Pub Date : 2025-05-30DOI: 10.1016/j.pacs.2025.100737
Zhigang Wang , Changpeng Ai , Ting Sun , Zhiyang Wang , Wuyu Zhang , Feifan Zhou , Shengnan Wu
Sepsis-associated encephalopathy (SAE) is a common complication of sepsis, involving acute brain dysfunction. Although cerebrovascular impairment plays a critical role in SAE, sepsis-induced microvascular changes remain poorly quantified. Here, we used photoacoustic microscopy to dynamically assess blood-brain barrier permeability in septic mice, analyzing vascular structure across five parameters. Additionally, we examined pathological changes in major cortical regions and conducted behavioral tests to validate the findings. Results showed microvascular degeneration, including reduced vascular density and branching, alongside an increase in fine vessels. Motor-related cortical areas were most affected, correlating with severe motor and cognitive deficits in septic mice. This study provides the first in vivo, multi-parametric analysis of sepsis-induced cerebrovascular pathology, revealing region-specific damage. Our findings directly link microvascular dysfunction to SAE progression and highlight photoacoustic microscopy’s potential in neuroscience research.
{"title":"Photoacoustic imaging detects cerebrovascular pathological changes in sepsis","authors":"Zhigang Wang , Changpeng Ai , Ting Sun , Zhiyang Wang , Wuyu Zhang , Feifan Zhou , Shengnan Wu","doi":"10.1016/j.pacs.2025.100737","DOIUrl":"10.1016/j.pacs.2025.100737","url":null,"abstract":"<div><div>Sepsis-associated encephalopathy (SAE) is a common complication of sepsis, involving acute brain dysfunction. Although cerebrovascular impairment plays a critical role in SAE, sepsis-induced microvascular changes remain poorly quantified. Here, we used photoacoustic microscopy to dynamically assess blood-brain barrier permeability in septic mice, analyzing vascular structure across five parameters. Additionally, we examined pathological changes in major cortical regions and conducted behavioral tests to validate the findings. Results showed microvascular degeneration, including reduced vascular density and branching, alongside an increase in fine vessels. Motor-related cortical areas were most affected, correlating with severe motor and cognitive deficits in septic mice. This study provides the first <em>in vivo</em>, multi-parametric analysis of sepsis-induced cerebrovascular pathology, revealing region-specific damage. Our findings directly link microvascular dysfunction to SAE progression and highlight photoacoustic microscopy’s potential in neuroscience research.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"44 ","pages":"Article 100737"},"PeriodicalIF":7.1,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144189517","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 : 2025-05-29DOI: 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":"2025-05-29","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 : 2025-05-29DOI: 10.1016/j.pacs.2025.100734
Daria Voitovich , Alexey Kurnikov , Anna Orlova , Aleksej Petushkov , Liubov Shimolina , Anastasia Komarova , Marina Shirmanova , Yu-Hang Liu , Daniel Razansky , Pavel Subochev
Optical-resolution optoacoustic (photoacoustic) microscopy is a hybrid imaging modality combining focused optical excitation with ultrasound detection, thus achieving micrometer-scale spatial resolution and high-contrast angiographic imaging. Despite these important advantages, maintaining safe laser fluence levels is essential to prevent tissue damage while ensuring sufficient detection sensitivity. Here, we introduce a model that directly relates the detector’s noise-equivalent pressure (NEP) to the local laser fluence at the imaged blood vessel. The model incorporates acoustic propagation effects from an optoacoustic source to a spherically focused detector with limited aperture and bandwidth, offering a more comprehensive understanding of how fluence and ultrasonic sensitivity are interconnected. The effects of ultrasound generation propagation and detection were accounted for using analytical estimations and numerical simulations, while detector's NEP was experimentally measured with a calibrated hydrophone. The proposed model for evaluating of local laser fluence with a calibrated ultrasound detector was validated through in vitro experiments with superficially located blood layer and numerical Monte Carlo/k-Wave simulations featuring deeper vessels. In vivo experiments employing 532 nm laser excitation and wideband 1–30 MHz ultrasonic detection further demonstrated the model’s capacity for real-time adjustments of laser parameters to ensure tissue safety.
{"title":"Local laser fluence estimation in optical resolution optoacoustic angiography employing calibrated ultrasound detector","authors":"Daria Voitovich , Alexey Kurnikov , Anna Orlova , Aleksej Petushkov , Liubov Shimolina , Anastasia Komarova , Marina Shirmanova , Yu-Hang Liu , Daniel Razansky , Pavel Subochev","doi":"10.1016/j.pacs.2025.100734","DOIUrl":"10.1016/j.pacs.2025.100734","url":null,"abstract":"<div><div>Optical-resolution optoacoustic (photoacoustic) microscopy is a hybrid imaging modality combining focused optical excitation with ultrasound detection, thus achieving micrometer-scale spatial resolution and high-contrast angiographic imaging. Despite these important advantages, maintaining safe laser fluence levels is essential to prevent tissue damage while ensuring sufficient detection sensitivity. Here, we introduce a model that directly relates the detector’s noise-equivalent pressure (NEP) to the local laser fluence at the imaged blood vessel. The model incorporates acoustic propagation effects from an optoacoustic source to a spherically focused detector with limited aperture and bandwidth, offering a more comprehensive understanding of how fluence and ultrasonic sensitivity are interconnected. The effects of ultrasound generation propagation and detection were accounted for using analytical estimations and numerical simulations, while detector's NEP was experimentally measured with a calibrated hydrophone. The proposed model for evaluating of local laser fluence with a calibrated ultrasound detector was validated through in vitro experiments with superficially located blood layer and numerical Monte Carlo/k-Wave simulations featuring deeper vessels. In vivo experiments employing 532 nm laser excitation and wideband 1–30 MHz ultrasonic detection further demonstrated the model’s capacity for real-time adjustments of laser parameters to ensure tissue safety.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"44 ","pages":"Article 100734"},"PeriodicalIF":7.1,"publicationDate":"2025-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314645","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}
Photoacoustic imaging combines the advantages of optical and acoustic imaging, making it a promising tool in biomedical imaging. Photoacoustic tomography (PAT) reconstructs images by solving the inverse problem from detected photoacoustic waves to initial pressure map. The heterogeneous speed of sound (SoS) distribution in biological tissue significantly affects image quality, as uncertain SoS variations can cause image distortions. Previously reported dual-speed-of-sound (dual-SoS) imaging methods effectively address these distortions by accounting for the SoS differences between tissues and the coupling medium. However, these methods require recalculating the distribution parameters of the SoS for each frame during dynamic imaging, which is highly time-consuming and poses a significant challenge for achieving real-time dynamic dual-SoS PAT imaging. To address this issue, we propose a signal-domain dual-SoS correction method for PAT image reconstruction. In this method, two distinct SoS regions are differentiated by recognizing the photoacoustic signal features of the imaging target's contours. The signals are then corrected based on the respective SoS values, enabling signal-domain-based dual-SoS dynamic real-time PAT imaging. The proposed method was validated through numerical simulations and in-vivo experiments of human finger. The results show that, compared to the single-SoS reconstruction method, the proposed approach produces higher-quality images, achieving the resolution error by near 12 times and a 30 % increase in contrast. Furthermore, the method enables dual-SoS dynamic real-time PAT reconstruction at 10 fps, which is 187.22 % faster than existing dual-SoS reconstruction methods, highlighting its feasibility for dynamic PAT imaging of heterogeneous tissues.
{"title":"Signal-domain speed-of-sound correction for ring-array-based photoacoustic tomography","authors":"Daohuai Jiang , Hengrong Lan , Shangqing Tong , Xianzeng Zhang , Fei Gao","doi":"10.1016/j.pacs.2025.100735","DOIUrl":"10.1016/j.pacs.2025.100735","url":null,"abstract":"<div><div>Photoacoustic imaging combines the advantages of optical and acoustic imaging, making it a promising tool in biomedical imaging. Photoacoustic tomography (PAT) reconstructs images by solving the inverse problem from detected photoacoustic waves to initial pressure map. The heterogeneous speed of sound (SoS) distribution in biological tissue significantly affects image quality, as uncertain SoS variations can cause image distortions. Previously reported dual-speed-of-sound (dual-SoS) imaging methods effectively address these distortions by accounting for the SoS differences between tissues and the coupling medium. However, these methods require recalculating the distribution parameters of the SoS for each frame during dynamic imaging, which is highly time-consuming and poses a significant challenge for achieving real-time dynamic dual-SoS PAT imaging. To address this issue, we propose a signal-domain dual-SoS correction method for PAT image reconstruction. In this method, two distinct SoS regions are differentiated by recognizing the photoacoustic signal features of the imaging target's contours. The signals are then corrected based on the respective SoS values, enabling signal-domain-based dual-SoS dynamic real-time PAT imaging. The proposed method was validated through numerical simulations and in-vivo experiments of human finger. The results show that, compared to the single-SoS reconstruction method, the proposed approach produces higher-quality images, achieving the resolution error by near 12 times and a 30 % increase in contrast. Furthermore, the method enables dual-SoS dynamic real-time PAT reconstruction at 10 fps, which is 187.22 % faster than existing dual-SoS reconstruction methods, highlighting its feasibility for dynamic PAT imaging of heterogeneous tissues.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"44 ","pages":"Article 100735"},"PeriodicalIF":7.1,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144154505","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 : 2025-05-17DOI: 10.1016/j.pacs.2025.100730
Yizhou Tan , Min Zhang , Zhifeng Wu , Jingqin Chen , Yaguang Ren , Chengbo Liu , Ying Gu
Current consensus suggests a simultaneous occurrence of hypoxia and inflammation. For the first time, we observed a hyperoxia state during the initiation stage of gouty arthritis (GA) via optical-resolution photoacoustic microscopy. GA as a paradigm of acute sterile inflammation, has been regarded as a single process. However, our experimental results demonstrated that the onset-resolution inflammation process composed of two sub-processes with different features. In the initial sub-process, inflammation and resolution events appear in hyperoxia state (1st-5th days). In the subsequent sub-process, post-resolution events appear in hypoxia state (6th-15th days), which is related with the second wave of immune response. Furthermore, we demonstrated that the inflammatory cytokines together with hyperoxia levels in initial sub-process can be downregulated by photobiomodulation, resulting in the hypoxia levels in subsequent sub-process were inhibited. Our results unveiled the detailed progress of GA and provided potential non-invasive monitoring and treatment strategies.
{"title":"Local oxygen concentration reversal from hyperoxia to hypoxia monitored by optical-resolution photoacoustic microscopy in inflammation-resolution process","authors":"Yizhou Tan , Min Zhang , Zhifeng Wu , Jingqin Chen , Yaguang Ren , Chengbo Liu , Ying Gu","doi":"10.1016/j.pacs.2025.100730","DOIUrl":"10.1016/j.pacs.2025.100730","url":null,"abstract":"<div><div>Current consensus suggests a simultaneous occurrence of hypoxia and inflammation. For the first time, we observed a hyperoxia state during the initiation stage of gouty arthritis (GA) via optical-resolution photoacoustic microscopy. GA as a paradigm of acute sterile inflammation, has been regarded as a single process. However, our experimental results demonstrated that the onset-resolution inflammation process composed of two sub-processes with different features. In the initial sub-process, inflammation and resolution events appear in hyperoxia state (1st-5th days). In the subsequent sub-process, post-resolution events appear in hypoxia state (6th-15th days), which is related with the second wave of immune response. Furthermore, we demonstrated that the inflammatory cytokines together with hyperoxia levels in initial sub-process can be downregulated by photobiomodulation, resulting in the hypoxia levels in subsequent sub-process were inhibited. Our results unveiled the detailed progress of GA and provided potential non-invasive monitoring and treatment strategies.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"44 ","pages":"Article 100730"},"PeriodicalIF":7.1,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144168794","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 : 2025-05-16DOI: 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":"2025-05-16","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}
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":"2025-05-10","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 : 2025-05-01DOI: 10.1016/j.pacs.2025.100729
Fangzhou Lin , Shang Gao , Yichuan Tang , Xihan Ma , Ryo Murakami , Ziming Zhang , John D. Obayemi , Winston O. Soboyejo , Haichong K. Zhang
Spectroscopic photoacoustic (sPA) imaging uses multiple wavelengths to differentiate and quantify chromophores based on their unique optical absorption spectra. This technique has been widely applied in areas such as vascular mapping, tumor detection, and therapeutic monitoring. However, PA imaging is highly susceptible to noise, leading to a low signal-to-noise ratio (SNR) and compromised image quality. Furthermore, low SNR in spectral data adversely affects spectral unmixing outcomes, hindering accurate quantitative PA imaging. Traditional denoising techniques like frame averaging, though effective in improving SNR, can be impractical for dynamic imaging scenarios due to reduced frame rates. Advanced methods, including learning-based approaches and analytical algorithms, have demonstrated promise but often require extensive training data and parameter tuning. Moreover, spectral information preservation is unclear, limiting their adaptability for clinical usage. Additionally, training data is not always accessible for learning-based methods. In this work, we propose a Spectroscopic Photoacoustic Denoising (SPADE) framework using hybrid analytical and data-free learning method. This framework integrates a data-free learning-based method with an efficient BM3D-based analytical approach while preserving spectral integrity, providing noise reduction, and ensuring that functional information is maintained. The SPADE framework was validated through simulation, phantom, in vivo, and ex vivo studies. These studies demonstrated that SPADE improved image SNR by over 15 in high noise cases and preserved spectral information (R > 0.8), outperforming conventional methods, especially in low SNR conditions. SPADE presents a promising solution for preserving the accuracy of quantitative PA imaging in clinical applications where noise reduction and spectral preservation are critical.
{"title":"Spectroscopic photoacoustic denoising framework using hybrid analytical and data-free learning method","authors":"Fangzhou Lin , Shang Gao , Yichuan Tang , Xihan Ma , Ryo Murakami , Ziming Zhang , John D. Obayemi , Winston O. Soboyejo , Haichong K. Zhang","doi":"10.1016/j.pacs.2025.100729","DOIUrl":"10.1016/j.pacs.2025.100729","url":null,"abstract":"<div><div>Spectroscopic photoacoustic (sPA) imaging uses multiple wavelengths to differentiate and quantify chromophores based on their unique optical absorption spectra. This technique has been widely applied in areas such as vascular mapping, tumor detection, and therapeutic monitoring. However, PA imaging is highly susceptible to noise, leading to a low signal-to-noise ratio (SNR) and compromised image quality. Furthermore, low SNR in spectral data adversely affects spectral unmixing outcomes, hindering accurate quantitative PA imaging. Traditional denoising techniques like frame averaging, though effective in improving SNR, can be impractical for dynamic imaging scenarios due to reduced frame rates. Advanced methods, including learning-based approaches and analytical algorithms, have demonstrated promise but often require extensive training data and parameter tuning. Moreover, spectral information preservation is unclear, limiting their adaptability for clinical usage. Additionally, training data is not always accessible for learning-based methods. In this work, we propose a <u>S</u>pectroscopic <u>P</u>hoto<u>a</u>coustic <u>De</u>noising (SPADE) framework using hybrid analytical and data-free learning method. This framework integrates a data-free learning-based method with an efficient BM3D-based analytical approach while preserving spectral integrity, providing noise reduction, and ensuring that functional information is maintained. The SPADE framework was validated through simulation, phantom, in vivo, and ex vivo studies. These studies demonstrated that SPADE improved image SNR by over 15 <span><math><mi>dB</mi></math></span> in high noise cases and preserved spectral information (R > 0.8), outperforming conventional methods, especially in low SNR conditions. SPADE presents a promising solution for preserving the accuracy of quantitative PA imaging in clinical applications where noise reduction and spectral preservation are critical.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"44 ","pages":"Article 100729"},"PeriodicalIF":7.1,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143898778","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 : 2025-04-26DOI: 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":"2025-04-26","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 : 2025-04-18DOI: 10.1016/j.pacs.2025.100725
Wei Li , Xiaoxuan Zhong , Jie Huang , Xue Bai , Yizhi Liang , Linghao Cheng , Long Jin , Hao-Cheng Tang , Yinyan Lai , Bai-Ou Guan
Photoacoustic microscopy (PAM) faces a fundamental trade-off between detection sensitivity and field of view (FOV). While optical ultrasound sensors offer high-sensitivity unfocused detection, implementing multichannel detection remains challenging. Here, we present a wavelength-time-division multiplexed (WTDM) fiber-optic sensor array that assigns distinct wavelengths to individual sensors and employs varying-length delay fibers for temporal separation, enabling efficient multichannel detection through a single photodetector. Using a 4-element sensor array, we achieved an expanded FOV of 5 × 8 mm² while maintaining high temporal resolution (160 kHz A-line rate, 0.25 Hz frame rate) and microscopic spatial resolution (10.7 μm). The system's capabilities were validated through comparative monitoring of cerebral and intestinal hemodynamics in mice during hypercapnia challenge, revealing distinct temporal patterns with notably delayed recovery in cerebral vascular response compared to intestinal vasculature. This WTDM approach establishes a promising platform for large-field, high-speed photoacoustic imaging in biomedical applications.
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