\rm REF}}}(x,y)$, in steps of a quarter wavelength (0°, 90°, 180°, and 270°). The final phase image is obtained with the four-quadrant inverse tangent function,

$$\varphi = {\rm atan2}({I_{90}} - {I_{270}},{I_0} - {I_{180}}),$$
where indices $(x,y)$ are omitted for clarity. In this work, field calculations were done with built-in functions in Lumerical and phase interferometry was implemented in MATLAB (MathWorks Inc., Natick, MA). Importantly, the FDTD approach allows for accurately modeling of objects of arbitrary shape and with key structural details of the size of the wavelength, i.e., multiple scattering and resonance effects are taken into account. This capability is essential in modeling light scattering from microscopically heterogeneous samples such as tissue sections.

3. COMPARISON BETWEEN CONFOCAL QPI AND WIDE-FIELD QPI

We first analyzed fundamental aspects of image contrast with a dielectric resolution test target based on group 9 and 10 of the USAF-1951 pattern. This test target contains numbers and bars with refractive index $n = 1.5$ that are embedded in a 250 nm thick film of refractive index $n = 1.38$ and situated on an infinite substrate ($n = 1.38$). Comparison between the calculated phase images reveals the presence of halo and shade-off effects with common-path WF QPI [cf. Figs. 1(d)–1(f)]. Halo is manifested as a dark ring around the structures, while shade-off appears as a reduced optical phase in the center of the structures, as it is further quantified by a line profile across the square of group 9 [Fig. 1(g)]. Halo and shade-off are recognized as problems in QPI as they prevent accurate topography measurements and make interpretation of phase contrast in biological samples difficult [22,46,47]. In contrast, our calculations confirm that this problem is avoided with confocal and traditional WF QPI that are both free of halo and shade-off because the reference beam is generated directly from the illuminating beam.

Further differences can be observed with the presence of ringing artifacts at sharp edges [Fig. 1(g)] as well as a slight reduction in spatial resolution [Fig. 1(h)] with WF QPI as only elements 1–4 of group 10 are resolved according to the Rayleigh criterion. Interestingly, the line profile in Fig. 1(h) further shows significant negative phase contrast even in the case of traditional WF QPI. This is surprising as negative phase was typically attributed to common-path WF QPI only. Negative phase appears to be more strongly pronounced with the series of bars than with the isolated square [cf. Figs. 1(g) and 1(h)]. In comparison, confocal QPI is free of ringing, and negative phase contrast is not observed. Confocal QPI resolves all elements of group 10, corresponding to a spatial resolution of better than 1 µm (${{\rm NA}_{{\rm obj}}} = 0.8$, $\lambda = 561\;{\rm nm}$). We attribute these unexpected differences between confocal and WF QPI to the low numerical aperture of the condenser; in our calculation, this is ${{\rm NA}_{{\rm cond}}} = 0.09$. This choice follows a widely employed strategy to mitigate halo and shade-off by stepping down the condenser to establish sufficient spatial coherence at the sample [22,23]. However, the drawback of this solution is that imaging needs to be treated as coherent, which is known to lead to ringing and reduced spatial resolution in case of non-interferometric microscopy [48]. Our calculations predict this effect also for QPI. Hence, confocal QPI provides slightly better performance than common-path QPI when imaging small objects at the diffraction limit.

 figure: Fig. 2.

Fig. 2. Calculated phase contrast for numerical tissue section models. (a) High-resolution 2D refractive index model adopted from ultrastructural electron microscopy data on breast. (b) Calculated phase images illustrate improved spatial resolution and absence of ringing artifacts with confocal QPI (${{\rm NA}_{{\rm cond}}} = 0.09$ and ${{\rm NA}_{{\rm obj}}} = 0.8$). (c) Large area, low resolution model of breast tissue constructed from the data in Fig. 3. (d) Calculated phase images illustrate the effect of halo on image contrast that appears with common-path wide-field QPI (${{\rm NA}_{{\rm cond}}} = 0.045$ and ${{\rm NA}_{{\rm obj}}} = 0.4$). Common-path WF phase data were offset by ${+}0.1$ and ${+}0.5$ rad in (b) and (d).

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Improved spatial resolution and absence of halo and ringing, as well as suppression of out-of-plane scattering, are advantages that make confocal QPI well suited for the label-free imaging of biological specimens. As a demonstration of this application potential, we next compare QPI performance for label-free imaging of tissue sections. To this end, we first built a 2D refractive index model of a thin section of breast tissue that accurately reproduces shape and distribution of subcellular components with high resolution [Fig. 2(a)]. As a template, we employed an ultrastructural image of breast that was acquired with electron microscopy and thus affords nanoscale spatial resolution [49]. We labeled and assigned typical values for the refractive index to the individual cell components including cytoplasm ($n = 1.375$), nucleus (1.36), nucleolus (1.38), mitochondria (1.41) [50], lipid droplets (1.48) [51], golgi apparatus, endoplasmic reticulum, and microvilli (membranes, 1.46) [52] and assume extracellular medium (1.35). Figure 2(b) shows the calculated phase images. The spatial resolution advantage of confocal microscopy is apparent as well as the absence of ringing, producing a visibly cleaner image in comparison to WF methods. This interesting observation indicates that confocal QPI might have an advantage over WF QPI in resolving malignancy-induced morphological alterations, especially those located at the sub-micrometer level, which could help to reveal indicators of malignancies more reliably and should be explored further.

To illustrate degradation of image contrast by halo, we built a low-resolution model of the breast tissue section from Fig. 4(b) (area marked by yellow dashed box). To this end, we assumed a high-refractive index layer ($n = 1.40$) on a low-refractive index substrate ($n = 1.38$) to produce a phase contrast, in analogy to the USAF-1951 test sample from Fig. 1. Layer thickness was scaled linearly to match the experimental phase in Fig. 4, resulting in a thickness variation between 0 and 2.5 µm, which agrees reasonably well with the expected thickness of deparaffinized tissue sections. The resulting 3D model is illustrated by horizontal $(x,y)$ and vertical $(x,z)$ cross sections in Fig. 2(c). We observe that the halo of common-path WF QPI yields similar brightness between epithelial and extracellular regions as well as bright patches within the epithelium [Fig. 2(d)]. This effect confounds the analysis of global tissue architecture. Microscopic features such as size and shape cell nuclei seem to be correctly reproduced despite halo, although it is more difficult to locate them because of a lack of contrast. Confocal QPI and traditional WF QPI yield the correct contrast, which could benefit accuracy of manual inspections as well as automated diagnosis by machine learning algorithms. We note that halo in common-path WF QPI is mitigated in the limit of zero condenser ${\rm NA}$. However, mechanical limitations of the microscope (${{\rm NA}_{{\rm cond}}} \ge 0.09$) and shot noise (light throughput with halogen lamps) set a lower practical limit to ${{\rm NA}_{{\rm cond}}}$. Here we assumed ${{\rm NA}_{{\rm cond}}} = 0.045$, which is in the typical range of common-path WF QPI.

4. EXPERIMENTAL DEMONSTRATION OF CONFOCAL QPI OF TISSUE SECTIONS

For our study, multiorgan tumor tissue microarrays (TMAs) were purchased from US Biomax Inc. (Serial# MC246b). The TMAs contained multiple organ tumor and matched adjacent tissue cores. Each core measured 1.5 mm in diameter and 5 µm in thickness. For phase imaging, an unstained tissue was ordered on a reflective glass slide (Deposition Research Lab, Inc., St. Charles, Missouri), deparaffinized in xylene, and subsequently coverslipped using Permount (Fisher Scientific) as mounting medium. For comparison, a consecutive H&E-stained tissue section was obtained as well and imaged on a slide scanner (NanoZoomer 2.0 HT, Hamamatsu Photonics).

Confocal QPI was performed on a commercial confocal microscope (A1R, Nikon). To this end, we implemented SOH, a recently introduced holographic modality of confocal QPI (see Ref. [44] for details). This form of confocal QPI can be easily implemented in common confocal microscopes because modifications of the microscope hardware are not needed. Briefly, the sample was imaged in transflection geometry with the confocal focus placed at the reflective layer of the glass slide, at 561 nm wavelength and 2.3% power setting [Fig. 3(a)]. At each position $(x,y)$ on the sample, the local refractive index imparts a specific phase shift on the reflected beam that carries information on the local tissue structure. A Mirau interference objective (CF IC EPI Plan DI ${20} \times$, 0.4 NA, Nikon) detected this phase shift by interfering the reflected beam from the sample, ${U_{\rm S}}$, with an internally generated reference beam, ${U_{\rm R}}$. While the focus was raster-scanned across the sample, the sample was vertically vibrated at sub-micrometer amplitude with a piezo nanopositioner stage (Nano-Z100, Mad City Labs Inc.). This modulation introduced a sinusoidal-wave phase modulation across the image that encoded amplitude and phase of the sample beam, ${U_{\rm S}}$, across the pixels, that is, holographically. Since the combined beam was of the same wavelength as the laser emission, an 80:20 beam splitter was inserted in lieu of a dichroic beam splitter, all filters were removed in front of the detector, and the pinhole was fully opened. Figure 3(b) shows an example confocal hologram as obtained with the TMA, exhibiting a finely spaced fringe pattern [Fig. 3(c)]. Fourier transform (FT) of the hologram reveals multiple direct and conjugate terms as a result of the sinusoidal phase modulation [Fig. 3(d)]. Spatial filtering the direct terms “1” and “2” and performing an inverse FT allowed for reconstruction of quantitative amplitude and phase images. The amplitude image shows only weak contrast [Fig. 3(e)], which confirms that the coverslipped tissue section mainly behaves as a phase-contrast object. The phase image reveals rich information on the tissue structure with micrometer-scale spatial resolution and free of halo [Fig. 3(f)].

 figure: Fig. 3.

Fig. 3. Confocal phase imaging of tissue sections mounted on reflective glass slides with sinusoidal-wave synthetic optical holography. (a) Schematic. (b) Example hologram and (c) digital zoom. (d) Fourier transform of the hologram. (e), (f) Reconstructed amplitude and phase images. Scale bar is 50 µm.

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Mosaic imaging and subsequent stitching was applied to acquire phase images of entire tissue cores. To this end, individual tiles were acquired, each covering an area of ${210}\;{\unicode{x00B5}{\rm m}} \times {210}\;{\unicode{x00B5}{\rm m}}$ of the sample, allowing 10% overlap between tiles. Phase drift as a result of interferometer instabilities was removed by subtraction of horizontal line averages, and contrast between tiles was equalized by applying limited high-pass filtering to each tile (low frequency cutoff was 1/2 of the tile width). The stitched phase image of each tissue core was evaluated to assess the capability of confocal phase microscopy to spatially resolve tissue architecture and to explore how well the contrast of the common H&E stain was reproduced. Digital zooms are shown to determine cell shape and size, particularly shape, size, and orientation of cell nuclei. For each organ type, phase images of benign and normal adjacent tissue cores are compared to reveal how well disease-induced changes in the tissue morphology can be recognized on core and cellular level.

5. EXPERIMENTAL RESULTS

For normal adjacent breast tissue, the overall tissue architecture was correctly reproduced [Fig. 4(a)]. Benign epithelium appeared with large phase shift (dark gray shade) with respect to the substrate, revealing the organization of breast lobules in the typically well-organized form of clustered grapes. Stroma exhibited lower phase shift than epithelium (lighter gray), and adipocytes appeared as round disks of zero phase shift as a result of the removal of fatty tissue during sample preparation. A digital zoom [Fig. 4(b)] showed a rich level of detail of the tissue morphology. Individual lobules were well resolved. Stroma was nicely outlined, revealing a host of fibroblasts (F), blood vessels (B), and secretions (S). Loose stroma appeared with a lighter shading than dense stroma. Further digital zoom on an individual lobule [Fig. 4(c)] revealed that cells maintained their orientation. Nuclear features of epithelial cells were resolved well in shape; however, the contrast between nucleus and cytoplasm was weaker than in H&E. The edge of the basement membrane was clearly visible in some areas of the phase image, comparable to H&E. Digital zoom on stroma [Fig. 4(d)] resolved the spindly shape of fibroblasts with high resolution.

 figure: Fig. 4.

Fig. 4. Confocal phase images of (a)–(d) normal adjacent breast and (e)–(h) histologic specimen corresponding to phase. Confocal phase imaging of (i)–(l) invasive ductal carcinoma and (m)–(p) corresponding H&E image. Scale bar applies to entire column (core-level, medium zoom, high zoom setting). Numerical aperture (NA): 0.40 (confocal phase), 0.75 (H&E).

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Confocal phase imaging of invasive ductal carcinoma [Fig. 4(i)] revealed a changed tissue morphology that was clearly different to the normal adjacent section in Fig. 4(a). Digital zoom [Fig. 4(j)] showed fibrous stroma. Individual tumor nests were also present in the image but were difficult to discern because of similar shades of gray as the surrounding stroma at core-level view. Nevertheless, further zoom into the image [Figs. 4(k) and 4(l)] revealed individual tumor cells recognizable by their outline. Loss of polarity could be recognized as well as enlarged nuclei of different sizes and with prominent, often multiple nucleoli (large phase shift, dark spots).

 figure: Fig. 5.

Fig. 5. Confocal phase images of (a), (b) cancer adjacent prostate and (c), (d) corresponding H&E image. Confocal phase imaging of (e), (f) adenocarcinoma lesion in prostate and (g), (h) corresponding H&E image. Numerical aperture (NA): 0.40 (confocal phase), 0.75 (H&E).

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With cancer-adjacent prostate tissue, ducts and acini were clearly revealed on a core-level view [Fig. 5(a)]. Epithelial cell layers appeared slightly brighter than the surrounding stroma (arrowheads). Zoom on part of the duct revealed prostatic intraepithelial neoplasia (PIN) seen as cell stacking [Fig. 5(b)]. Cell polarization was clearly visible, and the basement membrane was outlined well. Cell nuclei appear darker than the surrounding cytoplasm. In comparison, prostate with adenocarcinoma lesions showed large central lumen filled with secretion and cell debris [Fig. 5(e)]. The excess of glands in the surrounding tissue is evident when compared with the cancer-adjacent section. Stroma fibers and lymphocytes can be recognized by dark color. Several glands looked disorganized. Zoom on two individual glands revealed round and enlarged nuclei with prominent nucleoli [Fig. 5(f)]. Generally, the contrast between cell nuclei and cytoplasm was lower compared to H&E; however, microscopic digital zoom on individual glands allowed us to clearly discern nuclei owing the high spatial detailed provided by the phase images. Again, just as for breast tissue, the image quality allows an assessment of key features in both PIN and cancer.

While prostate and breast constitute the largest single cancers in men and women, we also evaluated phase contrast images for stomach and cerebrum (see Supplement 1). These examples demonstrate the broad applicability of our method and utility for a variety of tissue morphologies.

6. DISCUSSION AND CONCLUSION

We have demonstrated high-resolution label-free imaging of tissue sections with confocal QPI as a means to provide morphological detail regarding disease state. This study presents a significant step for QPI towards digital pathology by providing two advances. The first major result is that confocal QPI can reproduce the spatial level of detail and shading (in gray scale) with similar quality as compared to imaging of H&E stained slides, the gold standard in histopathology. This is because confocal QPI is free of halo and shade-off, thus solving a common problem of WF QPI. Halo-free QPI maps facilitate human interpretation of tissue and are better aligned to common practice. We note that the QPI data were acquired with a lower resolving microscope objective (0.40 NA) compared to the H&E images (0.75 NA), leading to a small reduction in spatial resolution. As indicated by the FDTD simulations, confocal QPI can resolve subcellular details with high resolution if combined with high-NA objectives (commercially available), which should be explored further. As typical in QPI, refractive index is relatively nonspecific, leading to weaker contrast than with H&E staining. This is, however, partially compensated by the ability of QPI to provide quantitative measurements. Recent research shows exciting ways of how QPI data can be converted into strong and meaningful image contrast by applying deep-learning algorithms [5355]. In this regard, our technique could lead to increased spatial detail (resolution advantage) and robustness (halo-free) in such computational staining. As a further way to increase specificity, confocal QPI is in principle amenable for coregistered QPI and fluorescence imaging [5658], which we expect should be supported by most commercial systems with spectral detector units.

Further, the results of this study demonstrate for the first time the value of FDTD calculations to obtain an accurate prediction of QPI performance for tissue imaging. First, generic QPI system can be modeled by rigorously solving Maxwell’s equations. Particularly, it is not required to develop specific analytical models to describe image contrast, thus providing a flexible simulation platform. Second, arbitrary samples can be simulated where multiple scattering and resonance effects are taken into account. The attainable size and detail are in principle only limited by the memory and computational cost of the FDTD method. The combination of FDTD with volume electron microscopy [59,60] offers the opportunity to simulate 3D tissue phantoms reflecting different disease states and so to test the diagnostic potential of QPI methods. In this case, the volumetric dimension of tissue morphology can be properly considered. Specifically for the case of confocal QPI, calculations predicted improved image quality in terms of spatial resolution and contrast when compared to traditional WF methods with low condenser settings. Further work could address the question how multiple scattering and speckle affect phase imaging in case of relatively thick and heterogeneous samples such as with standard histology slides.

While confocal microscopy is a widely spread technique, confocal QPI is not trivial: careful alignment of the optical setup, phase instabilities caused by environmental influences, and slow measurements are typically associated with interferometry-based confocal phase imaging. Our implementation based on SOH addresses these challenges by providing rapid, alignment-free QPI on a beam scanning commercial confocal instrument with a Mirau interference objective. Confocal QPI holds promise to simplify histopathologic analysis by removing the need for sample staining. Synergy with IR microscopy could lead to transformative imaging modalities where high-resolution morphology data provided by confocal QPI are combined with highly specific but low-resolution recognition of cell type and disease state provided by IR microscopy.

The practicality of confocal QPI for application in histopathology is determined by a set of factors. First, the image acquisition time of confocal QPI is determined by the number of pixels, which in turn is set by the resolution and imaging area, and the time for each pixel, which is set by one of two limiting factors: the speed of scanning or required integration time at each pixel. Integration time may be improved by prioritizing the single detector used to acquire the signal. Scanning speed may be improved with scanner technology like resonant scanners. The serial scanning scheme inherent to our technique presents a unique opportunity to adaptively and inhomogeneously vary the scanning density and resolution to minimize data required. Application requirements set sensitivity, resolution, and image size after which speed may be optimized for high throughput by these various routes apparent at this time. The implementation of confocal QPI presented in this work employs nonresonant galvo beam scanning (as opposed to sample scanning) to uniformly raster-scan the focus across the sample and thus allows for fast imaging times on the order seconds. Each image tile was acquired at less than 1 mW laser power, ${2048} \times {2048}\;{\rm pixels}$, and at $1\;\unicode{x00B5} {\rm s}$ dwell time. Mosaic imaging time was about 14 min for each tissue core (1.5 mm diameter). In comparison, WF QPI offers imaging times on the order of tens of seconds per core, i.e., one to two magnitudes faster (e.g., Ref. [61]). Optimization of confocal QPI may feasibly reach the speed of WF QPI. Significant speed improvement is available with resonant scanners, management of laser power and low noise detectors, and adaptive sampling and resolution strategies.

Second, variation in refractive index among the different subcellular constituents is weak; thus, a sufficiently high signal-to-noise ratio (SNR) is needed. Confocal QPI is in principle capable of reaching high SNR owing to laser illumination and light detection with a photo detector, significantly reducing shot noise. Our current implementation of confocal QPI allows for a precision in phase measurement of 13 mrad (equivalent to a detection sensitivity of $\lambda /483$ in the optical path length) [44], which is already sufficient to resolve morphology in tissue sections of standard (5 µm) thickness. We anticipate further improvements in terms of spatial resolution by using higher NA objectives, higher SNR by improved isolation of the system from environmental influences, and improved imaging times. The presented FDTD method can be utilized to model the impact of such modifications on the image quality and guide instrument development.

Funding

National Institutes of Health (R01CA197516); H2020 Marie Skłodowska-Curie Actions (655888, SYNTOH); Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana-Champaign (Postdoctoral Fellowship).

Acknowledgment

We sincerely thank A. J. Cyphersmith, G. A. Fried, M. Sivaguru, and K. A. Janssen at the Core Facilities at the Carl R. Woese Institute for Genomic Biology for access and training on the Zeiss LSM 710 confocal microscope and NanoZoomer slide scanner. We also thank W. Mei at the College of Veterinary Medicine, University of Illinois at Urbana–Champaign for access and training on the Nikon A1R confocal microscope. M. S. acknowledges support by the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie. T. P. W. was supported by a Beckman Institute Postdoctoral Fellowship from the Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana–Champaign.

Disclosures

M. S. and P. S. C. are authors of U.S. patent 9,213,313.

 

See Supplement 1 for supporting content.

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References

  • View by:

  1. V. Kumar and A. K. Abbas, and J. C. Aster, eds., Robbins and Cotran Pathologic Basis of Disease, 9th ed. (Elsevier/Saunders, 2015).
  2. H. Majeed, S. Sridharan, M. Mir, L. Ma, E. Min, W. Jung, and G. Popescu, “Quantitative phase imaging for medical diagnosis,” J. Biophoton. 10, 177–205 (2017).
    [Crossref]
  3. M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9, 1771–1791 (2014).
    [Crossref]
  4. C. J. Mann, L. Yu, C.-M. Lo, and M. K. Kim, “High-resolution quantitative phase-contrast microscopy by digital holography,” Opt. Express 13, 8693–8698 (2005).
    [Crossref]
  5. G. Popescu, L. P. Deflores, J. C. Vaughan, K. Badizadegan, H. Iwai, R. R. Dasari, and M. S. Feld, “Fourier phase microscopy for investigation of biological structures and dynamics,” Opt. Lett. 29, 2503–2505 (2004).
    [Crossref]
  6. S. Bernet, A. Jesacher, S. Fürhapter, C. Maurer, and M. Ritsch-Marte, “Quantitative imaging of complex samples by spiral phase contrast microscopy,” Opt. Express 14, 3792–3805 (2006).
    [Crossref]
  7. G. Popescu, Y. Park, N. Lue, C. Best-Popescu, L. Deflores, R. R. Dasari, M. S. Feld, and K. Badizadegan, “Optical imaging of cell mass and growth dynamics,” Am. J. Physiol. Cell Physiol. 295, C538–C544 (2008).
    [Crossref]
  8. B. Kemper and G. von Bally, “Digital holographic microscopy for live cell applications and technical inspection,” Appl. Opt. 47, A52–A61 (2008).
    [Crossref]
  9. N. T. Shaked, M. T. Rinehart, and A. Wax, “Dual-interference-channel quantitative-phase microscopy of live cell dynamics,” Opt. Lett. 34, 767–769 (2009).
    [Crossref]
  10. K. Lee, K. Kim, J. Jung, J. Heo, S. Cho, S. Lee, G. Chang, Y. Jo, H. Park, and Y. Park, “Quantitative phase imaging techniques for the study of cell pathophysiology: from principles to applications,” Sensors 13, 4170–4191 (2013).
    [Crossref]
  11. Y. Cotte, F. Toy, P. Jourdain, N. Pavillon, D. Boss, P. Magistretti, P. Marquet, and C. Depeursinge, “Marker-free phase nanoscopy,” Nat. Photonics 7, 113–117 (2013).
    [Crossref]
  12. P. Marquet, C. Depeursinge, and P. J. Magistretti, “Review of quantitative phase-digital holographic microscopy: promising novel imaging technique to resolve neuronal network activity and identify cellular biomarkers of psychiatric disorders,” Neurophotonics 1, 020901 (2014).
    [Crossref]
  13. B. Bhaduri, C. Edwards, H. Pham, R. Zhou, T. H. Nguyen, L. L. Goddard, and G. Popescu, “Diffraction phase microscopy: principles and applications in materials and life sciences,” Adv. Opt. Photon. 6, 57–119 (2014).
    [Crossref]
  14. Y. Park, C. Depeursinge, and G. Popescu, “Quantitative phase imaging in biomedicine,” Nat. Photonics 12, 578–589 (2018).
    [Crossref]
  15. Z. Wang, G. Popescu, K. V. Tangella, and A. Balla, “Tissue refractive index as marker of disease,” J. Biomed. Opt. 16, 116017 (2011).
    [Crossref]
  16. S. Sridharan, V. Macias, K. Tangella, A. Kajdacsy-Balla, and G. Popescu, “Prediction of prostate cancer recurrence using quantitative phase imaging,” Sci. Rep. 5, 9976 (2015).
    [Crossref]
  17. S. Uttam, H. V. Pham, J. LaFace, B. Leibowitz, J. Yu, R. E. Brand, D. J. Hartman, and Y. Liu, “Early prediction of cancer progression by depth-resolved nanoscale mapping of nuclear architecture from unstained tissue specimens,” Cancer Res. 75, 4718–4727 (2015).
    [Crossref]
  18. M. E. Kandel, S. Sridharan, J. Liang, Z. Luo, K. Han, V. Macias, A. Shah, R. Patel, K. Tangella, A. Kajdacsy-Balla, G. Guzman, and G. Popescu, “Label-free tissue scanner for colorectal cancer screening,” J. Biomed. Opt. 22, 066016 (2017).
    [Crossref]
  19. P. Wang, R. Bista, R. Bhargava, R. E. Brand, and Y. Liu, “Spatial-domain low-coherence quantitative phase microscopy for cancer diagnosis,” Opt. Lett. 35, 2840–2842 (2010).
    [Crossref]
  20. H. Majeed, M. E. Kandel, K. Han, Z. Luo, V. Macias, K. Tangella, A. Balla, and G. Popescu, “Breast cancer diagnosis using spatial light interference microscopy,” J. Biomed. Opt. 20, 111210 (2015).
    [Crossref]
  21. C. Maurer, A. Jesacher, S. Bernet, and M. Ritsch-Marte, “Phase contrast microscopy with full numerical aperture illumination,” Opt. Express 16, 19821–19829 (2008).
    [Crossref]
  22. C. Edwards, B. Bhaduri, T. Nguyen, B. G. Griffin, H. Pham, T. Kim, G. Popescu, and L. L. Goddard, “Effects of spatial coherence in diffraction phase microscopy,” Opt. Express 22, 5133–5146 (2014).
    [Crossref]
  23. T. H. Nguyen, C. Edwards, L. L. Goddard, and G. Popescu, “Quantitative phase imaging with partially coherent illumination,” Opt. Lett. 39, 5511–5514 (2014).
    [Crossref]
  24. P. Hosseini, R. Zhou, Y.-H. Kim, C. Peres, A. Diaspro, C. Kuang, Z. Yaqoob, and P. T. C. So, “Pushing phase and amplitude sensitivity limits in interferometric microscopy,” Opt. Lett. 41, 1656–1659 (2016).
    [Crossref]
  25. Z. Wang, L. Millet, M. Mir, H. Ding, S. Unarunotai, J. Rogers, M. U. Gillette, and G. Popescu, “Spatial light interference microscopy (SLIM),” Opt. Express 19, 1016–1026 (2011).
    [Crossref]
  26. Y. Choi, T. D. Yang, K. J. Lee, and W. Choi, “Full-field and single-shot quantitative phase microscopy using dynamic speckle illumination,” Opt. Lett. 36, 2465–2467 (2011).
    [Crossref]
  27. B. Bhaduri, H. Pham, M. Mir, and G. Popescu, “Diffraction phase microscopy with white light,” Opt. Lett. 37, 1094–1096 (2012).
    [Crossref]
  28. G. E. Sommargren, “Optical heterodyne profilometry,” Appl. Opt. 20, 610–618 (1981).
    [Crossref]
  29. H. J. Matthews, D. K. Hamilton, and C. J. R. Sheppard, “Surface profiling by phase-locked interferometry,” Appl. Opt. 25, 2372–2374 (1986).
    [Crossref]
  30. G. Barbastathis, M. Balberg, and D. J. Brady, “Confocal microscopy with a volume holographic filter,” Opt. Lett. 24, 811–813 (1999).
    [Crossref]
  31. J. V. Knuuttila, P. T. Tikka, and M. M. Salomaa, “Scanning Michelson interferometer for imaging surface acoustic wave fields,” Opt. Lett. 25, 613–615 (2000).
    [Crossref]
  32. J. Graebner, “Optical scanning interferometer for dynamic imaging of high-frequency surface motion,” IEEE Ultrasonics Symposium (Cat. No.00CH37121), San Juan, Puerto Rico, USA, (2000, IEEE), pp. 733–736.
  33. G. G. Fattinger and P. T. Tikka, “Modified Mach–Zender laser interferometer for probing bulk acoustic waves,” Appl. Phys. Lett. 79, 290–292 (2001).
    [Crossref]
  34. N. Lue, W. Choi, K. Badizadegan, R. R. Dasari, M. S. Feld, and G. Popescu, “Confocal diffraction phase microscopy of live cells,” Opt. Lett. 33, 2074–2076 (2008).
    [Crossref]
  35. A. S. Goy and D. Psaltis, “Digital confocal microscope,” Opt. Express 20, 22720–22727 (2012).
    [Crossref]
  36. A. S. Goy, M. Unser, and D. Psaltis, “Multiple contrast metrics from the measurements of a digital confocal microscope,” Biomed. Opt. Express 4, 001091 (2013).
    [Crossref]
  37. C. Joo, T. Akkin, B. Cense, B. H. Park, and J. F. de Boer, “Spectral-domain optical coherence phase microscopy for quantitative phase-contrast imaging,” Opt. Lett. 30, 2131–2133 (2005).
    [Crossref]
  38. M. A. Choma, A. K. Ellerbee, C. Yang, T. L. Creazzo, and J. A. Izatt, “Spectral-domain phase microscopy,” Opt. Lett. 30, 1162–1164 (2005).
    [Crossref]
  39. M. Schnell, P. S. Carney, and R. Hillenbrand, “Synthetic optical holography for rapid nanoimaging,” Nat. Commun. 5, 3499 (2014).
    [Crossref]
  40. M. Schnell, M. J. Perez-Roldan, P. S. Carney, and R. Hillenbrand, “Quantitative confocal phase imaging by synthetic optical holography,” Opt. Express 22, 15267–15276 (2014).
    [Crossref]
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2020 (1)

Y. N. Nygate, M. Levi, S. K. Mirsky, N. A. Turko, M. Rubin, I. Barnea, G. Dardikman-Yoffe, M. Haifler, A. Shalev, and N. T. Shaked, “Holographic virtual staining of individual biological cells,” Proc. Natl. Acad. Sci. USA 117, 9223–9231 (2020).
[Crossref]

2019 (2)

2018 (4)

H. Majeed, T. H. Nguyen, M. E. Kandel, A. Kajdacsy-Balla, and G. Popescu, “Label-free quantitative evaluation of breast tissue using spatial light interference microscopy (SLIM),” Sci. Rep. 8, 6875 (2018).
[Crossref]

Y. Rivenson, Y. Zhang, H. Günaydın, D. Teng, and A. Ozcan, “Phase recovery and holographic image reconstruction using deep learning in neural networks,” Light Sci. Appl. 7, 17141 (2018).
[Crossref]

I. Y. Yanina, E. N. Lazareva, and V. V. Tuchin, “Refractive index of adipose tissue and lipid droplet measured in wide spectral and temperature ranges,” Appl. Opt. 57, 4839–4848 (2018).
[Crossref]

Y. Park, C. Depeursinge, and G. Popescu, “Quantitative phase imaging in biomedicine,” Nat. Photonics 12, 578–589 (2018).
[Crossref]

2017 (3)

H. Majeed, S. Sridharan, M. Mir, L. Ma, E. Min, W. Jung, and G. Popescu, “Quantitative phase imaging for medical diagnosis,” J. Biophoton. 10, 177–205 (2017).
[Crossref]

M. E. Kandel, S. Sridharan, J. Liang, Z. Luo, K. Han, V. Macias, A. Shah, R. Patel, K. Tangella, A. Kajdacsy-Balla, G. Guzman, and G. Popescu, “Label-free tissue scanner for colorectal cancer screening,” J. Biomed. Opt. 22, 066016 (2017).
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S. Chowdhury, W. J. Eldridge, A. Wax, and J. A. Izatt, “Structured illumination multimodal 3D-resolved quantitative phase and fluorescence sub-diffraction microscopy,” Biomed. Opt. Express 8, 2496–2518 (2017).
[Crossref]

2016 (4)

B. Titze and C. Genoud, “Volume scanning electron microscopy for imaging biological ultrastructure: volume scanning electron microscopy,” Biol. Cell 108, 307–323 (2016).
[Crossref]

P. Y. Liu, L. K. Chin, W. Ser, H. F. Chen, C.-M. Hsieh, C.-H. Lee, K.-B. Sung, T. C. Ayi, P. H. Yap, B. Liedberg, K. Wang, T. Bourouina, and Y. Leprince-Wang, “Cell refractive index for cell biology and disease diagnosis: past, present and future,” Lab Chip 16, 634–644 (2016).
[Crossref]

C. Liu, S. Knitter, Z. Cong, I. Sencan, H. Cao, and M. A. Choma, “High-speed line-field confocal holographic microscope for quantitative phase imaging,” Opt. Express 24, 9251–9265 (2016).
[Crossref]

P. Hosseini, R. Zhou, Y.-H. Kim, C. Peres, A. Diaspro, C. Kuang, Z. Yaqoob, and P. T. C. So, “Pushing phase and amplitude sensitivity limits in interferometric microscopy,” Opt. Lett. 41, 1656–1659 (2016).
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2015 (5)

H. Majeed, M. E. Kandel, K. Han, Z. Luo, V. Macias, K. Tangella, A. Balla, and G. Popescu, “Breast cancer diagnosis using spatial light interference microscopy,” J. Biomed. Opt. 20, 111210 (2015).
[Crossref]

S. Sridharan, V. Macias, K. Tangella, A. Kajdacsy-Balla, and G. Popescu, “Prediction of prostate cancer recurrence using quantitative phase imaging,” Sci. Rep. 5, 9976 (2015).
[Crossref]

S. Uttam, H. V. Pham, J. LaFace, B. Leibowitz, J. Yu, R. E. Brand, D. J. Hartman, and Y. Liu, “Early prediction of cancer progression by depth-resolved nanoscale mapping of nuclear architecture from unstained tissue specimens,” Cancer Res. 75, 4718–4727 (2015).
[Crossref]

A. Kremer, S. Lippens, S. Bartunkova, B. Asselbergh, C. Blanpain, M. Fendrych, A. Goossens, M. Holt, S. Janssens, M. Krols, J.-C. Larsimont, C. McGUIRE, M. Nowack, X. Saelens, A. Schertel, B. Schepens, M. Slezak, V. Timmerman, C. Theunis, R. Van Brempt, Y. Visser, and C. Guérin, “Developing 3D SEM in a broad biological context: 3D SEM,” J. Microsc. 259, 80–96 (2015).
[Crossref]

S. Chowdhury, W. J. Eldridge, A. Wax, and J. A. Izatt, “Spatial frequency-domain multiplexed microscopy for simultaneous, single-camera, one-shot, fluorescent, and quantitative-phase imaging,” Opt. Lett. 40, 4839–4842 (2015).
[Crossref]

2014 (9)

B. Deutsch, M. Schnell, R. Hillenbrand, and P. S. Carney, “Synthetic optical holography with nonlinear-phase reference,” Opt. Express 22, 26621–26634 (2014).
[Crossref]

M. Schnell, P. S. Carney, and R. Hillenbrand, “Synthetic optical holography for rapid nanoimaging,” Nat. Commun. 5, 3499 (2014).
[Crossref]

M. Schnell, M. J. Perez-Roldan, P. S. Carney, and R. Hillenbrand, “Quantitative confocal phase imaging by synthetic optical holography,” Opt. Express 22, 15267–15276 (2014).
[Crossref]

C. Liu, S. Marchesini, and M. K. Kim, “Quantitative phase-contrast confocal microscope,” Opt. Express 22, 17830–17839 (2014).
[Crossref]

P. Marquet, C. Depeursinge, and P. J. Magistretti, “Review of quantitative phase-digital holographic microscopy: promising novel imaging technique to resolve neuronal network activity and identify cellular biomarkers of psychiatric disorders,” Neurophotonics 1, 020901 (2014).
[Crossref]

B. Bhaduri, C. Edwards, H. Pham, R. Zhou, T. H. Nguyen, L. L. Goddard, and G. Popescu, “Diffraction phase microscopy: principles and applications in materials and life sciences,” Adv. Opt. Photon. 6, 57–119 (2014).
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M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9, 1771–1791 (2014).
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C. Edwards, B. Bhaduri, T. Nguyen, B. G. Griffin, H. Pham, T. Kim, G. Popescu, and L. L. Goddard, “Effects of spatial coherence in diffraction phase microscopy,” Opt. Express 22, 5133–5146 (2014).
[Crossref]

T. H. Nguyen, C. Edwards, L. L. Goddard, and G. Popescu, “Quantitative phase imaging with partially coherent illumination,” Opt. Lett. 39, 5511–5514 (2014).
[Crossref]

2013 (3)

K. Lee, K. Kim, J. Jung, J. Heo, S. Cho, S. Lee, G. Chang, Y. Jo, H. Park, and Y. Park, “Quantitative phase imaging techniques for the study of cell pathophysiology: from principles to applications,” Sensors 13, 4170–4191 (2013).
[Crossref]

Y. Cotte, F. Toy, P. Jourdain, N. Pavillon, D. Boss, P. Magistretti, P. Marquet, and C. Depeursinge, “Marker-free phase nanoscopy,” Nat. Photonics 7, 113–117 (2013).
[Crossref]

A. S. Goy, M. Unser, and D. Psaltis, “Multiple contrast metrics from the measurements of a digital confocal microscope,” Biomed. Opt. Express 4, 001091 (2013).
[Crossref]

2012 (2)

2011 (4)

2010 (1)

2009 (1)

2008 (4)

2006 (2)

2005 (3)

2004 (1)

2001 (1)

G. G. Fattinger and P. T. Tikka, “Modified Mach–Zender laser interferometer for probing bulk acoustic waves,” Appl. Phys. Lett. 79, 290–292 (2001).
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2000 (1)

1999 (1)

1994 (1)

1993 (1)

Y. Clermont, L. Xia, A. Rambourg, J. D. Turner, and L. Hermo, “Structure of the Golgi apparatus in stimulated and nonstimulated acinar cells of mammary glands of the rat,” Anat. Rec. 237, 308–317 (1993).
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1986 (1)

1981 (2)

G. E. Sommargren, “Optical heterodyne profilometry,” Appl. Opt. 20, 610–618 (1981).
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Asselbergh, B.

A. Kremer, S. Lippens, S. Bartunkova, B. Asselbergh, C. Blanpain, M. Fendrych, A. Goossens, M. Holt, S. Janssens, M. Krols, J.-C. Larsimont, C. McGUIRE, M. Nowack, X. Saelens, A. Schertel, B. Schepens, M. Slezak, V. Timmerman, C. Theunis, R. Van Brempt, Y. Visser, and C. Guérin, “Developing 3D SEM in a broad biological context: 3D SEM,” J. Microsc. 259, 80–96 (2015).
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P. Y. Liu, L. K. Chin, W. Ser, H. F. Chen, C.-M. Hsieh, C.-H. Lee, K.-B. Sung, T. C. Ayi, P. H. Yap, B. Liedberg, K. Wang, T. Bourouina, and Y. Leprince-Wang, “Cell refractive index for cell biology and disease diagnosis: past, present and future,” Lab Chip 16, 634–644 (2016).
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Backman, V.

I. R. Çapoğlu, J. D. Rogers, A. Taflove, and V. Backman, “The microscope in a computer: image synthesis from three-dimensional full-vector solutions of Maxwell’s equations at the nanometer scale,” in Progress in Optics, E. Wolf, ed. (Elsevier, 2012), Vol. 57.

Badizadegan, K.

Baker, M. J.

M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9, 1771–1791 (2014).
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Balberg, M.

Balla, A.

H. Majeed, M. E. Kandel, K. Han, Z. Luo, V. Macias, K. Tangella, A. Balla, and G. Popescu, “Breast cancer diagnosis using spatial light interference microscopy,” J. Biomed. Opt. 20, 111210 (2015).
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Z. Wang, G. Popescu, K. V. Tangella, and A. Balla, “Tissue refractive index as marker of disease,” J. Biomed. Opt. 16, 116017 (2011).
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Barnea, I.

Y. N. Nygate, M. Levi, S. K. Mirsky, N. A. Turko, M. Rubin, I. Barnea, G. Dardikman-Yoffe, M. Haifler, A. Shalev, and N. T. Shaked, “Holographic virtual staining of individual biological cells,” Proc. Natl. Acad. Sci. USA 117, 9223–9231 (2020).
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A. Kremer, S. Lippens, S. Bartunkova, B. Asselbergh, C. Blanpain, M. Fendrych, A. Goossens, M. Holt, S. Janssens, M. Krols, J.-C. Larsimont, C. McGUIRE, M. Nowack, X. Saelens, A. Schertel, B. Schepens, M. Slezak, V. Timmerman, C. Theunis, R. Van Brempt, Y. Visser, and C. Guérin, “Developing 3D SEM in a broad biological context: 3D SEM,” J. Microsc. 259, 80–96 (2015).
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M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9, 1771–1791 (2014).
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Best-Popescu, C.

G. Popescu, Y. Park, N. Lue, C. Best-Popescu, L. Deflores, R. R. Dasari, M. S. Feld, and K. Badizadegan, “Optical imaging of cell mass and growth dynamics,” Am. J. Physiol. Cell Physiol. 295, C538–C544 (2008).
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Bhargava, R.

M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9, 1771–1791 (2014).
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P. Wang, R. Bista, R. Bhargava, R. E. Brand, and Y. Liu, “Spatial-domain low-coherence quantitative phase microscopy for cancer diagnosis,” Opt. Lett. 35, 2840–2842 (2010).
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Bista, R.

Blanpain, C.

A. Kremer, S. Lippens, S. Bartunkova, B. Asselbergh, C. Blanpain, M. Fendrych, A. Goossens, M. Holt, S. Janssens, M. Krols, J.-C. Larsimont, C. McGUIRE, M. Nowack, X. Saelens, A. Schertel, B. Schepens, M. Slezak, V. Timmerman, C. Theunis, R. Van Brempt, Y. Visser, and C. Guérin, “Developing 3D SEM in a broad biological context: 3D SEM,” J. Microsc. 259, 80–96 (2015).
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Boss, D.

Y. Cotte, F. Toy, P. Jourdain, N. Pavillon, D. Boss, P. Magistretti, P. Marquet, and C. Depeursinge, “Marker-free phase nanoscopy,” Nat. Photonics 7, 113–117 (2013).
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Bourouina, T.

P. Y. Liu, L. K. Chin, W. Ser, H. F. Chen, C.-M. Hsieh, C.-H. Lee, K.-B. Sung, T. C. Ayi, P. H. Yap, B. Liedberg, K. Wang, T. Bourouina, and Y. Leprince-Wang, “Cell refractive index for cell biology and disease diagnosis: past, present and future,” Lab Chip 16, 634–644 (2016).
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Brady, D. J.

Brand, R. E.

S. Uttam, H. V. Pham, J. LaFace, B. Leibowitz, J. Yu, R. E. Brand, D. J. Hartman, and Y. Liu, “Early prediction of cancer progression by depth-resolved nanoscale mapping of nuclear architecture from unstained tissue specimens,” Cancer Res. 75, 4718–4727 (2015).
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P. Wang, R. Bista, R. Bhargava, R. E. Brand, and Y. Liu, “Spatial-domain low-coherence quantitative phase microscopy for cancer diagnosis,” Opt. Lett. 35, 2840–2842 (2010).
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M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9, 1771–1791 (2014).
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Cao, H.

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I. R. Çapoğlu, J. D. Rogers, A. Taflove, and V. Backman, “The microscope in a computer: image synthesis from three-dimensional full-vector solutions of Maxwell’s equations at the nanometer scale,” in Progress in Optics, E. Wolf, ed. (Elsevier, 2012), Vol. 57.

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Cense, B.

Chang, G.

K. Lee, K. Kim, J. Jung, J. Heo, S. Cho, S. Lee, G. Chang, Y. Jo, H. Park, and Y. Park, “Quantitative phase imaging techniques for the study of cell pathophysiology: from principles to applications,” Sensors 13, 4170–4191 (2013).
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P. Y. Liu, L. K. Chin, W. Ser, H. F. Chen, C.-M. Hsieh, C.-H. Lee, K.-B. Sung, T. C. Ayi, P. H. Yap, B. Liedberg, K. Wang, T. Bourouina, and Y. Leprince-Wang, “Cell refractive index for cell biology and disease diagnosis: past, present and future,” Lab Chip 16, 634–644 (2016).
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P. Y. Liu, L. K. Chin, W. Ser, H. F. Chen, C.-M. Hsieh, C.-H. Lee, K.-B. Sung, T. C. Ayi, P. H. Yap, B. Liedberg, K. Wang, T. Bourouina, and Y. Leprince-Wang, “Cell refractive index for cell biology and disease diagnosis: past, present and future,” Lab Chip 16, 634–644 (2016).
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K. Lee, K. Kim, J. Jung, J. Heo, S. Cho, S. Lee, G. Chang, Y. Jo, H. Park, and Y. Park, “Quantitative phase imaging techniques for the study of cell pathophysiology: from principles to applications,” Sensors 13, 4170–4191 (2013).
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Choi, Y.

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Y. Clermont, L. Xia, A. Rambourg, J. D. Turner, and L. Hermo, “Structure of the Golgi apparatus in stimulated and nonstimulated acinar cells of mammary glands of the rat,” Anat. Rec. 237, 308–317 (1993).
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Y. Cotte, F. Toy, P. Jourdain, N. Pavillon, D. Boss, P. Magistretti, P. Marquet, and C. Depeursinge, “Marker-free phase nanoscopy,” Nat. Photonics 7, 113–117 (2013).
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Dardikman-Yoffe, G.

Y. N. Nygate, M. Levi, S. K. Mirsky, N. A. Turko, M. Rubin, I. Barnea, G. Dardikman-Yoffe, M. Haifler, A. Shalev, and N. T. Shaked, “Holographic virtual staining of individual biological cells,” Proc. Natl. Acad. Sci. USA 117, 9223–9231 (2020).
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Dasari, R. R.

de Boer, J. F.

Deflores, L.

G. Popescu, Y. Park, N. Lue, C. Best-Popescu, L. Deflores, R. R. Dasari, M. S. Feld, and K. Badizadegan, “Optical imaging of cell mass and growth dynamics,” Am. J. Physiol. Cell Physiol. 295, C538–C544 (2008).
[Crossref]

Deflores, L. P.

Depeursinge, C.

Y. Park, C. Depeursinge, and G. Popescu, “Quantitative phase imaging in biomedicine,” Nat. Photonics 12, 578–589 (2018).
[Crossref]

P. Marquet, C. Depeursinge, and P. J. Magistretti, “Review of quantitative phase-digital holographic microscopy: promising novel imaging technique to resolve neuronal network activity and identify cellular biomarkers of psychiatric disorders,” Neurophotonics 1, 020901 (2014).
[Crossref]

Y. Cotte, F. Toy, P. Jourdain, N. Pavillon, D. Boss, P. Magistretti, P. Marquet, and C. Depeursinge, “Marker-free phase nanoscopy,” Nat. Photonics 7, 113–117 (2013).
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M. J. Baker, J. Trevisan, P. Bassan, R. Bhargava, H. J. Butler, K. M. Dorling, P. R. Fielden, S. W. Fogarty, N. J. Fullwood, K. A. Heys, C. Hughes, P. Lasch, P. L. Martin-Hirsch, B. Obinaju, G. D. Sockalingum, J. Sulé-Suso, R. J. Strong, M. J. Walsh, B. R. Wood, P. Gardner, and F. L. Martin, “Using Fourier transform IR spectroscopy to analyze biological materials,” Nat. Protoc. 9, 1771–1791 (2014).
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Eldridge, W. J.

Ellerbee, A. K.

Fantini, S.

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G. G. Fattinger and P. T. Tikka, “Modified Mach–Zender laser interferometer for probing bulk acoustic waves,” Appl. Phys. Lett. 79, 290–292 (2001).
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Adv. Opt. Photon. (1)

Am. J. Physiol. Cell Physiol. (1)

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S. Chowdhury, W. J. Eldridge, A. Wax, and J. A. Izatt, “Structured illumination multimodal 3D-resolved quantitative phase and fluorescence sub-diffraction microscopy,” Biomed. Opt. Express 8, 2496–2518 (2017).
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J. Biomed. Opt. (3)

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Light Sci. Appl. (2)

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Nat. Protoc. (1)

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Neurophotonics (1)

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M. Schnell, M. J. Perez-Roldan, P. S. Carney, and R. Hillenbrand, “Quantitative confocal phase imaging by synthetic optical holography,” Opt. Express 22, 15267–15276 (2014).
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Supplementary Material (1)

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Figures (5)

Fig. 1.
Fig. 1. Performance prediction of an idealized confocal QPI microscope and comparison to wide-field QPI methods. (a) Virtual imaging system based on FDTD describing transmission-mode confocal QPI. (b) Virtual wide-field QPI. (c) Equally spaced plane waves describing Köhler illumination. (d)–(f) Synthesized quantitative phase images of a phase-object resolution test target at wavelength $\lambda = 561\;{\rm nm}$ for confocal QPI ( ${{\rm NA}_{{\rm cond}}} = {{\rm NA}_{{\rm obj}}} = 0.8$ ), traditional wide-field QPI, and common-path wide-field QPI ( ${{\rm NA}_{{\rm cond}}} = 0.09$ and ${{\rm NA}_{{\rm obj}}} = 0.8$ ). (g), (h) Vertical line profiles across the square of group 9 and the bars of group 10, respectively, as indicated by the arrows in (d)–(f). PML, perfectly matched layer boundary; Bloch, Bloch periodic boundary.
Fig. 2.
Fig. 2. Calculated phase contrast for numerical tissue section models. (a) High-resolution 2D refractive index model adopted from ultrastructural electron microscopy data on breast. (b) Calculated phase images illustrate improved spatial resolution and absence of ringing artifacts with confocal QPI ( ${{\rm NA}_{{\rm cond}}} = 0.09$ and ${{\rm NA}_{{\rm obj}}} = 0.8$ ). (c) Large area, low resolution model of breast tissue constructed from the data in Fig. 3. (d) Calculated phase images illustrate the effect of halo on image contrast that appears with common-path wide-field QPI ( ${{\rm NA}_{{\rm cond}}} = 0.045$ and ${{\rm NA}_{{\rm obj}}} = 0.4$ ). Common-path WF phase data were offset by ${+}0.1$ and ${+}0.5$ rad in (b) and (d).
Fig. 3.
Fig. 3. Confocal phase imaging of tissue sections mounted on reflective glass slides with sinusoidal-wave synthetic optical holography. (a) Schematic. (b) Example hologram and (c) digital zoom. (d) Fourier transform of the hologram. (e), (f) Reconstructed amplitude and phase images. Scale bar is 50 µm.
Fig. 4.
Fig. 4. Confocal phase images of (a)–(d) normal adjacent breast and (e)–(h) histologic specimen corresponding to phase. Confocal phase imaging of (i)–(l) invasive ductal carcinoma and (m)–(p) corresponding H&E image. Scale bar applies to entire column (core-level, medium zoom, high zoom setting). Numerical aperture (NA): 0.40 (confocal phase), 0.75 (H&E).
Fig. 5.
Fig. 5. Confocal phase images of (a), (b) cancer adjacent prostate and (c), (d) corresponding H&E image. Confocal phase imaging of (e), (f) adenocarcinoma lesion in prostate and (g), (h) corresponding H&E image. Numerical aperture (NA): 0.40 (confocal phase), 0.75 (H&E).

Equations (3)

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I Δ φ C F ( x , y ) = n = x , y , z | E n O B J ( x , y ) + e i Δ φ E n R E F | 2 ,
I Δ φ W F ( x , y ) = s x , s y n = x , y , z | E n , s x , s y O B J ( x , y ) + e i Δ φ E n , s x , s y R E F ( x , y ) | 2 ,
φ = a t a n 2 ( I 90 I 270 , I 0 I 180 ) ,

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