Hematoxylin and eosin (H&E) staining is the gold standard for most histopathological diagnostics but requires lengthy processing times not suitable for point-of-care diagnosis. Here we demonstrate a 266-nm excitation ultraviolet photoacoustic remote sensing (UV-PARS) and 1310-nm microscopy system capable of virtual H&E 3D imaging of tissues. Virtual hematoxylin staining of nuclei is achieved with UV-PARS, while virtual eosin staining is achieved using the already implemented interrogation laser from UV-PARS for scattering contrast. We demonstrate the capabilities of this dual-contrast system for en-face planar and depth-resolved imaging of human tissue samples exhibiting high concordance with H&E staining procedures and confocal fluorescence microscopy. To our knowledge, this is the first microscopy approach capable of depth-resolved imaging of unstained thick tissues with virtual H&E contrast.
Realistic label-free virtual histopathology has been a long sought-after goal not yet achieved with current methods. Here, we introduce high-resolution hematoxylin and eosin (H&E)-like virtual histology of unstained human breast lumpectomy specimen sections using ultraviolet scattering-augmented photoacoustic remote sensing microscopy. Together with a colormap-matching algorithm based on blind stain separation from a reference true H&E image, we are able to produce virtual H&E images of unstained tissues with close concordance to true H&E-stained sections, with promising diagnostic utility.
There is an unmet need for fast virtual histology technologies that exhibit histological realism and can scan large sections of fresh tissue within intraoperative time-frames. Ultraviolet photoacoustic remote sensing microscopy (UV-PARS) is an emerging imaging modality capable of producing virtual histology images that show good concordance to conventional histology stains. However, a UV-PARS scanning system that can perform rapid intraoperative imaging over mm-scale fields-of-view at fine resolution (<500 nm) has yet to be demonstrated. In this work, we present a UV-PARS system which utilizes voice-coil stage scanning to demonstrate finely resolved images for 2×2 mm2 areas at 500 nm sampling resolution in 1.33 minutes and coarsely resolved images for 4×4 mm2 areas at 900 nm sampling resolution in 2.5 minutes. The results of this work demonstrate the speed and resolution capabilities of the UV-PARS voice-coil system and further develop the potential for UV-PARS microscopy to be employed in a clinical setting.
The goal of oncologic surgeries is to completely resect tumor tissue, yet in up to 40% of such surgeries, positive marginsare found in the resected tissues. Postoperative histology using H&E-stained brightfield microscopy is the gold standard for determining margin status, but rapid frozen section analysis is sometimes performed for intraoperative guidance, albeit with inaccuracies. In this work, we introduce a virtual histological imaging method based on a non-contact, reflection-mode ultraviolet photoacoustic remote sensing and scattering microscope, combined with deep learning using a cycle-consistent generative adversarial network. The system is capable of high-resolution scanning with 390 nm resolution comparable toconventional histopathology, and fast widefield scanning, generating images with histological realism in freshly-resected thick tissues or thin sections. Cytologic and architectural features of interest are readily identifiable in virtual histology images of benign and malignant tissues. To evaluate system performance, a blinded study of pathologists rating image quality metrics was conducted, with our virtual histology approach offering preferred hematoxylin-like detail (P=0.0018) and overall stain quality (P=0.0321) compared to frozen section analysis.
Photoacoustic remote sensing (PARS) is a novel all-optical imaging modality that allows for non-contact detection of initial photoacoustic pressures. Using 266-nm excitation pulses, ultraviolet PARS (UV-PARS) has previously demonstrated imaging contrast for cell nuclei in histological samples with
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