Rapid ultraviolet photoacoustic remote sensing microscopy using voice-coil stage scanning.

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.

Virtual histopathology with ultraviolet scattering and photoacoustic remote sensing microscopy.

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.

Deep learning-enabled realistic virtual histology with ultraviolet photoacoustic remote sensing microscopy.

The goal of oncologic surgeries is complete tumor resection, yet positive margins are frequently found postoperatively using gold standard H&E-stained histology methods. Frozen section analysis is sometimes performed for rapid intraoperative margin evaluation, albeit with known inaccuracies. Here, we introduce a label-free histological imaging method based on an ultraviolet photoacoustic remote sensing and scattering microscope, combined with unsupervised deep learning using a cycle-consistent generative adversarial network for realistic virtual staining. Unstained tissues are scanned at rates of up to 7 mins/cm2, at resolution equivalent to 400x digital histopathology. Quantitative validation suggests strong concordance with conventional histology in benign and malignant prostate and breast tissues. In diagnostic utility studies we demonstrate a mean sensitivity and specificity of 0.96 and 0.91 in breast specimens, and respectively 0.87 and 0.94 in prostate specimens. We also find virtual stain quality is preferred (P = 0.03) compared to frozen section analysis in a blinded survey of pathologists.