SignificancePhotoacoustic microscopy (PAM) offers advantages in high-resolution and high-contrast imaging of biomedical chromophores. The speed of imaging is critical for leveraging these benefits in both preclinical and clinical settings. Ongoing technological innovations have substantially boosted PAM’s imaging speed, enabling real-time monitoring of dynamic biological processes.AimThis concise review synthesizes historical context and current advancements in high-speed PAM, with an emphasis on developments enabled by ultrafast lasers, scanning mechanisms, and advanced imaging processing methods.ApproachWe examine cutting-edge innovations across multiple facets of PAM, including light sources, scanning and detection systems, and computational techniques and explore their representative applications in biomedical research.Results:This work delineates the challenges that persist in achieving optimal high-speed PAM performance and forecasts its prospective impact on biomedical imaging.ConclusionsRecognizing the current limitations, breaking through the drawbacks, and adopting the optimal combination of each technology will lead to the realization of ultimate high-speed PAM for both fundamental research and clinical translation.
Traditional deconvolution methods can improve the spatial resolution of photoacoustic computed tomography (PACT) systems but are often sensitive to noise. We propose a novel approach to enhance the resolution of PACT, by modeling the system’s point spread function (PSF) and performing deep-learning-based deconvolution. We train a robust deep learning model without the need for ground truth, using a self-supervised method on a mixed dataset of simulation, phantom, and in vivo data, in combination with various data augmentation techniques. We demonstrate that our deep learning deconvolution achieves superior spatial resolution, image contrast, and artifact suppression, when compared to traditional deconvolution methods.
A unique deep learning network, Deep-E, is proposed, which utilizes 2D training data to solve a 3D problem. The novelty of this simulation method is to generate a 2D matrix in the axial-elevational plane using an arc-shaped transducer element, instead of generating a 3D matrix using the linear transducer arrays. Deep-E exhibited significant resolution improvement on the in vivo human breast data. In addition, we were able to restore deeper vascular structures and remove the noise artifact. We envision that Deep-E will have a significant impact in linear-array-based photoacoustic imaging studies by providing high-speed and high-resolution image enhancement.
We present a new functional photoacoustic microscopy system with the highest imaging speed and ultrawide field of view. The high imaging speed is enabled by a 12-facets polygon for fast scanning and a Raman-shifter system for fast dual-wavelength measurement of oxygen saturation in vivo. we imaged the dynamic functions in mouse brains in response to hypoxia challenge, sodium nitroprusside (SNP), and ischemic stroke. The experimental results have demonstrated that the high-speed photoacoustic microscopy system can be a powerful tool for studying the rapid hemodynamics in the mouse brains of a wide range of pathological and physiological models.
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