Photon absorption remote sensing (PARS) microscopy enables label-free imaging using intrinsic absorption and scattering contrast. We investigate closely spaced UV excitation (270 to 290 nm) to probe wavelength-dependent biomolecular contrast in murine gastrointestinal tissue. A multi-wavelength dataset was analyzed with a variational bayesian gaussian mixture model (VB-GMM) to separate overlapping spectral responses. Nuclear absorption decreased consistently from 270 to 290 nm while VB-GMM identified five spatially coherent spectral components. Dense UV sampling with PARS shows promise for enhanced label-free contrast characterization.

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.

We present an optical spectroscopy system based on photon absorption remote sensing (PARS) that simultaneously captures radiative and non-radiative sample relaxation following UV-excitation. Non-radiative relaxations are measured through probing the excited sample's thermal and pressure-induced refractive index changes, while a spectrometer is used to record radiative sample relaxation, which occurs as fluorescence. We benchmark the generated non-radiative, radiative, and combined total absorption PARS absorbance spectra of liquid eumelanin, NADH, DMSO, and methylene blue samples against data collected from UV-Visible spectrophotometry. Finally, we leverage the absorption and fluorescence spectral data collected from the PARS system to accurately determine the make-up of mixed craft ink samples. The PARS system overcomes the limitations of traditional optical spectroscopy techniques by broadening the range of samples that can be analyzed and by providing a more detailed level of sample characterization. (c) 2026 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

In this study, we present a non-radiative photon absorption remote sensing (NR-PARS) submodule as a method for mechanical sensing of single micro-objects. NR-PARS employs probe beam scattering to capture the non-radiative relaxation process following the absorption of a light pulse. When operated at a gigahertz-range bandwidth, NR-PARS resolves sub-nanosecond dynamics, tracing both photoacoustic (PA) pressure propagation and thermal diffusion. Coupled with a developed descriptive model, this GHz-range measurement enables retrieval of minimally distorted PA temporal profiles, which encode the ratio between the absorber’s sound speed and diameter. Proof-of-principle experiments with polystyrene microspheres demonstrate the ability to assess elastic properties at the single-particle level.

Histochemical staining is essential for visualizing tissue architecture but is destructive and limited by tissue availability. Virtual staining with label-free microscopy offers a non-destructive alternative, enabling multiple stains from the same section. We present a dual-excitation Photon Absorption Remote Sensing (PARS) microscopy system, the first application of 355 nm UVA alongside 266 nm UVC excitation. The 355 nm source extends PARS contrast to include red blood cells, melanin, and enhanced stromal architecture through complementary radiative and non-radiative absorption. Using the RegGAN framework, we demonstrate the first PARS virtual staining across routine (H&E) and specialized (Masson’s trichrome, PAS, Jones methenamine silver) stains in human and murine tissues. Quantitative metrics show dual-excitation improves virtual stain similarity over single-wavelength inputs. A masked evaluation by expert pathologists provides an initial, coarse assessment indicating that virtual stains achieve ratings on a visual diagnostic quality scale comparable to chemical counterparts under limited evaluation conditions. These results support dual-excitation PARS as a promising non-destructive approach for multi-stain virtual histology. Corresponding whole-slide image pairs are available at the BioImage Archive ( https://doi.org/10.6019/S-BIAD2232 ).

Photon Absorption Remote Sensing (PARS) enables label-free imaging of subcellular morphology by observing biomolecule specific absorption interactions. Coupled with deep-learning, PARS produces label-free virtual Hematoxylin and Eosin (H E) stained images in unprocessed tissues. This study evaluates the diagnostic performance of PARS virtual H E images in excisional skin biopsies, including Squamous (SCC), Basal (BCC) Cell Carcinoma, and normal skin. Sixteen unstained formalin-fixed paraffin-embedded skin excisions were PARS imaged, virtually H E stained, then chemically stained and imaged at 40x. Seven fellowship trained dermatopathologists assessed all images. Example PARS and chemical H E whole-slide images from this study are available at the BioImage Archive (https://doi.org/10.6019/S-BIAD2324). Concordance analysis indicates 95.5

The mechanical properties of micro-scale bio-entities are fundamental for understanding their functions and pathological states. However, current methods for assessing elastic properties at single-particle level such as Brillouin and atomic force microscopies exhibit intrinsic limitations, including being often slow, having poor resolution, or involving complicated and invasive setups. In this study, we explore Photon Absorption Remote Sensing (PARS) microscopy as a unique solution for mechanical sensing of single micro-objects. PARS uses probe beam scattering/reflectivity measurements to capture non-radiative relaxation process following the absorption of a pulse of light by a micro-object. In particular, we demonstrate that, when operating at GHz-range bandwidth, PARS can trace the sub-nanosecond dynamics of non-radiative relaxation in individual micro-objects, capturing both photoacoustic (PA) pressure propagation and thermal diffusion. This GHz-range measurement, in conjunction with a developed descriptive model, enables the experimental extraction of a minimally distorted PA temporal profile. The PA temporal profile contain information on the ratio between the absorbing object's sound speed and its characteristic diameter, offering a new dimension in PARS microscopy. This enables the assessment of the object's elastic properties, deduced from its speed of sound. Additionally, it offers the potential for sizing objects with known sound speeds. The proof of principle experiments was conducted using spherical polystyrene absorbers, ranging in size from 1 to 10 micrometers with known properties, embedded in a Polydimethylsiloxane (PDMS) matrix. This technique expands the scope of PARS imaging, opening new perspectives for clinical applications in mechanobiology by demonstrating its potential for mechanical imaging.

OBJECTIVE:Functional vascular imaging is a critical method for early detection and prevention of disease. Established non-contact vascular imaging techniques capture predominantly structural information. In this study, a novel non-contact label-free in vivo Photon Absorption Remote Sensing (PARS) microscope is developed for structural and functional vascular imaging. METHODS:The presented in vivo PARS microscope captures the endogenous absorption of green (532nm) light to form a complete picture of vasculature and surrounding tissues. Imaging system repeatability is enhanced through robust transient absorption signal extraction, and state-of-the-art real-time alignment methods. RESULTS:Detailed imaging of vascular structure is demonstrated through in vivo microscopy of two established animal models: mouse ear and chicken embryo. Preliminary functional contrast is realized through video rate imaging of red blood cell dynamics in the capillary networks of chicken embryos. CONCLUSION:The presented in vivo PARS microscope successfully captures detailed structural and functional vascular contrast. SIGNIFICANCE:This innovative non-contact label-free imaging technique holds promise as a tool for preventative medical care, as functional change often precedes structural change.

OBJECTIVE:Pathologists rely on histochemical stains to impart contrast in thin translucent tissue samples, revealing tissue features necessary for identifying pathological conditions. However, the chemical labeling process is destructive and often irreversible or challenging to undo, imposing practical limits on the number of stains that can be applied to the same tissue section. Here we present an automated label-free whole slide scanner using a PARS microscope designed for imaging thin, transmissible samples. METHODS:Peak SNR and in-focus acquisitions are achieved across entire tissue sections using the scattering signal from the PARS detection beam to measure the optimal focal plane. Whole slide images (WSI) are seamlessly stitched together using a custom contrast leveling algorithm. Identical tissue sections are subsequently H&E stained and brightfield imaged. The one-to-one WSIs from both modalities are visually and quantitatively compared. RESULTS:PARS WSIs are presented at standard 40x magnification in malignant human breast and skin samples. We show correspondence of subcellular diagnostic details in both PARS and H&E WSIs and demonstrate virtual H&E staining of an entire PARS WSI. The one-to-one WSI from both modalities show quantitative similarity in nuclear features and structural information. CONCLUSION:PARS WSIs are compatible with existing digital pathology tools, and samples remain suitable for histochemical, immunohistochemical, and other staining techniques. SIGNIFICANCE:This work is a critical advance for integrating label-free optical methods into standard histopathology workflows.

Vascular imaging is an essential tool for understanding tissue health in the diagnosis and treatment of disease, especially for applications in cancer and ophthalmology. This work explores the application of Photon Absorption Remote Sensing (PARS) microscopy to several established in vivo vasculature models including chicken embryo and mouse ear. Significant system improvements are presented which have enabled the repeatable acquisition of images that are larger, require less scan time, and capture detailed absorption contrast of both radiative and non-radiative relaxation. The presented work demonstrates PARS as a promising platform for future clinical evaluation of disease pathology and treatment efficacy.

Label-free optical absorption microscopy techniques have evolved as effective tools for non-invasive chemical specific structural, and functional imaging. Yet most modern label-free microscopy modalities target only a fraction of the contrast afforded by an optical absorption interaction. We introduce a comprehensive optical absorption microscopy technique, Photon Absorption Remote Sensing (PARS), which simultaneously captures the dominant light matter interactions which occur as a pulse of light is absorbed by a molecule. In PARS, the optical scattering, attenuation, and the transient radiative and non-radiative relaxation processes are collected at each optical absorption event. This provides a complete representation of the absorption event, providing unique contrast presented here as the total absorption (TA) and quantum efficiency ratio (QER) measurements. By capturing a complete view of each absorption interaction, PARS bridges many of the specificity challenges associated with label-free imaging, facilitating recovery of a wider range of biomolecules than independent radiative or non-radiative modalities. To show the versatility of PARS, we explore imaging across a wide range of biological specimens, from single cells to in-vivo imaging of living subjects. These examples of label-free histopathological imaging, and vascular imaging illustrate some of the numerous fields where PARS may have profound impacts. Overall PARS may provide comprehensive label-free contrast in a wide variety of biological specimens, providing otherwise inaccessible visualizations, and representing a new a source of rich data to develop new AI and machine learning methods for diagnostics and visualization.

The latest innovations in label-free virtual-histology using Photon Absorption Remote Sensing (PARS) are presented. PARS captures endogenous absorption (radiative and non-radiative) and scattering contrasts, enabling label-free biomolecule specific imaging. Combined with innovations in AI and signal processing, PARS provides several label-free virtual histochemical stains (e.g., H&E, T-blue) directly from a single scan of an unprocessed tissue specimen. Results validated through clinical efficacy studies indicate that PARS virtual staining is diagnostically indistinguishable from gold standard chemical histology. This represents a crucial milestone in deploying PARS as an alternative to standard histopathology.

Accurate and fast histological staining is crucial in histopathology, impacting diagnostic precision and reliability. Traditional staining methods are time-consuming and subjective, causing delays in diagnosis. Digital pathology plays a vital role in advancing and optimizing histology processes to improve efficiency and reduce turnaround times. This study introduces a novel deep learning-based framework for virtual histological staining using photon absorption remote sensing (PARS) images. By extracting features from PARS time-resolved signals using a variant of the K-means method, valuable multi-modal information is captured. The proposed multi-channel cycleGAN model expands on the traditional cycleGAN framework, allowing the inclusion of additional features. Experimental results reveal that specific combinations of features outperform the conventional channels by improving the labeling of tissue structures prior to model training. Applied to human skin and mouse brain tissue, the results underscore the significance of choosing the optimal combination of features, as it reveals a substantial visual and quantitative concurrence between the virtually stained and the gold standard chemically stained hematoxylin and eosin images, surpassing the performance of other feature combinations. Accurate virtual staining is valuable for reliable diagnostic information, aiding pathologists in disease classification, grading, and treatment planning. This study aims to advance label-free histological imaging and opens doors for intraoperative microscopy applications.

Modern histopathology relies on the microscopic examination of thin tissue sections stained with histochemical techniques, typically using brightfield or fluorescence microscopy. However, the staining of samples can permanently alter their chemistry and structure, meaning an individual tissue section must be prepared for each desired staining contrast. This not only consumes valuable tissue samples but also introduces delays in essential diagnostic timelines. In this work, virtual histochemical staining is developed using label-free photon absorption remote sensing (PARS) microscopy. We present a method that generates virtually stained histology images that are indistinguishable from the gold standard hematoxylin and eosin (H&E) staining. First, PARS label-free ultraviolet absorption images are captured directly within unstained tissue specimens. The radiative and non-radiative absorption images are then preprocessed, and virtually stained through the presented pathway. The preprocessing pipeline features a self-supervised Noise2Void denoising convolutional neural network (CNN) as well as a novel algorithm for pixel-level mechanical scanning error correction. These developments significantly enhance the recovery of sub-micron tissue structures, such as nucleoli location and chromatin distribution. Finally, we used a cycle-consistent generative adversarial network CycleGAN architecture to virtually stain the preprocessed PARS data. Virtual staining is applied to thin unstained sections of malignant human skin and breast tissue samples. Clinically relevant details are revealed, with comparable contrast and quality to gold standard H&E-stained images. This work represents a crucial step to deploying label-free microscopy as an alternative to standard histopathology techniques.

Histological analysis is crucial for the diagnosis of a wide variety of diseases. However, labelling of thin tissue sections can alter tissue chemistry and is greatly influenced by pre-analytic variables. Furthermore, biopsies provide a limited number of tissue sections and thus stains must be used sparingly. Total Absorption Photoacoustic Remote Sensing (TA-PARS) is an all-optical and label-free technique capable of capturing both radiative and non-radiative endogenous contrasts in cells, tissues and biomolecules. Here we present an automated full-slide TA-PARS scanning system capable of providing label-free virtually stained whole slide images with sufficient resolution (~300nm) to recover subcellular diagnostic characteristics.

Photon absorption remote sensing (PARS) is a new laser-based microscope technique that permits cellular-level resolution of unstained fresh, frozen, and fixed tissues. Our objective was to determine whether PARS could provide an image quality sufficient for the diagnostic assessment of breast cancer needle core biopsies (NCB). We PARS imaged and virtually H&E stained seven independent unstained formalin-fixed paraffin-embedded breast NCB sections. These identical tissue sections were subsequently stained with standard H&E and digitally scanned. Both the 40× PARS and H&E whole-slide images were assessed by seven breast cancer pathologists, masked to the origin of the images. A concordance analysis was performed to quantify the diagnostic performances of standard H&E and PARS virtual H&E. The PARS images were deemed to be of diagnostic quality, and pathologists were unable to distinguish the image origin, above that expected by chance. The diagnostic concordance on cancer vs. benign was high between PARS and conventional H&E (98% agreement) and there was complete agreement for within-PARS images. Similarly, agreement was substantial (kappa > 0.6) for specific cancer subtypes. PARS virtual H&E inter-rater reliability was broadly consistent with the published literature on diagnostic performance of conventional histology NCBs across all tested histologic features. PARS was able to image unstained tissues slides that were diagnostically equivalent to conventional H&E. Due to its ability to non-destructively image fixed and fresh tissues, and the suitability of the PARS output for artificial intelligence assistance in diagnosis, this technology has the potential to improve the speed and accuracy of breast cancer diagnosis.

Deep learning-based virtual staining is applied to total-absorption photoacoustic remote sensing (TA-PARS) imaging, demonstrating virtual histological images with equivalent quality and contrast to histochemical staining.

Histological imaging is essential for fields such as oncology, and biological research. However, capturing histological images requires tissue to be prepared in thin sections, then stained with dyes such as hematoxylin and eosin (H&E) to capture tissue morphology. This requires intensive processing, which fundamentally alters tissue structure and chemistry. Presented here is the second generation of photoacoustic remote sensing (SG-PARS) histology microscopes, which aim to circumvent current limitations, providing histological visualizations label-free directly within bulk unprocessed tissues. As photons interact with tissues, they may be scattered or absorbed, where absorption will cause the emission of photons (radiative relaxation) or the generation of heat and pressure (non-radiative relaxation). The SG-PARS features a refined architecture providing simultaneous sensitivity to optical scattering, non-radiative relaxation, and radiative relaxation. Leveraging these contrasts, the SG-PARS may provide visualizations in unstained specimens which directly emulate H&E staining. Combined with deep-learning based (cycleGAN) colorization, the SG-PARS rapidly generates emulated H&E images nearly equivalent to traditional H&E preparations. In addition, the SG-PARS may provide novel chromophore specific properties proposed as the total absorption (the combined radiative and non-radiative absorption magnitude) and the quantum efficiency ratio (QER) (the proportional radiative and non-radiative absorption response). These characteristics may enable visualizations with chromophore specificity beyond that provided by traditional H&E staining. In addition, the SG-PARS features extensive architecture innovations, such as a new 2.7 MHz excitation with a hybrid optomechanical scanning architecture, and a novel visible wavelength detection with a circulator-based pathway and an avalanche photodetector. Applied directly in unprocessed human tissues, the SG-PARS provides marked improvements in contrast, sensitivity, resolution, and scanning speed representing a vital milestone in the development of a microscope ready for clinical adoption.

Histopathological visualizations are a pillar of modern medicine and biological research. Surgical oncology relies exclusively on post-operative histology to determine definitive surgical success and guide adjuvant treatments. The current histology workflow is based on bright-field microscopic assessment of histochemical stained tissues and has some major limitations. For example, the preparation of stained specimens for brightfield assessment requires lengthy sample processing, delaying interventions for days or even weeks. Therefore, there is a pressing need for improved histopathology methods. In this paper, we present a deep-learning-based approach for virtual label-free histochemical staining of total-absorption photoacoustic remote sensing (TA-PARS) images of unstained tissue. TA-PARS provides an array of directly measured label-free contrasts such as scattering and total absorption (radiative and non-radiative), ideal for developing H&E colorizations without the need to infer arbitrary tissue structures. We use a Pix2Pix generative adversarial network to develop visualizations analogous to H&E staining from label-free TA-PARS images. Thin sections of human skin tissue were first virtually stained with the TA-PARS, then were chemically stained with H&E producing a one-to-one comparison between the virtual and chemical staining. The one-to-one matched virtually- and chemically- stained images exhibit high concordance validating the digital colorization of the TA-PARS images against the gold standard H&E. TA-PARS images were reviewed by four dermatologic pathologists who confirmed they are of diagnostic quality, and that resolution, contrast, and color permitted interpretation as if they were H&E. The presented approach paves the way for the development of TA-PARS slide-free histological imaging, which promises to dramatically reduce the time from specimen resection to histological imaging.

Photoacoustic Remote Sensing (PARS®) is a non-contact, label-free imaging modality that provides optical absorption contrast in biological tissues. Images are formed by raster-scanning over a target. A time-domain signal is collected at each point, representing initial pressure-induced via the photoacoustic effect. Conventionally, only the amplitude of the time-domain signals is considered to estimate pixel values, disregarding the rich temporal information present in the signals. For instance, the signal shape carries information, which may be related to specific biological structures. In this work, clustering based on signal shape is explored, followed by feature extraction, enabling the virtual labeling of PARS images.