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REVIEW 3 major objections 5 minor 75 references

Photon Absorption Remote Sensing (PARS): Comprehensive Absorption Imaging Enabling Label-Free Biomolecule Characterization and Mapping

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read A label-free absorption microscope that records radiative and non-radiative relaxation at two wavelengths can map biomolecules in unstained tissue using Gaussian mixture models and non-negative least squares.

desk verdict An impressive proof-of-concept for label-free biomolecule unmixing from six PARS contrasts, but the linear-mixture model applied to the decay-rate channel is physically shaky and the validation is in-sample. read the letter →

arxiv 2506.20069 v1 pith:NBKN3X3R submitted 2025-06-25 physics.optics

Benjamin R. Ecclestone , James A. Tummon Simmons , James E.D. Tweel , Deepak Dinakaran , Parsin Haji Reza This is my paper · ORCID
classification physics.optics
keywords PhotonAbsorptionRemoteSensinglabel-freemicroscopyimagingradiativeandnon-radiativerelaxationGaussianmixturemodelsnon-negativeleastsquaresbiomoleculeunmixingvirtualstaining
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper introduces Photon Absorption Remote Sensing (PARS), an absorption microscope that simultaneously records the radiative amplitude, non-radiative energy, and non-radiative decay rate at each of two excitation wavelengths, 266 nm and 532 nm. It claims that this six-channel per-pixel measurement is rich enough that established statistical tools—Gaussian mixture models (GMM) to extract biomolecule endmembers and non-negative least squares (NNLS) to invert a linear mixture model—can characterize and map nuclei, gray matter, white matter, red blood cells, and paraffin wax in unstained tissue sections. The abundance maps are compared with chemical H&E and DAPI ground truth and with a Pix2Pix virtual-staining baseline. If the claim holds, label-free histology could obtain stain-like biomolecule specificity from a single scan, without consuming the specimen or needing paired stained training data.

What carries the argument

The central object is the six-dimensional PARS endmember vector: for each pixel, radiative amplitude ($R_A$), non-radiative energy ($NR_E$), and non-radiative decay rate ($NR_D$) at 266 nm and at 532 nm. The model that carries the argument is the convex linear-mixture equation $y_n = \sum_k \mu_k a_{nk} + \sigma_n$ for pixel $n$, with abundances $a_{nk} \ge 0$ and $\sum_k a_{nk} = 1$, where $\mu_k$ are pure biomolecule PARS signatures and $\sigma_n$ is Gaussian noise. Gaussian mixture models, fit by expectation maximization with clinician-seeded initial parameters, estimate the endmembers from unlabelled image data, and non-negative least squares inverts the mixture to estimate abundance at every pixel. This machinery converts the measured optical transients into biomolecular identities without stained ground truth or learned image-to-image translation.

What would settle it

Image a set of FFPE sections containing known graded mixtures of purified or enriched nuclei and white matter (with paraffin completing the volume), run the GMM/NNLS pipeline, and compare estimated abundances to the prepared fractions; failure to recover the fractions within measurement noise would falsify the linear-mixture claim.

Watch

Extended reading notes

Core claim

On its own terms, the central discovery is that the PARS signal is not one contrast but a composite de-excitation fingerprint. After a pump pulse excites a voxel, the system measures how much energy returns as Stokes-shifted fluorescence (radiative amplitude) and how much is shed as heat (non-radiative energy), plus how fast the thermal transient decays (non-radiative decay rate), at both 266 nm and 532 nm excitation. The paper argues that these six values form a characteristic PARS signature or endmember for each biomolecule, and that a voxel containing several biomolecules follows a convex linear mixture of endmembers. Fitting a Gaussian mixture model to an unlabelled tissue image, seeded with clinician annotations, yields the endmembers; non-negative least squares then produces per-pixel abundance maps for nuclei, gray matter, white matter, red blood cells, and paraffin wax. Those maps are shown to match DAPI and H&E staining and to support a direct H&E-like color mapping that uses no spatial context, which the paper presents as evidence that PARS contrast itself, rather than learned priors, carries the specificity.

Load-bearing premise

The load-bearing premise is that each pixel's six measured PARS signals equal a weighted sum of a few pure biomolecule endmember signals, with non-negative abundances that sum to one; if the decay-rate channel, in particular, does not mix linearly, the estimated abundance maps will be systematically biased.

Editorial extensions

If this is right

  • A single PARS scan of an unstained FFPE section yields six registered contrast channels, allowing the same physical section to be scanned label-free and then chemically stained for direct one-to-one validation.
  • GMM-extracted endmembers and NNLS abundances separate diagnostic structures—nuclei, white matter tracts, gray matter, red blood cells, and paraffin—without any deep-learning model or stained ground truth.
  • The same pipeline can be applied to specimens where stained ground truth is unavailable, such as freshly resected tissue, because endmember extraction operates on the unlabelled image itself.
  • Recoloring NNLS abundance estimates reproduces H&E-like appearance without spatial context, showing that the specificity needed for virtual staining can come from the optical contrast rather than from learned morphological priors.
  • The six-channel PARS data form a more specific input for future deep-learning virtual staining and diagnostic models than single-contrast absorption images.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper does not test synthetic mixtures of purified endmembers, so a direct check of the linear-mixture assumption—spiking known fractions of purified nuclei and white matter into a section and seeing whether NNLS recovers the fractions—would determine how quantitative the abundance maps are.
  • Because the non-radiative decay rate is a fitted log-linear slope rather than an integrated energy, it is the channel most likely to violate strict linear additivity in mixed voxels; excluding it from the unmixing may give more robust abundance estimates.
  • The endmember extraction is performed on the same image that is later unmixed, so the reported stain agreement partly reflects in-sample clustering; cross-sample transfer would require a calibration step, as the paper itself notes when it says endmembers may differ between tissue types.
  • Adding the radiative emission spectrum or fluorescence lifetime, both listed as future work, would extend the same six-dimensional linear-mixture framework and likely separate endmembers that currently overlap, such as red blood cells and white matter.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

3 major / 5 minor

Summary. This manuscript introduces Photon Absorption Remote Sensing (PARS), a multiwavelength pump-probe microscope that simultaneously records radiative amplitude, non-radiative energy, and non-radiative decay rate at 266 nm and 532 nm excitation. The authors demonstrate the system on formalin-fixed paraffin-embedded human skin and murine brain sections and propose a statistical pipeline: Gaussian mixture models (GMM), seeded by clinician annotations of the same PARS data, extract biomolecule 'endmembers,' and non-negative least squares (NNLS) unmixes those endmembers to produce abundance maps. The abundance maps are compared visually with DAPI and H&E stains, and an H&E-like visualization is produced both directly from the unmixing and through a Pix2Pix virtual-staining network.

Significance. If the central claim holds, the paper would be a significant step toward label-free histopathology: a single label-free scan yields six absorption-derived contrasts and can separate nuclei, white matter, gray matter, and red blood cells in unstained FFPE tissue without deep learning. The hardware achievement—simultaneous radiative and non-radiative readouts at two excitation wavelengths, with whole-slide imaging and one-to-one PARS/H&E registration on the same section—is credible and well described. However, the statistical validation is weakened by the in-sample use of GMM endmembers and by the application of a linear-mixture model to a non-additive channel (NR_D). These are not fatal to the proof-of-concept but are load-bearing for the paper's strongest claim of direct, validated biomolecule unmixing.

major comments (3)
  1. [III.D and III.C] The forward-model equation in Section III.D, y_n = sum_k mu_k a_nk + sigma_n, is applied to all six PARS channels, including NR_D. Section III.C defines NR_D as the log-linear slope of the normalized non-radiative transient. For a voxel containing two or more absorbers, the measured transient is approximately an amplitude-weighted sum of the constituent transients, and the log-linear slope of that sum is not equal to the weighted sum of the constituent slopes unless the constituent time constants are equal. The paper provides no argument for equal time constants and, in fact, reports pixel-to-pixel variation in decay rates and paraffin-dominated material properties. The NNLS inversion therefore solves a model that does not describe the data-generating process for the NR_D channel. This is load-bearing because the abundance maps are presented as physically calibrated biomolecule mixtures. I recommend either demonstrating NR_D additivity with controlled or synthetic-mixture experiments, or repeating the unmixing without NR_D in the feature vector and showing that the stain-like maps are preserved.
  2. [IV.B.i, IV.B.ii, Figures 6 and 7] The endmembers are extracted by a GMM whose initialization is seeded with clinician-drawn labels on the same PARS images, and the resulting endmembers are then unmixed back onto those same pixels. The paper itself states that the GMM 'is not guaranteed to converge on the desired clinical features if randomly initialized' and that 'endmember extraction and unmixing is performed directly on the presented data.' As a result, the visual agreement with DAPI and H&E in Figures 6 and 7 reflects in-sample fit quality rather than out-of-sample predictive performance. The comparison is also purely qualitative; no overlap or correlation metric between abundance maps and stained ground truth is reported. To support the claim of 'directly validated' biomolecule unmixing, the authors should add held-out validation (for example, train endmembers on one tissue region or sample and test on another) and report quantitative agreement metrics.
  3. [IV.B.ii] The paper's claim that the method operates 'without stained ground truth images or deep-learning methods' is overstated because the pipeline still requires clinician-labeled seeds on the target data, a user-chosen number of endmembers K, and manual contrast adjustment before labeling. These are not chemically stained ground truths, but they are human supervision on the same image, and the paper states that endmembers may not transfer between samples. The manuscript should clarify the amount and type of supervision required and report sensitivity of the unmixing to the initialization and to K; otherwise the practical claim of automation goes beyond the evidence presented.
minor comments (5)
  1. [Figure 4 caption] The phrase 'rime domain signals' should read 'time domain signals.'
  2. [References] Reference 74 is not the original Pix2Pix paper; the correct citation is Isola et al., 'Image-to-Image Translation with Conditional Adversarial Networks,' CVPR 2017.
  3. [IV.A] The text refers to 'SI: Figure XX' when discussing the QER metric; this should be resolved to a specific supplemental figure reference.
  4. [IV.C, Figure 8] The in-text panel citations for Figure 8 are inconsistent: the chemical H&E is called 'Figure 8(a)' in one sentence and 'Figure 8(e)' in another, and the whole-slide PARS image is called 'Figure 8(d)' although panel (d) is labeled as the Pix2Pix virtual stain. Please harmonize panel labels and text citations.
  5. [III.D] The symbol k is used both for the number of endmembers and as a summation index; using K for the count and k for the index would improve clarity.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: the PARS contrast derivation and GMM/NNLS unmixing are supported by independent stain comparisons; the in-sample fitting and self-citations are limitations, not load-bearing circular steps.

full rationale

The paper's central claim is that six PARS contrasts (radiative amplitude, non-radiative energy, and non-radiative decay rate at 266 nm and 532 nm) can be used to characterize and unmix biomolecules via GMM endmember extraction and NNLS abundance mapping. The endmembers are estimated from the same PARS data used for unmixing, with clinician-seeded initializations from the same images, so the abundance maps are in-sample projections rather than out-of-sample predictions. This limits generalizability and weakens the strength of the claim, but it is not circular in the derivation sense: the endmembers and abundances are not defined as the quantities they are supposed to explain, and the H&E and DAPI comparisons provide an independent external benchmark that the PARS-derived maps are checked against. The forward linear-mixture model is an assumption, and applying it to NR_D, a log-linear fitted decay slope, may be physically misspecified, but misspecification is a correctness concern rather than a circularity. Several references to prior work by the same group are present (e.g., refs. 49-53, 57), but they are used for signal-processing details, imaging workflow, and previous virtual-staining demonstrations, not as the sole justification for the current claim, which rests on the present data and stain comparisons. No equation or fitted parameter is shown to reduce by construction to the predicted quantity, so no specific circular step can be identified.

Assumptions & free parameters 5 free parameters · 4 assumptions · 2 invented entities

The central claim depends on (i) the linear mixture forward model used by NNLS, (ii) a clinician-seeded GMM whose K and initialization are hand-chosen for each specimen, (iii) the assumption that the recorded non-radiative signal is thermally dominated in paraffin-embedded tissue, and (iv) per-channel normalization choices. No new physical entities are postulated; QER and TA are definitions over the measured channels.

free parameters (5)
  • GMM component count K = 5 for brain (nuclei, gray matter, white matter, RBC, paraffin); 3 in the skin example
    Chosen by clinicians based on prior knowledge of tissue composition; determines the number of endmembers and therefore the structure of all unmixing results (Section III.D, IV.B).
  • GMM initialization (means, covariances, prevalences) = From manual semantic labels on the same PARS images
    EM is seeded with parameters computed from clinician annotations, and the paper notes EM may converge to different local minima without this seeding (Section III.D).
  • Manual label contrast adjustment = Unknown, per-image
    Data is 'contrast adjusted and projected to maximize visual differentiation of relevant biomolecules' before labeling; this choice influences which clusters the GMM is seeded to find (Section III.D).
  • NR_D log-linear fit window (b) to (c) = Post-excitation segment within the ~500 ns recording
    The decay-rate feature depends on the chosen fitting interval; the window endpoints are selected by the authors (Section II, Figure 2d).
  • Global-average per-channel normalization = Each of the 6 channels divided by its global mean
    Endmember signatures are only interpretable relative to the global average of each channel, which rescales all subsequent NNLS abundances (Section IV.B, Figure 6 caption).
assumptions (4)
  • domain assumption PARS signal at a pixel is a linear mixture of endmember signals with positivity and sum-to-one abundance constraints (y_n = sum mu_k a_nk + sigma_n)
    Assumed in Section III.D; plausible for energy integrals but not for the fitted decay slope, and sum-to-one requires the background (paraffin) class to complete the sum.
  • standard math Measurement noise per contrast is independent Gaussian, justifying the GMM likelihood
    Invoked in Section III.D to reformulate the linear mixture as a Gaussian mixture model; not validated against the noise statistics of the measured signals.
  • domain assumption FFPE tissue behaves as a semi-infinite absorber with paraffin-wax material properties for timescale estimates
    Section II assumes water in tissue is replaced by paraffin and uses paraffin sound speed 1050 m/s and thermal diffusivity 0.15 mm^2/s to argue the non-radiative signal is thermally dominated.
  • domain assumption Non-radiative signal is dominated by photothermal effects over the ~500 ns recording window
    Section II: photoacoustic transit time ~1.6 ns is short relative to the recorded window, so NR_E and NR_D are extracted from a thermal (Newton cooling) model, ignoring the photoacoustic component.
invented entities (2)
  • Quantum efficiency ratio (QER)
    purpose: Metric to display relative radiative vs non-radiative relaxation, QER = (R_A - NR_E)/(NR_E + R_A)
    Defined from the paper's own measured channels; correlated with but not equal to QY/PCE, and it carries no falsifiable handle outside this instrument (Section IV.A, SI Figure 3).
  • Total-Absorption (TA) mapping
    purpose: Sum of radiative and non-radiative relaxation to represent absolute absorption independent of quantum yield
    Composite image formed from the measured channels; a visualization convention, not a new physical entity (Section I, IV.A).

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Cite this review

Pith. "Pith review of Photon Absorption Remote Sensing (PARS): Comprehensive Absorption Imaging Enabling Label-Free Biomolecule Characterization and Mapping." pith.science (2026). https://pith.science/paper/NBKN3X3R

@misc{pith2026250620069,
  author       = {Pith},
  title        = {Pith review of: Photon Absorption Remote Sensing (PARS): Comprehensive Absorption Imaging Enabling Label-Free Biomolecule Characterization and Mapping},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NBKN3X3R}},
  note         = {Machine review of arXiv:2506.20069}
}
read the original abstract

Label-free optical absorption microscopy techniques continue to evolve as promising tools for label-free histopathological imaging of cells and tissues. However, critical challenges relating to specificity and contrast, as compared to current gold-standard methods continue to hamper adoption. This work introduces Photon Absorption Remote Sensing (PARS), a new absorption microscope modality, which simultaneously captures the dominant de-excitation processes following an absorption event. In PARS, radiative (auto-fluorescence) and non-radiative (photothermal and photoacoustic) relaxation processes are collected simultaneously, providing enhanced specificity to a range of biomolecules. As an example, a multiwavelength PARS system featuring UV (266 nm) and visible (532 nm) excitation is applied to imaging human skin, and murine brain tissue samples. It is shown that PARS can directly characterize, differentiate, and unmix, clinically relevant biomolecules inside complex tissues samples using established statistical processing methods. Gaussian mixture models (GMM) are used to characterize clinically relevant biomolecules (e.g., white, and gray matter) based on their PARS signals, while non-negative least squares (NNLS) is applied to map the biomolecule abundance in murine brain tissues, without stained ground truth images or deep-learning methods. PARS unmixing and abundance estimates are directly validated and compared against chemically stained ground truth images, and deep learning based-image transforms. Overall, it is found that the PARS unique and rich contrast may provide comprehensive, and otherwise inaccessible, label-free characterization of molecular pathology, representing a new source of data to develop AI and machine learning methods for diagnostics and visualization.

Figures

Figures reproduced from arXiv: 2506.20069 by the authors.

Figure 5
Figure 5. PARS image of thin sections of FFPE murine brain tissues. The PARS image, overalays all absorption contrasts from both 266 𝑛𝑚 and 532 𝑛𝑚 excitation merged into a single image. Scale Bar: 1 𝑚𝑚. Subregions shown in (i)-(iv) show fine detail contrast of the TA images. Scale Bar: 100 𝜇𝑚. (i) transverse cross section of white matter tracts, (ii) vessels and gray matter near the pia matter, (iii) dense white matter tracts… view at source ↗

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Works this paper leans on

75 extracted references · 74 canonical work pages

  1. [1]

    Sinn, H. P. & Kreipe, H. A Brief Overview of the WHO Classification of Breast Tumors, 4th Edition, Focusing on Issues and Updates from the 3rd Edition. Breast Care 8, 149 (2013)

    work page 2013

  2. [2]

    Louis, D. N. et al. The 2021 WHO Classification of Tumors of the Central Nervous System: a summary. Neuro Oncol 23, 1231 (2021)

    work page 2021

  3. [3]

    Kellenberger, E. et al. Artefacts and morphological changes during chemical fixation. J Microsc 168, 181–201 (1992)

    work page 1992

  4. [4]

    Dapson, R. W. Macromolecular changes caused by formalin fixation and antigen retrieval. Biotechnic & Histochemistry 82, 133–140 (2007)

    work page 2007

  5. [5]

    E., Lefever, M

    Morrison, L. E., Lefever, M. R., Lewis, H. N., Kapadia, M. J. & Bauer, D. R. Conventional histological and cytological staining with simultaneous immunohistochemistry enabled by invisible chromogens. Lab Invest 102, 545 (2022)

    work page 2022

  6. [6]

    Howat, W. J. & Wilson, B. A. Tissue fixation and the effect of molecular fixatives on downstream staining procedures. Methods 70, 12–19 (2014)

    work page 2014

  7. [7]

    Feldman, A. T. & Wolfe, D. Tissue processing and hematoxylin and eosin staining. Methods in Molecular Biology 1180, 31–43 (2014). Photon Absorption Remote Sensing (PARS) 18

    work page 2014

  8. [8]

    It’s free imaging — label-free, that is

    Marx, V . It’s free imaging — label-free, that is. Nature Methods 2019 16:12 16, 1209–1212 (2019)

    work page 2019

Show all 75 references
  1. [9]

    T., Boppart, S

    Shaked, N. T., Boppart, S. A., Wang, L. V . & Popp, J. Label-free biomedical optical imaging. Nature Photonics 2023 17:12 17, 1031–1041 (2023)

    2023

  2. [10]

    & Wang, L

    Lin, L. & Wang, L. V . Photoacoustic Imaging. Adv Exp Med Biol 3233, 147–175 (2021)

    2021

  3. [11]

    Croce, A. C. & Bottiroli, G. Autofluorescence Spectroscopy and Imaging: A Tool for Biomedical Research and Diagnosis. Eur J Histochem 58, 320–337 (2014)

    2014

  4. [12]

    Kolenc, O. I. & Quinn, K. P. Evaluating Cell Metabolism Through Autofluorescence Imaging of NAD(P)H and FAD. https://home.liebertpub.com/ars 30, 875–889 (2019)

    2019

  5. [13]

    & Wang, L

    Liu, C. & Wang, L. Functional photoacoustic microscopy of hemodynamics: a review. Biomedical Engineering Letters 2022 12:2 12, 97–124 (2022)

    2022

  6. [14]

    Loo, J. et al. Autofluorescence Virtual Staining System for H&E Histology and Multiplex Immunofluorescence Applied to Immuno-Oncology Biomarkers in Lung Cancer. Cancer research communications 5, 54–65 (2025)

    2025

  7. [16]

    Rivenson, Y . et al. Virtual histological staining of unlabelled tissue -autofluorescence images via deep learning. Nature Biomedical Engineering 2019 3:6 3, 466–477 (2019)

    2019

  8. [17]

    Li, Y . et al. Virtual histological staining of unlabeled autopsy tissue. Nature Communications 2024 15:1 15, 1–17 (2024)

    2024

  9. [18]

    Bai, B. et al. Label-Free Virtual HER2 Immunohistochemical Staining of Breast Tissue using Deep Learning. BME Front 2022, (2022)

    2022

  10. [19]

    Bai, B. et al. Deep learning -enabled virtual histological staining of biological samples. Light: Science & Applications 2023 12:1 12, 1–20 (2023)

    2023

  11. [20]

    Schnell, M. et al. All-digital histopathology by infrared-optical hybrid microscopy. Proc Natl Acad Sci U S A 117, 3388–3396 (2020)

    2020

  12. [21]

    Zharov, V . P. & Lapotko, D. O. Photothermal imaging of nanoparticles and cells. IEEE Journal on Selected Topics in Quantum Electronics 11, 733–751 (2005)

    2005

  13. [22]

    Ishigane, G. et al. Label-free mid-infrared photothermal live-cell imaging beyond video rate. Light: Science & Applications 2023 12:1 12, 1–14 (2023)

    2023

  14. [23]

    Cao, R. et al. Label-free intraoperative histology of bone tissue via deep-learning-assisted ultraviolet photoacoustic microscopy. Nat Biomed Eng 7, 124 (2022)

    2022

  15. [24]

    & Wong, T

    Kang, L., Li, X., Zhang, Y . & Wong, T. T. W. Deep learning enables ultraviolet photoacoustic microscopy based histological imaging with near real -time virtual staining. Photoacoustics 25, 100308 (2022)

    2022

  16. [25]

    Wong, T. T. W. et al. Fast label-free multilayered histology-like imaging of human breast cancer by photoacoustic microscopy. Sci Adv 3, (2017)

    2017

  17. [26]

    Yoon, C. et al. Deep learning-based virtual staining, segmentation, and classification in label -free photoacoustic histology of human specimens. Light: Science & Applications 2024 13:1 13, 1–14 (2024). Photon Absorption Remote Sensing (PARS) 19

    2024

  18. [27]

    Sarri, B. et al. Stimulated Raman histology: one to one comparison with standard hematoxylin and eosin staining. Biomedical Optics Express, Vol. 10, Issue 10, pp. 5378-5384 10, 5378–5384 (2019)

    2019

  19. [28]

    Hollon, T. C. et al. Rapid intraoperative diagnosis of pediatric brain tumors using stimulated raman histology. Cancer Res 78, 278–289 (2018)

    2018

  20. [29]

    Orringer, D. A. et al. Rapid intraoperative histology of unprocessed surgical specimens via fibre - laser-based stimulated Raman scattering microscopy. Nature Biomedical Engineering 2017 1:2 1, 1–13 (2017)

    2017

  21. [30]

    F., Park, J., Chaney, E

    Tehrani, K. F., Park, J., Chaney, E. J., Tu, H. & Boppart, S. A. Nonlinear Imaging Histopathology: A Pipeline to Correlate Gold -Standard Hematoxylin and Eosin Staining With Modern Nonlinear Microscopy. IEEE Journal of Selected Topics in Quantum Electronics 29, (2023)

    2023

  22. [31]

    M., Zipfel, W

    Williams, R. M., Zipfel, W. R. & Webb, W. W. Multiphoton microscopy in biological research. Curr Opin Chem Biol 5, 603–608 (2001)

    2001

  23. [32]

    Tai, S.-P . et al. Optical biopsy of fixed human skin with backward -collected optical harmonics signals. Optics Express, Vol. 13, Issue 20, pp. 8231-8242 13, 8231–8242 (2005)

    2005

  24. [33]

    Campagnola, P. J. & Dong, C. Y . Second harmonic generation microscopy: principles and applications to disease diagnosis. Laser Photon Rev 5, 13–26 (2011)

    2011

  25. [34]

    Chen, S. Y . et al. In vivo virtual biopsy of human skin by using noninvasive higher harmonic generation microscopy. IEEE Journal on Selected Topics in Quantum Electronics 16, 478 –492 (2010)

    2010

  26. [35]

    Pekmezci, M. et al. Detection of glioma infiltration at the tumor margin using quantitative stimulated Raman scattering histology. Sci Rep 11, (2021)

    2021

  27. [36]

    Ji, M. et al. Rapid, label-free detection of brain tumors with stimulated raman scattering microscopy. Sci Transl Med 5, (2013)

    2013

  28. [37]

    Wang, Q. et al. Deep learning-based virtual H& E staining from label-free autofluorescence lifetime images. npj Imaging 2024 2:1 2, 1–11 (2024)

    2024

  29. [38]

    Martell, M. T. et al. Deep learning-enabled realistic virtual histology with ultraviolet photoacoustic remote sensing microscopy. Nature Communications 2023 14:1 14, 1–17 (2023)

    2023

  30. [39]

    Analytical applications of Raman spectroscopy

    Kudelski, A. Analytical applications of Raman spectroscopy. Talanta 76, 1–8 (2008)

    2008

  31. [40]

    Rioboó, R. J. J., Gontán, N., Sanderson, D., Desco, M. & Gómez -Gaviro, M. V . Brillouin Spectroscopy: From Biomedical Research to New Generation Pathology Diagnosis. International Journal of Molecular Sciences 2021, Vol. 22, Page 8055 22, 8055 (2021)

    2021

  32. [41]

    Kabakova, I. et al. Brillouin microscopy. Nature Reviews Methods Primers 2024 4:1 4, 1–20 (2024)

    2024

  33. [42]

    Rurack Div, K. I. Fluorescence Quantum Yields: Methods of Determination and Standards. Standardization and Quality Assurance in Fluorescence Measurements I 5, 101–145 (2008)

    2008

  34. [43]

    Bialkowski, S., Astrath, N. G. C. & Proskurnin, M. Photothermal spectroscopy methods. 485

  35. [44]

    Lichtman, J. W. & Conchello, J. A. Fluorescence microscopy. Nature Methods 2005 2:12 2, 910– 919 (2005)

    2005

  36. [45]

    Cell and tissue autofluorescence research and diagnostic applications

    Monici, M. Cell and tissue autofluorescence research and diagnostic applications. Biotechnol Annu Rev 11, 227–256 (2005). Photon Absorption Remote Sensing (PARS) 20

    2005

  37. [46]

    & Wang, L

    Zhou, Y ., Yao, J. & Wang, L. V . Tutorial on photoacoustic tomography. J Biomed Opt 21, 061007 (2016)

    2016

  38. [47]

    Biomedical photoacoustic imaging

    Beard, P . Biomedical photoacoustic imaging. Interface Focus 1, 602 (2011)

    2011

  39. [48]

    Adhikari, S. et al. Photothermal Microscopy: Imaging the Optical Absorption of Single Nanoparticles and Single Molecules. ACS Nano 14, 16414–16445 (2020)

    2020

  40. [49]

    Tummon Simmons, J. A. et al. Label-Free Non-Contact Vascular Imaging Using Photon Absorption Remote Sensing. IEEE Trans Biomed Eng 72, 1160–1169 (2025)

    2025

  41. [50]

    Tweel, J. E. D. et al. Automated Whole Slide Imaging for Label -Free Histology using Photon Absorption Remote Sensing Microscopy. IEEE Trans Biomed Eng 1–13 (2024) doi:10.1109/TBME.2024.3355296

    2024

  42. [51]

    Tweel, J. E. D. et al. Photon Absorption Remote Sensing Imaging of Breast Needle Core Biopsies Is Diagnostically Equivalent to Gold Standard H&E Histologic Assessment. Current Oncology 2023, Vol. 30, Pages 9760-9771 30, 9760–9771 (2023)

    2023

  43. [52]

    Boktor, M. et al. Virtual histological staining of label -free total absorption photoacoustic remote sensing (TA-PARS). Scientific Reports 2022 12:1 12, 1–12 (2022)

    2022

  44. [53]

    Ecclestone, B. R. et al. Label-free complete absorption microscopy using second generation photoacoustic remote sensing. Scientific Reports 2022 12:1 12, 1–17 (2022)

    2022

  45. [54]

    & Markovitsi, D

    Gustavsson, T. & Markovitsi, D. Fundamentals of the Intrinsic DNA Fluorescence. Acc Chem Res 54, 1226–1235 (2021)

    2021

  46. [55]

    W., Poyart, C

    Petrich, J. W., Poyart, C. & Martin, J. L. Photophysics and reactivity of heme proteins: a femtosecond absorption study of hemoglobin, myoglobin, and protoheme. Biochemistry 27, 4049– 4060 (1988)

    1988

  47. [56]

    K., Zhou, Q

    Yao, D.-K., Maslov, K., Shung, K. K., Zhou, Q. & Wang, L. V . In vivo label-free photoacoustic microscopy of cell nuclei by excitation of DNA and RNA. Opt Lett 35, 4139 (2010)

    2010

  48. [57]

    Ecclestone, B. R. et al. Photon Absorption Remote Sensing Virtual Histopathology: Diagnostic Equivalence to Gold-Standard H&E Staining in Skin Cancer Excisional Biopsies. (2025)

    2025

  49. [58]

    Longaker, P. R. & Litvak, M. M. Perturbation of the Refractive Index of Absorbing Media by a Pulsed Laser Beam. J Appl Phys 40, 4033–4041 (1969)

    1969

  50. [59]

    Wang, L. V . & Hsin-I Wu. Biomedical Optics: Principles and Imaging. Biomedical Optics: Principles and Imaging (2012) doi:10.1002/9780470177013

    2012 doi

  51. [60]

    E., Swofford, R

    Long, M. E., Swofford, R. L. & Albrecht, A. C. Thermal lens technique: A new method of absorption spectroscopy. Science (1979) 191, 183–184 (1976)

    1979

  52. [61]

    L., Long, M

    Swofford, R. L., Long, M. E. & Albrecht, A. C. C–H vibrational states of benzene, naphthalene, and anthracene in the visible region by thermal lensing spectroscopy and the local mode model. J Chem Phys 65, 179–190 (1976)

    1976

  53. [62]

    Whinnery, J. R. Laser Measurement of Optical Absorption in Liquids. Acc Chem Res 7, 225–231 (1974)

    1974

  54. [63]

    Hajireza, P., Shi, W., Bell, K., Paproski, R. J. & Zemp, R. J. Non -interferometric photoacoustic remote sensing microscopy. Light: Science & Applications 2017 6:6 6, e16278–e16278 (2017). Photon Absorption Remote Sensing (PARS) 21

    2017

  55. [64]

    A., Amer, N

    Olmstead, M. A., Amer, N. M., Kohn, S., Fournier, D. & Boccara, A. C. Photothermal displacement spectroscopy: An optical probe for solids and surfaces. Applied Physics A Solids and Surfaces 32, 141–154 (1983)

    1983

  56. [65]

    & Cielo, P

    Rousset, G., Bertrand, L. & Cielo, P. A pulsed thermoelastic analysis of photothermal surface displacements in layered materials. J Appl Phys 57, 4396–4405 (1985)

    1985

  57. [66]

    P., Park, H

    Chen, S., Grigoropoulos, C. P., Park, H. K., Kerstens, P. & Tam, A. C. Photothermal displacement measurement of transient melting and surface deformation during pulsed laser heating. Appl Phys Lett 73, 2093–2095 (1998)

    1998

  58. [67]

    Kaur, C. et al. Investigation of Non-Radiative Relaxation Dynamics Under Pulsed Excitation Using Photon Absorption Remote Sensing: A Proof-of-Principle Study in Mechanical Sensing. (2025)

    2025

  59. [68]

    & Wang, L

    Xia, J., Yao, J. & Wang, L. V . Photoacoustic tomography: principles and advances. Electromagn Waves (Camb) 147, 1 (2014)

    2014

  60. [69]

    Ultrasonic behaviour of paraffin wax during melting and solidification processes

    Kamioka, H. Ultrasonic behaviour of paraffin wax during melting and solidification processes. Jpn J Appl Phys 33, 2908–2912 (1994)

    1994

  61. [70]

    & Lindemann, A

    Blumm, J. & Lindemann, A. Characterization of the thermophysical properties of molten polymers and liquids using the flash technique. High Temp High Press 35–36, 627–632 (2003)

    2003

  62. [71]

    Ni, H. et al. Millimetre-deep micrometre -resolution vibrational imaging by shortwave infrared photothermal microscopy. Nature Photonics 2024 18:9 18, 944–951 (2024)

    2024

  63. [72]

    Bankhead, P. et al. QuPath: Open source software for digital pathology image analysis. Scientific Reports 2017 7:1 7, 1–7 (2017)

    2017

  64. [73]

    Gaussian Mixture Models

    Reynolds, D. Gaussian Mixture Models. Encyclopedia of Biometrics 659–663 (2018) doi:10.1007/978-0-387-73003-5_196

    2018 doi

  65. [74]

    S., Rao, N

    Tahmid, M., Alam, M. S., Rao, N. & Ashrafi, K. M. A. Image-to-Image Translation with Conditional Adversarial Networks. Proceedings of 2023 IEEE 9th International Women in Engineering (WIE) Conference on Electrical and Computer Engineering, WIECON -ECE 2023 468–472 (2016) doi:1...

    2016

  66. [75]

    Y ., Park, T., Isola, P

    Zhu, J. Y ., Park, T., Isola, P. & Efros, A. A. Unpaired Image-to-Image Translation using Cycle - Consistent Adversarial Networks. Proceedings of the IEEE International Conference on Computer Vision 2017-October, 2242–2251 (2017)

    2017

  67. [76]

    & Ruusuvuori, P

    Latonen, L., Koivukoski, S., Khan, U. & Ruusuvuori, P. Virtual staining for histology by deep learning. Trends Biotechnol 42, 1177–1191 (2024). Photon Absorption Remote Sensing (PARS) 22 VI. Supplemental Information Supplemental Figure 1: H&E stained brightfield microscope ima...

    2024

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.