Label-free intraoperative histology of bone tissue via deep-learning-assisted ultraviolet photoacoustic microscopy

Cao, Rui; Nelson, Scott D.; Davis, Samuel P. X.; Liang, Yu; Luo, Yilin; Zhang, Yide; Crawford, Brooke; Wang, Lihong V.

doi:10.1038/s41551-022-00940-z

Cited by 71 publications

(64 citation statements)

References 45 publications

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“…Therefore, PAISE can potentially be a powerful tool for brain science. With regard to clinical applications, PAISE may also enable single-shot stain-free histopathology combined with UV wavelengths, which would be significantly faster than current UV-PAM modalities that requires pointwise scanning [15,26,27].…”

Section: Discussionmentioning

confidence: 99%

Single-shot photoacoustic imaging with single-element transducer through a spatiotemporal encoder

Zhao¹,

Wang²

2023

Preprint

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Current photoacoustic (PA) imaging modalities typically require either serial detection with a single-element transducer or parallel detections with an ultrasonic array, indicating a dilemma between system cost and imaging throughput. Photoacoustic topography through ergodic relay (PATER) was recently developed to address this bottleneck. However, PATER requires object-specific calibration due to varied boundary condition, and must be re-calibrated through pointwise scanning for each object before measurements, which is time-consuming and severely limits practical application. Here, we develop a new method, photoacoustic imaging through a spatiotemporal encoder (PAISE), which is capable of single-shot widefield PA imaging with a single-element transducer, and does not require object-specific calibration. We validated PAISE through comprehensive numerical simulations and experiments, and demonstrated that PAISE can image different objects with a single calibration. Together, these results highlight the potential of PAISE for significant impact in both scientific research and clinical applications.

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Section: Discussionmentioning

confidence: 99%

Single-shot photoacoustic imaging with single-element transducer through a spatiotemporal encoder

Zhao¹,

Wang²

2023

Preprint

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“…Therefore, in the future, PAISE may be a powerful tool for brain science research. With regard to clinical applications, PAISE may also enable single-shot stain-free histopathology combined with UV wavelengths, which would be significantly faster than current UV-PAM modalities that requires pointwise scanning 15 , 29 , 30 …”

Section: Discussionmentioning

confidence: 99%

“…With regard to clinical applications, PAISE may also enable single-shot stain-free histopathology combined with UV wavelengths, which would be significantly faster than current UV-PAM modalities that requires pointwise scanning. 15,29,30 In summary, we have developed PAISE, a new imaging modality that can conduct single-shot imaging using a single-element transducer without the need of object-specific calibration, which successfully overcomes the major limitation of previous PATER technology. In the future, PAISE can be potentially useful for a wide range of biomedical applications.…”

Section: Discussionmentioning

confidence: 99%

Single-shot photoacoustic imaging with single-element transducer through a spatiotemporal encoder

Zhao

Wang

2023

J. Biomed. Opt.

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. Significance Current photoacoustic (PA) imaging modalities typically require either serial detection with a single-element transducer or parallel detections with an ultrasonic array, indicating a dilemma between system cost and imaging throughput. PA topography through ergodic relay (PATER) was recently developed to address this bottleneck. However, PATER requires object-specific calibration due to varied boundary condition and must be recalibrated through pointwise scanning for each object before measurements, which is time-consuming and severely limits practical application. Aim We aim to develop a new single-shot PA imaging technique that only requires a one-time calibration for imaging different objects using a single-element transducer. Approach We develop an imaging method, PA imaging through a spatiotemporal encoder (PAISE), to address the above issue. The spatial information is effectively coded into unique temporal features by the spatiotemporal encoder, which allows for compressive image reconstruction. An ultrasonic waveguide is proposed as a critical element to guide the PA waves from the object into the prism, which effectively accounts for the varied boundary condition of different objects. We further add irregular-shaped edges on the prism to introduce randomized internal reflections and further facilitate the scrambling of acoustic waves. Results The proposed technique is validated through comprehensive numerical simulations and experiments, and it is demonstrated that PAISE can successfully overcome the changed boundary condition and can image different samples given a single calibration. Conclusions The proposed PAISE technique is capable of single-shot widefield PA imaging with a single-element transducer and does not require sample-specific calibration, which successfully overcomes the major limitation of previous PATER technology.

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“…c Blind inference of a trained virtual staining model. The virtual histology images are rapidly generated from label-free images using a digital computer smears 39 , as well as H&E 40 and IHC 41 staining on prostate tissue sections; photoacoustic microscopy was also demonstrated to achieve virtual H&E staining of mouse brain 42 and frozen sections of bone tissue 43 . As another example, Mayerich.…”

Section: Label-free Virtual Stainingmentioning

confidence: 99%

Deep learning-enabled virtual histological staining of biological samples

Bai

Yang

et al. 2023

Light Sci Appl

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Histological staining is the gold standard for tissue examination in clinical pathology and life-science research, which visualizes the tissue and cellular structures using chromatic dyes or fluorescence labels to aid the microscopic assessment of tissue. However, the current histological staining workflow requires tedious sample preparation steps, specialized laboratory infrastructure, and trained histotechnologists, making it expensive, time-consuming, and not accessible in resource-limited settings. Deep learning techniques created new opportunities to revolutionize staining methods by digitally generating histological stains using trained neural networks, providing rapid, cost-effective, and accurate alternatives to standard chemical staining methods. These techniques, broadly referred to as virtual staining, were extensively explored by multiple research groups and demonstrated to be successful in generating various types of histological stains from label-free microscopic images of unstained samples; similar approaches were also used for transforming images of an already stained tissue sample into another type of stain, performing virtual stain-to-stain transformations. In this Review, we provide a comprehensive overview of the recent research advances in deep learning-enabled virtual histological staining techniques. The basic concepts and the typical workflow of virtual staining are introduced, followed by a discussion of representative works and their technical innovations. We also share our perspectives on the future of this emerging field, aiming to inspire readers from diverse scientific fields to further expand the scope of deep learning-enabled virtual histological staining techniques and their applications.

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Label-free intraoperative histology of bone tissue via deep-learning-assisted ultraviolet photoacoustic microscopy

Cited by 71 publications

References 45 publications

Single-shot photoacoustic imaging with single-element transducer through a spatiotemporal encoder

Single-shot photoacoustic imaging with single-element transducer through a spatiotemporal encoder

Single-shot photoacoustic imaging with single-element transducer through a spatiotemporal encoder

Deep learning-enabled virtual histological staining of biological samples

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