Pub Date : 2025-10-01Epub Date: 2025-08-13DOI: 10.1016/j.pacs.2025.100760
Xiaowei Chen , Xue Wen , Bingyan Fang , Zhixiong Lei , Jiarui Chen , Lvming Zeng , Kedi Xiong , Weizhan Luo , Lan Zhang , Hongbo Fu , Shiyue Li , Jian Zhang
Integrated photoacoustic endoscopy and endoscopic ultrasound (PAE/EUS) are recognized as an effective method for detecting intestinal and intravascular diseases. Changes in the morphology and composition of the trachea are significant hallmarks of respiratory diseases. In this study, an acoustic-optic confocal probe was developed and integrated at the tip of a 2.1 mm diameter catheter to perform simultaneous PAE/EUS imaging. Phantom experimental results demonstrated that the catheter achieved a high lateral resolution of 11 µm, with an imaging depth of 12 mm, using an excitation energy of 1.5 μJ. Trachea from healthy and chronic obstructive pulmonary disease (COPD) rabbit models and in vivo were imaged by the PAE/EUS system. The results demonstrated that photoacoustic imaging could identify increases in the diameter and density of the tracheal microvessels, while ultrasound imaging provided detailed views of the tracheal submucosa. These findings underscore the potential of PAE/EUS in the diagnosis of COPD.
{"title":"An acoustic-optic confocal probe based photoacoustic and ultrasonic tracheal endoscopy for characterizing phantom and model of chronic obstructive pulmonary disease","authors":"Xiaowei Chen , Xue Wen , Bingyan Fang , Zhixiong Lei , Jiarui Chen , Lvming Zeng , Kedi Xiong , Weizhan Luo , Lan Zhang , Hongbo Fu , Shiyue Li , Jian Zhang","doi":"10.1016/j.pacs.2025.100760","DOIUrl":"10.1016/j.pacs.2025.100760","url":null,"abstract":"<div><div>Integrated photoacoustic endoscopy and endoscopic ultrasound (PAE/EUS) are recognized as an effective method for detecting intestinal and intravascular diseases. Changes in the morphology and composition of the trachea are significant hallmarks of respiratory diseases. In this study, an acoustic-optic confocal probe was developed and integrated at the tip of a 2.1 mm diameter catheter to perform simultaneous PAE/EUS imaging. Phantom experimental results demonstrated that the catheter achieved a high lateral resolution of 11 µm, with an imaging depth of 12 mm, using an excitation energy of 1.5 μJ. Trachea from healthy and chronic obstructive pulmonary disease (COPD) rabbit models and <em>in vivo</em> were imaged by the PAE/EUS system. The results demonstrated that photoacoustic imaging could identify increases in the diameter and density of the tracheal microvessels, while ultrasound imaging provided detailed views of the tracheal submucosa. These findings underscore the potential of PAE/EUS in the diagnosis of COPD.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"45 ","pages":"Article 100760"},"PeriodicalIF":6.8,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144852379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01Epub Date: 2025-04-26DOI: 10.1016/j.pacs.2025.100726
Yu Zhang , Shuang Li , Yibing Wang , Yu Sun , Tingting Huang , Wenyi Xiang , Changhui Li
In reality, photoacoustic imaging (PAI) is generally influenced by artifacts caused by sparse array or limited view. In this work, to balance the computing cost and artifact removal performance, we propose an iterative optimization method that does not need to repeat solving forward model for every iteration circle, called the regularized iteration method with structural prior (RISP). The structural prior is a probability matrix derived from multiple reconstructed images via randomly selecting partial array elements. High-probability values indicate high coherency among multiple reconstruction results at those positions, suggesting a high likelihood of representing true imaging results. In contrast, low-probability values indicate higher randomness, leaning more towards artifacts or noise. As a structural prior, this probability matrix, together with the original PAI result using all array elements, guides the regularized iteration of the PAI results. The simulation and real animal and human PAI study results demonstrated our method can substantially reduce image artifacts, as well as noise.
{"title":"Iterative optimization algorithm with structural prior for artifacts removal of photoacoustic imaging","authors":"Yu Zhang , Shuang Li , Yibing Wang , Yu Sun , Tingting Huang , Wenyi Xiang , Changhui Li","doi":"10.1016/j.pacs.2025.100726","DOIUrl":"10.1016/j.pacs.2025.100726","url":null,"abstract":"<div><div>In reality, photoacoustic imaging (PAI) is generally influenced by artifacts caused by sparse array or limited view. In this work, to balance the computing cost and artifact removal performance, we propose an iterative optimization method that does not need to repeat solving forward model for every iteration circle, called the regularized iteration method with structural prior (RISP). The structural prior is a probability matrix derived from multiple reconstructed images via randomly selecting partial array elements. High-probability values indicate high coherency among multiple reconstruction results at those positions, suggesting a high likelihood of representing true imaging results. In contrast, low-probability values indicate higher randomness, leaning more towards artifacts or noise. As a structural prior, this probability matrix, together with the original PAI result using all array elements, guides the regularized iteration of the PAI results. The simulation and real animal and human PAI study results demonstrated our method can substantially reduce image artifacts, as well as noise.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"44 ","pages":"Article 100726"},"PeriodicalIF":7.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143904413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01Epub Date: 2025-06-06DOI: 10.1016/j.pacs.2025.100738
Farzin Ghane Golmohamadi, Amna Mehmood, Hoang Trong Phan, Franz-Josef Schmitt, Jan Laufer
Pump-probe excitation of fluorophores has been shown to overcome the limitations of conventional multiwavelength imaging and linear unmixing approaches by providing fluorophore-specific contrast whilst eliminating the dominant background signal of endogenous chromophores. In this study, methods for generating pump-probe signals and images are investigated that rely on changing 1) the pump wavelength whilst keeping the probe wavelength fixed, 2) the probe wavelength whilst keeping the pump wavelength fixed, and 3) the time delay between the pump and probe pulse. Time-resolved PA signals were generated in purified solutions of genetically expressed red fluorescent proteins Katushka, mNeptune, and mCardinal in a cuvette. Spectra of the difference signal amplitude were found to correlate with the absorption and emission spectra. The difference signal plotted as a function of time delay also showed characteristic features for each protein. To demonstrate the capability of multiplexed imaging, the spatial distributions of Katushka and mNeptune were recovered from 2D difference images of a phantom. This study demonstrates that methods based on pump-probe excitation can be used to probe the photophysical properties of fluorophores. By detecting changes in these properties due to a stimulant, such as pH, the methods may find application in biosensing of the cellular microenvironment.
{"title":"Probing the photophysical properties of fluorescent proteins using photoacoustic pump-probe spectroscopy and imaging","authors":"Farzin Ghane Golmohamadi, Amna Mehmood, Hoang Trong Phan, Franz-Josef Schmitt, Jan Laufer","doi":"10.1016/j.pacs.2025.100738","DOIUrl":"10.1016/j.pacs.2025.100738","url":null,"abstract":"<div><div>Pump-probe excitation of fluorophores has been shown to overcome the limitations of conventional multiwavelength imaging and linear unmixing approaches by providing fluorophore-specific contrast whilst eliminating the dominant background signal of endogenous chromophores. In this study, methods for generating pump-probe signals and images are investigated that rely on changing 1) the pump wavelength whilst keeping the probe wavelength fixed, 2) the probe wavelength whilst keeping the pump wavelength fixed, and 3) the time delay between the pump and probe pulse. Time-resolved PA signals were generated in purified solutions of genetically expressed red fluorescent proteins Katushka, mNeptune, and mCardinal in a cuvette. Spectra of the difference signal amplitude were found to correlate with the absorption and emission spectra. The difference signal plotted as a function of time delay also showed characteristic features for each protein. To demonstrate the capability of multiplexed imaging, the spatial distributions of Katushka and mNeptune were recovered from 2D difference images of a phantom. This study demonstrates that methods based on pump-probe excitation can be used to probe the photophysical properties of fluorophores. By detecting changes in these properties due to a stimulant, such as pH, the methods may find application in biosensing of the cellular microenvironment.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"44 ","pages":"Article 100738"},"PeriodicalIF":7.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144280282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01Epub Date: 2025-06-04DOI: 10.1016/j.pacs.2025.100733
Tianrui Zhao , Edward Zhang , Paul C. Beard , Wenfeng Xia
Photoacoustic endoscopy has gained intensive research interest in recent years, particularly for guiding minimally invasive procedures in several clinical disciplines including oncology, cardiology and fetal medicine. Multimode fibres hold the potential to revolutionise medical endoscopy with ultrathin size and micrometre-level resolution. Compared to conventional endomicroscopes based on multi-core fibre bundles, multimode fibres-based endoscopes offer significantly higher spatial resolution, smaller diameters, and lower costs. However, current implementations of multimode fibre imaging, whether using raster-scan or speckle compressive sensing imaging, are hindered by limitations in frame rate or signal-to-noise ratio. In this work, we developed a multifocal excitation compressive-sensing photoacoustic endomicroscopy system that combines wavefront shaping-based light focusing with compressive sensing to achieve high imaging speed without compromising image quality. The method was validated through numerical simulations and experiments with carbon fibre phantoms and red blood cells ex vivo. Our results demonstrated comparable image quality to raster-scan-based imaging, while improving the frame rate by a factor of 5, reaching 11.5 frames per second. With further enhancements in focusing performance and the use of a higher repetition rate laser, this method shows promise for achieving real-time, high-resolution endomicroscopy through ultrathin probes, making it a valuable tool for guiding minimally invasive procedures.
{"title":"MECOPE: Multifocal excitation compressive-sensing photoacoustic endomicroscopy through a multimode fibre","authors":"Tianrui Zhao , Edward Zhang , Paul C. Beard , Wenfeng Xia","doi":"10.1016/j.pacs.2025.100733","DOIUrl":"10.1016/j.pacs.2025.100733","url":null,"abstract":"<div><div>Photoacoustic endoscopy has gained intensive research interest in recent years, particularly for guiding minimally invasive procedures in several clinical disciplines including oncology, cardiology and fetal medicine. Multimode fibres hold the potential to revolutionise medical endoscopy with ultrathin size and micrometre-level resolution. Compared to conventional endomicroscopes based on multi-core fibre bundles, multimode fibres-based endoscopes offer significantly higher spatial resolution, smaller diameters, and lower costs. However, current implementations of multimode fibre imaging, whether using raster-scan or speckle compressive sensing imaging, are hindered by limitations in frame rate or signal-to-noise ratio. In this work, we developed a multifocal excitation compressive-sensing photoacoustic endomicroscopy system that combines wavefront shaping-based light focusing with compressive sensing to achieve high imaging speed without compromising image quality. The method was validated through numerical simulations and experiments with carbon fibre phantoms and red blood cells <em>ex vivo</em>. Our results demonstrated comparable image quality to raster-scan-based imaging, while improving the frame rate by a factor of 5, reaching 11.5 frames per second. With further enhancements in focusing performance and the use of a higher repetition rate laser, this method shows promise for achieving real-time, high-resolution endomicroscopy through ultrathin probes, making it a valuable tool for guiding minimally invasive procedures.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"44 ","pages":"Article 100733"},"PeriodicalIF":7.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144263650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Model-based reconstruction provides state-of-the-art image quality for multispectral optoacoustic tomography. However, optimal regularization of in vivo data necessitates scan-specific adjustments of the regularization strength to compensate for fluctuations of the signal magnitudes between different sinograms. Magnitude fluctuations within in vivo data also pose a challenge for supervised deep learning of a model-based reconstruction operator, as training data must cover the complete range of expected signal magnitudes. In this work, we derive a scale-equivariant model-based reconstruction operator that i) automatically adjusts the regularization strength based on the norm of the input sinogram, and ii) facilitates supervised deep learning of the operator using input singorams with a fixed norm. Scale-equivariant model-based reconstruction applies appropriate regularization to sinograms of arbitrary magnitude, achieves slightly better accuracy in quantifying blood oxygen saturation, and enables more accurate supervised deep learning of the operator.
{"title":"Scale-equivariant deep model-based optoacoustic image reconstruction","authors":"Christoph Dehner , Ledia Lilaj , Vasilis Ntziachristos , Guillaume Zahnd , Dominik Jüstel","doi":"10.1016/j.pacs.2025.100727","DOIUrl":"10.1016/j.pacs.2025.100727","url":null,"abstract":"<div><div>Model-based reconstruction provides state-of-the-art image quality for multispectral optoacoustic tomography. However, optimal regularization of in vivo data necessitates scan-specific adjustments of the regularization strength to compensate for fluctuations of the signal magnitudes between different sinograms. Magnitude fluctuations within in vivo data also pose a challenge for supervised deep learning of a model-based reconstruction operator, as training data must cover the complete range of expected signal magnitudes. In this work, we derive a scale-equivariant model-based reconstruction operator that <em>i)</em> automatically adjusts the regularization strength based on the <span><math><msup><mrow><mi>L</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> norm of the input sinogram, and <em>ii)</em> facilitates supervised deep learning of the operator using input singorams with a fixed norm. Scale-equivariant model-based reconstruction applies appropriate regularization to sinograms of arbitrary magnitude, achieves slightly better accuracy in quantifying blood oxygen saturation, and enables more accurate supervised deep learning of the operator.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"44 ","pages":"Article 100727"},"PeriodicalIF":7.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144069772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01Epub Date: 2025-06-04DOI: 10.1016/j.pacs.2025.100739
Eunwoo Park , Donggyu Kim , Mingyu Ha , Donghyun Kim , Chulhong Kim
Photoacoustic microscopy (PAM), an imaging modality with emerging importance in diverse biomedical applications, provides excellent structural and functional information at the micro-scale. Technological innovations have significantly enhanced PAM’s performance, including sensitivity and contrast, making it a powerful tool. This review explores high-performance PAM, focusing on its signal-to-noise ratio, imaging speed, resolution, depth, functionality, and practicality, and commenting on the role of artificial intelligence in enhancing each feature. After providing comprehensive insights, the review concludes with future directions for developing high-performance PAM for advanced biomedical imaging and clinical applications.
{"title":"A comprehensive review of high-performance photoacoustic microscopy systems","authors":"Eunwoo Park , Donggyu Kim , Mingyu Ha , Donghyun Kim , Chulhong Kim","doi":"10.1016/j.pacs.2025.100739","DOIUrl":"10.1016/j.pacs.2025.100739","url":null,"abstract":"<div><div>Photoacoustic microscopy (PAM), an imaging modality with emerging importance in diverse biomedical applications, provides excellent structural and functional information at the micro-scale. Technological innovations have significantly enhanced PAM’s performance, including sensitivity and contrast, making it a powerful tool. This review explores high-performance PAM, focusing on its signal-to-noise ratio, imaging speed, resolution, depth, functionality, and practicality, and commenting on the role of artificial intelligence in enhancing each feature. After providing comprehensive insights, the review concludes with future directions for developing high-performance PAM for advanced biomedical imaging and clinical applications.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"44 ","pages":"Article 100739"},"PeriodicalIF":7.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144222615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01Epub Date: 2025-05-16DOI: 10.1016/j.pacs.2025.100732
Souradip Paul , S. Alex Lee , Shensheng Zhao , Yun-Sheng Chen
Photoacoustic tomography (PAT), widely applied using linear array ultrasound transducers for clinical and preclinical imaging, faces significant challenges due to sparse sensor arrangements and limited sensor pitch. These factors often compromise image quality, particularly in devices designed to have fewer sensors to reduce complexity and power consumption, such as wearable systems. Conventional reconstruction methods, including delay-and-sum and iterative model-based techniques, either lack accuracy or are computationally intensive. Recent advancements in deep learning offer promising improvements. In particular, model-based deep learning combines physics-informed priors with neural networks to enhance reconstruction quality and reduce computational demands. However, model matrix inversion during adjoint transformations presents computational challenges in model-based deep learning. To address the challenges, we introduce a simplified, efficient GE-CNN framework specifically tailored for linear array transducers. Our lightweight GE-CNN architecture significantly reduces computational demand, achieving a 4-fold reduction in model matrix size (2.09 GB for 32 elements vs. 8.38 GB for 128 elements) and accelerating processing by approximately 46.3 %, reducing the processing time from 7.88 seconds to 4.23 seconds. We rigorously evaluated this approach using synthetic models, experimental phantoms, and in-vivo rat liver imaging, highlighting the improved reconstruction performance with minimal hardware.
{"title":"Model-informed deep-learning photoacoustic reconstruction for low-element linear array","authors":"Souradip Paul , S. Alex Lee , Shensheng Zhao , Yun-Sheng Chen","doi":"10.1016/j.pacs.2025.100732","DOIUrl":"10.1016/j.pacs.2025.100732","url":null,"abstract":"<div><div>Photoacoustic tomography (PAT), widely applied using linear array ultrasound transducers for clinical and preclinical imaging, faces significant challenges due to sparse sensor arrangements and limited sensor pitch. These factors often compromise image quality, particularly in devices designed to have fewer sensors to reduce complexity and power consumption, such as wearable systems. Conventional reconstruction methods, including delay-and-sum and iterative model-based techniques, either lack accuracy or are computationally intensive. Recent advancements in deep learning offer promising improvements. In particular, model-based deep learning combines physics-informed priors with neural networks to enhance reconstruction quality and reduce computational demands. However, model matrix inversion during adjoint transformations presents computational challenges in model-based deep learning. To address the challenges, we introduce a simplified, efficient GE-CNN framework specifically tailored for linear array transducers. Our lightweight GE-CNN architecture significantly reduces computational demand, achieving a 4-fold reduction in model matrix size (2.09 GB for 32 elements vs. 8.38 GB for 128 elements) and accelerating processing by approximately 46.3 %, reducing the processing time from 7.88 seconds to 4.23 seconds. We rigorously evaluated this approach using synthetic models, experimental phantoms, and in-vivo rat liver imaging, highlighting the improved reconstruction performance with minimal hardware.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"44 ","pages":"Article 100732"},"PeriodicalIF":7.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01Epub Date: 2025-05-29DOI: 10.1016/j.pacs.2025.100731
Xiaoxue Wang , Jinzhuang Xu , Chenglong Zhang , Moritz Wildgruber , Wenjing Jiang , Lili Wang , Xiaopeng Ma
Photoacoustic tomography (PAT) combines the high spatial resolution of ultrasound imaging with the high contrast of optical imaging. To reduce acquisition time and lower the cost of photoacoustic imaging, sparse sampling strategy is often employed. Conventional reconstruction methods often produce artifacts when dealing with sparse data, affecting image quality and diagnostic accuracy. This paper proposes a Residual-Conditioned Sparse Transformer (RCST) network for reducing artifacts in photoacoustic images, aiming to enhance image quality under sparse sampling. By introducing residual prior information, our algorithm encodes and embeds it into local enhancement and detail recovery stages. We utilize sparse transformer blocks to identify and reduce artifacts while preserving key structures and details of the images. Experiments on multiple simulated and experimental datasets demonstrate that our method significantly suppresses artifacts and improves image quality, offering new possibilities for the application of photoacoustic imaging in biomedical research and clinical diagnostics.
{"title":"Residual-conditioned sparse transformer for photoacoustic image artifact reduction","authors":"Xiaoxue Wang , Jinzhuang Xu , Chenglong Zhang , Moritz Wildgruber , Wenjing Jiang , Lili Wang , Xiaopeng Ma","doi":"10.1016/j.pacs.2025.100731","DOIUrl":"10.1016/j.pacs.2025.100731","url":null,"abstract":"<div><div>Photoacoustic tomography (PAT) combines the high spatial resolution of ultrasound imaging with the high contrast of optical imaging. To reduce acquisition time and lower the cost of photoacoustic imaging, sparse sampling strategy is often employed. Conventional reconstruction methods often produce artifacts when dealing with sparse data, affecting image quality and diagnostic accuracy. This paper proposes a Residual-Conditioned Sparse Transformer (RCST) network for reducing artifacts in photoacoustic images, aiming to enhance image quality under sparse sampling. By introducing residual prior information, our algorithm encodes and embeds it into local enhancement and detail recovery stages. We utilize sparse transformer blocks to identify and reduce artifacts while preserving key structures and details of the images. Experiments on multiple simulated and experimental datasets demonstrate that our method significantly suppresses artifacts and improves image quality, offering new possibilities for the application of photoacoustic imaging in biomedical research and clinical diagnostics.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"44 ","pages":"Article 100731"},"PeriodicalIF":7.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144185200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01Epub Date: 2025-06-16DOI: 10.1016/j.pacs.2025.100744
Seongjin Bak , Sang Min Park , Yuon Song , Jeesu Kim , Tae Won Nam , Dong-Wook Han , Chang-Seok Kim , Soon-Woo Cho , Brett E. Bouma , Hwidon Lee
We present a high spectral energy density all-fiber nanosecond pulsed 1.7 μm light source specifically designed for photoacoustic microscopy (PAM). The system targets the 1st overtone absorption of C–H bonds near 1720 nm within the near-infrared-III (NIR-III) window, where lipids exhibit strong optical absorption, and tissues benefit from reduced scattering and high permissible fluence. To achieve narrow-linewidth, high pulse energy, and high pulse repetition rate (PRR), we developed a master oscillator fiber amplifier architecture based on stimulated Raman scattering. A 1589.80 nm Raman pump and a custom-built narrow-linewidth Raman seed laser were employed to generate spectrally pure 1719.44 nm pulses (∼0.10 nm linewidth). The proposed light source delivers nanosecond pulses (∼5 ns) with high pulse energy (≥2.2 μJ) and tunable PRRs up to 300 kHz, resulting in a spectral energy density of approximately 22 μJ/nm—significantly higher than that of conventional 1.7 μm light sources. Performance of the NIR-PAM system was validated through resolution testing with a 1951 USAF target, demonstrating a spatial resolution of approximately 4.14 μm and an axial resolution of approximately 85.5 μm. Phantom imaging of CH2-rich polymer films and ex vivo lipid-rich biological tissues confirmed the system’s high spatial fidelity and strong contrast for lipid-specific structures. This compact, stable, and spectrally refined light source with high spectral energy density can offer an effective solution for high-resolution, label-free molecular imaging and represents a promising platform for clinical photoacoustic imaging applications involving lipid detection and metabolic disease diagnostics.
{"title":"High spectral energy density all-fiber nanosecond pulsed 1.7 μm light source for photoacoustic microscopy","authors":"Seongjin Bak , Sang Min Park , Yuon Song , Jeesu Kim , Tae Won Nam , Dong-Wook Han , Chang-Seok Kim , Soon-Woo Cho , Brett E. Bouma , Hwidon Lee","doi":"10.1016/j.pacs.2025.100744","DOIUrl":"10.1016/j.pacs.2025.100744","url":null,"abstract":"<div><div>We present a high spectral energy density all-fiber nanosecond pulsed 1.7 μm light source specifically designed for photoacoustic microscopy (PAM). The system targets the 1st overtone absorption of C–H bonds near 1720 nm within the near-infrared-III (NIR-III) window, where lipids exhibit strong optical absorption, and tissues benefit from reduced scattering and high permissible fluence. To achieve narrow-linewidth, high pulse energy, and high pulse repetition rate (PRR), we developed a master oscillator fiber amplifier architecture based on stimulated Raman scattering. A 1589.80 nm Raman pump and a custom-built narrow-linewidth Raman seed laser were employed to generate spectrally pure 1719.44 nm pulses (∼0.10 nm linewidth). The proposed light source delivers nanosecond pulses (∼5 ns) with high pulse energy (≥2.2 μJ) and tunable PRRs up to 300 kHz, resulting in a spectral energy density of approximately 22 μJ/nm—significantly higher than that of conventional 1.7 μm light sources. Performance of the NIR-PAM system was validated through resolution testing with a 1951 USAF target, demonstrating a spatial resolution of approximately 4.14 μm and an axial resolution of approximately 85.5 μm. Phantom imaging of CH<sub>2</sub>-rich polymer films and ex vivo lipid-rich biological tissues confirmed the system’s high spatial fidelity and strong contrast for lipid-specific structures. This compact, stable, and spectrally refined light source with high spectral energy density can offer an effective solution for high-resolution, label-free molecular imaging and represents a promising platform for clinical photoacoustic imaging applications involving lipid detection and metabolic disease diagnostics.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"44 ","pages":"Article 100744"},"PeriodicalIF":7.1,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-01Epub Date: 2025-06-11DOI: 10.1016/j.pacs.2025.100741
Jianshuang Wei , Ren Zhang , Mingchen Jiang , Lulu Gao , Ximiao Yu , XiuLi Liu , Yanfeng Dai , Qingming Luo , Zhihong Zhang , Xiaoquan Yang
Non-alcoholic steatohepatitis (NASH) is a prevalent chronic liver disease characterized by significant alterations in liver microvascular structures, leading to microcirculatory dysfunction and potentially contributing to various extrahepatic complications. In this study, we propose a longitudinal investigative pipeline based on liver photoacoustic microscopy (LPAM), integrating optical-resolution photoacoustic microscopy (OR-PAM), a modular liver window (MLW), a custom 3D-printed liver imaging mount (LIM), and a dedicated vessel-sinusoid separation and analysis method. This pipeline enabled continuous monitoring and quantitative assessment of microvascular changes in a NASH mouse model over a six-week period. As NASH progressed, vessel density decreased by 64.18 %, and hepatic sinusoid vessel coverage was reduced by 77.38 %. Furthermore, hepatic sinusoidal volume, length, radius, tortuosity, and density declined by 87.29 %, 83.92 %, 21.86 %, 71.57 %, and 86.81 %, Analysis of hepatic sinusoidal branches revealed a 51.80 % decrease in the fractal dimension of composite branches and a 54.90 % increase in that of dead-end branches. These findings suggest that lipid accumulation and inflammatory responses contribute to the progressive deterioration of hepatic microvascular structures, thereby exacerbating vascular damage. LPAM offers a high-resolution, label-free imaging approach for dynamic monitoring of NASH-associated microvascular alterations. This study advances our understanding of hepatic microcirculatory changes in NASH and provides valuable insights for both basic research and clinical management.
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