high-speed-ultraviolet-photoacoustic-microscopy-for-histological

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Surgical margin analysis (SMA), an essential procedure to confirm the complete excision of cancerous tissue in tumor resection surgery, requires ...

Hong Kong University of Science and Technology

Laser diodes (LDs) have been considered as cost-effective and compact excitation sources to overcome the requirement of costly and bulky pulsed laser sources that are commonly used in photoacoustic microscopy (PAM). However, the spatial resolution and/or imaging speed of previously reported LD-based PAM systems have not been optimized simultaneously. In this paper, we developed a high-speed and high-resolution LD-based PAM system using a continuous wave LD, operating at a pulsed mode, with a repetition rate of 30 kHz, as an excitation source. A hybrid scanning mechanism that synchronizes a one-dimensional galvanometer mirror and a two-dimensional motorized stage is applied to achieve a fast imaging capability without signal averaging due to the high signal-to-noise ratio. By optimizing the optical system, a high lateral resolution of 4.8 μm has been achieved. In vivo microvasculature imaging of a mouse ear has been demonstrated to show the high performance of our LD-based PAM system.

High-speed high-resolution laser diode-based photoacoustic microscopy for in vivo microvasculature imaging.

Histological images can reveal rich cellular information of tissue sections, which are widely used by pathologists in disease diagnosis. However, the gold standard for histopathological examination is based on thin sections on slides, which involves inevitable time-consuming and labor-intensive tissue processing steps, hindering the possibility of intraoperative pathological assessment of the precious patient specimens. Here, by incorporating ultraviolet photoacoustic microscopy (UV-PAM) with deep learning, we show a rapid and label-free histological imaging method that can generate virtually stained histological images (termed Deep-PAM) for both thin sections and thick fresh tissue specimens. With the tissue non-destructive nature of UV-PAM, the imaged intact specimens can be reused for other ancillary tests. We demonstrated Deep-PAM on various tissue preparation protocols, including formalin-fixation and paraffin-embedding sections (7-µm thick) and frozen sections (7-µm thick) in traditional histology, and rapid assessment of intact fresh tissue (~ 2-mm thick, within 15 min for a tissue with a surface area of 5 mm × 5 mm). Deep-PAM potentially serves as a comprehensive histological imaging method that can be simultaneously applied in preoperative, intraoperative, and postoperative disease diagnosis.

Deep learning enables ultraviolet photoacoustic microscopy based histological imaging with near real-time virtual staining.

Rapid and high-resolution histological imaging with minimal tissue preparation has long been a challenging and yet captivating medical pursuit. Here, the authors propose a promising and transformative histological imaging method, termed computational high-throughput autofluorescence microscopy by pattern illumination (CHAMP). With the assistance of computational microscopy, CHAMP enables high-throughput and label-free imaging of thick and unprocessed tissues with large surface irregularity at an acquisition speed of 10 mm /10 s with 1.1-µm lateral resolution. Moreover, the CHAMP image can be transformed into a virtually stained histological image (Deep-CHAMP) through unsupervised learning within 15 s, where significant cellular features are quantitatively extracted with high accuracy. The versatility of CHAMP is experimentally demonstrated using mouse brain/kidney and human lung tissues prepared with various clinical protocols, which enables a rapid and accurate intraoperative/postoperative pathological examination without tissue processing or staining, demonstrating its great potential as an assistive imaging platform for surgeons and pathologists to provide optimal adjuvant treatment.

High-Throughput, Label-Free and Slide-Free Histological Imaging by Computational Microscopy and Unsupervised Learning.

Ultraviolet photoacoustic microscopy (UV-PAM) has been investigated to provide label-free and registration-free volumetric histological images for whole organs, offering new insights into complex biological organs. However, because of the high UV absorption of lipids and pigments in tissue, UV-PAM suffers from low image contrast and shallow image depth, hindering its capability for revealing various microstructures in organs. To improve the UV-PAM imaging contrast and imaging depth, here we propose to implement a state-of-the-art optical clearing technique, CUBIC (clear, unobstructed brain/body imaging cocktails and computational analysis), to wash out the lipids and pigments from tissues. Our results show that the UV-PAM imaging contrast and quality can be significantly improved after tissue clearing. With the cleared tissue, multilayers of cell nuclei can also be extracted from time-resolved PA signals. Tissue clearing-enhanced UV-PAM can provide fine details for organ imaging.

Ultraviolet photoacoustic microscopy with tissue clearing for high-contrast histological imaging.

Three-dimensional (3D) histology is vitally important to characterize disease-induced tissue heterogeneity at the individual cell level. However, it remains challenging for both high-throughput 3D imaging and volumetric reconstruction. Here we propose a label-free, cost-effective, and ready-to-use 3D histological imaging technique, termed microtomy-assisted autofluorescence tomography with ultraviolet excitation (MATE). With the combination of block-face imaging and serial microtome sectioning, MATE can achieve rapid and label-free imaging of paraffin-embedded whole organs at an acquisition speed of 1 cm per 4 h with a voxel resolution of 1.2 × 1.2 × 10 μm. We demonstrate that MATE enables simultaneous visualization of cell nuclei, fiber tracts, and blood vessels in mouse/human brains without tissue staining or clearing. Moreover, diagnostic features, including nuclear size and packing density, can be quantitatively extracted with high accuracy. MATE is augmented to the current slide-based 2D histology, holding great promise to facilitate histopathological interpretation at the organelle level.

Lei Kang

Translational and Advanced Bioimaging Laboratory,
Department of Chemical and Biological Engineering,
Translational and Advanced Bioimaging Laboratory, Department of Chemical and Biological Engineering

Hong Kong University of Science and Technology

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Lei Kang

Translational and Advanced Bioimaging Laboratory,Department of Chemical and Biological Engineering,Translational and Advanced Bioimaging Laboratory, Department of Chemical and Biological Engineering

Hong Kong University of Science and Technology

High-Speed Ultraviolet Photoacoustic Microscopy for Histological Imaging with Virtual-Staining assisted by Deep Learning

Translational and Advanced Bioimaging Laboratory,
Department of Chemical and Biological Engineering,
Translational and Advanced Bioimaging Laboratory, Department of Chemical and Biological Engineering