Pub Date : 2025-07-31DOI: 10.1016/j.pacs.2025.100756
Tim Wittig , Birte Winther , Charlene Reichl , Andrej Schmidt , Dierk Scheinert , Sabine Steiner
Objectives
This proof-of-concept study aimed to assess the feasibility of Multispectral Optoacoustic Tomography (MSOT) in evaluating changes in oxygenated hemoglobin (HbO2) levels in muscles of the lower limb before and after lower extremity revascularization (LER).
Methods
In 26 patients, HbO2 levels were assessed before and after LER, with follow-up assessing symptom control and patency for up to six months.
Results
A significant difference in HbO2 levels was observed between pre- and post-LER in the muscles of the lower limb. In 10 patients, HbO2 levels did not increase following LER, and at the 6-month follow-up, 2 of these patients required target lesion revascularization (TLR) due to restenosis of ≥ 50 % stenosis. In contrast, 16 patients demonstrated increased HbO2 levels post-LER, with no patients requiring TLR at 6-months.
Conclusion
This study demonstrates the potential of MSOT to detect changes in tissue perfusion following LER, highlighting its promise as a novel imaging modality for guiding treatment strategies.
{"title":"Optoacoustic imaging in lower extremity revascularization: A novel technique to assess perioperative muscle perfusion","authors":"Tim Wittig , Birte Winther , Charlene Reichl , Andrej Schmidt , Dierk Scheinert , Sabine Steiner","doi":"10.1016/j.pacs.2025.100756","DOIUrl":"10.1016/j.pacs.2025.100756","url":null,"abstract":"<div><h3>Objectives</h3><div>This proof-of-concept study aimed to assess the feasibility of Multispectral Optoacoustic Tomography (MSOT) in evaluating changes in oxygenated hemoglobin (HbO2) levels in muscles of the lower limb before and after lower extremity revascularization (LER).</div></div><div><h3>Methods</h3><div>In 26 patients, HbO2 levels were assessed before and after LER, with follow-up assessing symptom control and patency for up to six months.</div></div><div><h3>Results</h3><div>A significant difference in HbO2 levels was observed between pre- and post-LER in the muscles of the lower limb. In 10 patients, HbO2 levels did not increase following LER, and at the 6-month follow-up, 2 of these patients required target lesion revascularization (TLR) due to restenosis of ≥ 50 % stenosis. In contrast, 16 patients demonstrated increased HbO2 levels post-LER, with no patients requiring TLR at 6-months.</div></div><div><h3>Conclusion</h3><div>This study demonstrates the potential of MSOT to detect changes in tissue perfusion following LER, highlighting its promise as a novel imaging modality for guiding treatment strategies.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"45 ","pages":"Article 100756"},"PeriodicalIF":6.8,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771516","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 : 2025-07-25DOI: 10.1016/j.pacs.2025.100753
Taeil Yoon , Hakseok Ko , Jeongmyo Im , Euiheon Chung , Wonshik Choi , Byeongha Lee
Photoacoustic tomography (PAT) combines high optical contrast with deep acoustic penetration, making it valuable for biomedical imaging. However, all-optical systems often face challenges in measuring the acoustic wave-induced displacements on rough and dynamic tissues surfaces. We present an all-optical PAT system enabling fast and high-resolution volumetric imaging in vivo. By integrating holographic microscopy with a soft cover layer and coherent averaging, the system detects ultrasound-induced surface displacements over a 10 × 10 mm² area with 0.5 nm sensitivity in 1 s. A novel backpropagation algorithm reconstructs a depth-selective pressure image from two consecutive displacement maps. The coherent summation of these depth-selective pressure images enables the reconstruction of a 3D acoustic pressure image. Using adaptive multilayer backpropagation, we achieve imaging depths of up to 5 mm, with lateral and axial resolutions of 158 µm and 92 µm, respectively, demonstrated through in vivo imaging of mouse vasculature and chicken embryo vessels.
{"title":"All-optical in vivo photoacoustic tomography by adaptive multilayer acoustic backpropagation","authors":"Taeil Yoon , Hakseok Ko , Jeongmyo Im , Euiheon Chung , Wonshik Choi , Byeongha Lee","doi":"10.1016/j.pacs.2025.100753","DOIUrl":"10.1016/j.pacs.2025.100753","url":null,"abstract":"<div><div>Photoacoustic tomography (PAT) combines high optical contrast with deep acoustic penetration, making it valuable for biomedical imaging. However, all-optical systems often face challenges in measuring the acoustic wave-induced displacements on rough and dynamic tissues surfaces. We present an all-optical PAT system enabling fast and high-resolution volumetric imaging <em>in vivo</em>. By integrating holographic microscopy with a soft cover layer and coherent averaging, the system detects ultrasound-induced surface displacements over a 10 × 10 mm² area with 0.5 nm sensitivity in 1 s. A novel backpropagation algorithm reconstructs a depth-selective pressure image from two consecutive displacement maps. The coherent summation of these depth-selective pressure images enables the reconstruction of a 3D acoustic pressure image. Using adaptive multilayer backpropagation, we achieve imaging depths of up to 5 mm, with lateral and axial resolutions of 158 µm and 92 µm, respectively, demonstrated through <em>in vivo</em> imaging of mouse vasculature and chicken embryo vessels.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"45 ","pages":"Article 100753"},"PeriodicalIF":6.8,"publicationDate":"2025-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144771514","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 : 2025-07-23DOI: 10.1016/j.pacs.2025.100752
Ruobin Zhuang , Jianfeng He , Haoyang Lin , Huijian Luo , Leqing Lin , Lihao Wang , Bin Liu , Wenguo Zhu , Yongchun Zhong , Jianhui Yu , Markus Sigrist , Huadan Zheng
Accurate and rapid detection of hydrogen and hydrocarbons is critical for safety and efficiency in modern energy, industrial, and environmental systems. However, selective and simultaneous quantification of these species remains a significant technical challenge. Here, we introduce conductance–photoacoustic spectroscopy (ConPAS), an integrated sensing approach that combines conductance-based resonance modulation with quartz-enhanced photoacoustic spectroscopy in a single device. By bridging a quartz tuning fork (QTF) with a catalytic platinum microwire, ConPAS enables concurrent extraction of hydrogen and hydrocarbon concentrations from a unified electrical signal: hydrogen is quantified by frequency analysis, while hydrocarbon content is determined by amplitude analysis simultaneously. Experiments demonstrate minimum detection limits of 0.69 % for hydrogen, 40.26 ppm for propane, and 133.7 ppm for methane, with millisecond response time and excellent linearity (R² > 0.99). The modular architecture allows flexible adaptation to other analytes via material substitution, offering a scalable and versatile solution for simultaneous, multi-component gas sensing. This work establishes ConPAS as a powerful, calibration-compatible platform for integrated gas analysis in hydrogen-enriched environments, with broad implications for safety monitoring, process control, and advanced energy applications.
{"title":"Conductance-photoacoustic spectroscopy for fast and concurrent sensing of hydrogen and hydrocarbons","authors":"Ruobin Zhuang , Jianfeng He , Haoyang Lin , Huijian Luo , Leqing Lin , Lihao Wang , Bin Liu , Wenguo Zhu , Yongchun Zhong , Jianhui Yu , Markus Sigrist , Huadan Zheng","doi":"10.1016/j.pacs.2025.100752","DOIUrl":"10.1016/j.pacs.2025.100752","url":null,"abstract":"<div><div>Accurate and rapid detection of hydrogen and hydrocarbons is critical for safety and efficiency in modern energy, industrial, and environmental systems. However, selective and simultaneous quantification of these species remains a significant technical challenge. Here, we introduce conductance–photoacoustic spectroscopy (ConPAS), an integrated sensing approach that combines conductance-based resonance modulation with quartz-enhanced photoacoustic spectroscopy in a single device. By bridging a quartz tuning fork (QTF) with a catalytic platinum microwire, ConPAS enables concurrent extraction of hydrogen and hydrocarbon concentrations from a unified electrical signal: hydrogen is quantified by frequency analysis, while hydrocarbon content is determined by amplitude analysis simultaneously. Experiments demonstrate minimum detection limits of 0.69 % for hydrogen, 40.26 ppm for propane, and 133.7 ppm for methane, with millisecond response time and excellent linearity (R² > 0.99). The modular architecture allows flexible adaptation to other analytes via material substitution, offering a scalable and versatile solution for simultaneous, multi-component gas sensing. This work establishes ConPAS as a powerful, calibration-compatible platform for integrated gas analysis in hydrogen-enriched environments, with broad implications for safety monitoring, process control, and advanced energy applications.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"45 ","pages":"Article 100752"},"PeriodicalIF":6.8,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144738099","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 : 2025-07-20DOI: 10.1016/j.pacs.2025.100751
Sunghun Park , Woongki Hong , Hyeongyu Park , Eunji Lee , Sangwoo Nam , Jinhwan Jung , Jung Ho Hyun , Jaesok Yu , Hongki Kang , Jin Ho Chang
For high-performance combined photoacoustic (PA) and Ultrasound (US) microscopy, precise coaxial alignment of the US and laser beams is essential. This can be realized using broadband transparent ultrasound transducers (TUTs). However, the current dual-mode imaging systems encounter significant challenges in simultaneous PA and US data acquisition due to sequential transmission of light and ultrasound and mechanical movement of dual-mode probes, leading to longer acquisition times and potential registration inaccuracies. To overcome these limitations, we propose a recently developed high-frequency broadband TUT with an ultrathin (< 10 nm) gold electrode, achieving a center frequency of 65.6 MHz and a –6 dB bandwidth of 71.6 %. The ultrathin gold electrode facilitates laser-induced ultrasound (LUS), enabling simultaneous acquisition of PA and US images. In vivo experiments demonstrate that LUS imaging can effectively replace conventional US imaging, offering highly efficient dual-mode PA/US imaging with minimized registration errors.
{"title":"High-frequency (> 65 MHz) broadband transparent transducer with ultrathin gold electrode for dual-mode photoacoustic and laser-induced ultrasound microscopy","authors":"Sunghun Park , Woongki Hong , Hyeongyu Park , Eunji Lee , Sangwoo Nam , Jinhwan Jung , Jung Ho Hyun , Jaesok Yu , Hongki Kang , Jin Ho Chang","doi":"10.1016/j.pacs.2025.100751","DOIUrl":"10.1016/j.pacs.2025.100751","url":null,"abstract":"<div><div>For high-performance combined photoacoustic (PA) and Ultrasound (US) microscopy, precise coaxial alignment of the US and laser beams is essential. This can be realized using broadband transparent ultrasound transducers (TUTs). However, the current dual-mode imaging systems encounter significant challenges in simultaneous PA and US data acquisition due to sequential transmission of light and ultrasound and mechanical movement of dual-mode probes, leading to longer acquisition times and potential registration inaccuracies. To overcome these limitations, we propose a recently developed high-frequency broadband TUT with an ultrathin (< 10 nm) gold electrode, achieving a center frequency of 65.6 MHz and a –6 dB bandwidth of 71.6 %. The ultrathin gold electrode facilitates laser-induced ultrasound (LUS), enabling simultaneous acquisition of PA and US images. In vivo experiments demonstrate that LUS imaging can effectively replace conventional US imaging, offering highly efficient dual-mode PA/US imaging with minimized registration errors.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"45 ","pages":"Article 100751"},"PeriodicalIF":7.1,"publicationDate":"2025-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144686770","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 : 2025-07-11DOI: 10.1016/j.pacs.2025.100748
Miika Suhonen , Felix Lucka , Aki Pulkkinen , Simon Arridge , Ben Cox , Tanja Tarvainen
Image reconstruction in photoacoustic tomography relies on an accurate knowledge of the speed of sound in the target. However, the speed of sound distribution is not generally known, which may result in artefacts in the reconstructed distribution of initial pressure. Therefore, reconstructing the speed of sound simultaneously with the initial pressure would be valuable for accurate imaging in photoacoustic tomography. Furthermore, the speed of sound distribution could provide additional valuable information about the imaged target. In this work, simultaneous reconstruction of initial pressure and speed of sound in photoacoustic tomography is studied. This inverse problem is known to be highly ill-posed. To overcome this, we study an approach where the ill-posedness is alleviated by utilising multiple photoacoustic data sets that are generated by different initial pressure distributions within the same imaged target. Then, these initial pressure distributions are reconstructed simultaneously with the speed of sound distribution. A methodology for solving this minimisation problem is formulated using a gradient-based iterative approach equipped with bound constraints and a multigrid approach. The methodology was evaluated with numerical simulations. Different approaches for generating multiple initial pressure distributions and their effect on the solution of the image reconstruction problem were studied. The results show that initial pressure and speed of sound can be simultaneously reconstructed from photoacoustic data. Furthermore, utilising multiple initial pressure distributions improves the reconstructions such that the locations of initial pressure and speed of sound inhomogeneities can be better distinguished and image artifacts are reduced.
{"title":"Reconstructing multiple initial pressure and speed of sound distributions simultaneously in photoacoustic tomography","authors":"Miika Suhonen , Felix Lucka , Aki Pulkkinen , Simon Arridge , Ben Cox , Tanja Tarvainen","doi":"10.1016/j.pacs.2025.100748","DOIUrl":"10.1016/j.pacs.2025.100748","url":null,"abstract":"<div><div>Image reconstruction in photoacoustic tomography relies on an accurate knowledge of the speed of sound in the target. However, the speed of sound distribution is not generally known, which may result in artefacts in the reconstructed distribution of initial pressure. Therefore, reconstructing the speed of sound simultaneously with the initial pressure would be valuable for accurate imaging in photoacoustic tomography. Furthermore, the speed of sound distribution could provide additional valuable information about the imaged target. In this work, simultaneous reconstruction of initial pressure and speed of sound in photoacoustic tomography is studied. This inverse problem is known to be highly ill-posed. To overcome this, we study an approach where the ill-posedness is alleviated by utilising multiple photoacoustic data sets that are generated by different initial pressure distributions within the same imaged target. Then, these initial pressure distributions are reconstructed simultaneously with the speed of sound distribution. A methodology for solving this minimisation problem is formulated using a gradient-based iterative approach equipped with bound constraints and a multigrid approach. The methodology was evaluated with numerical simulations. Different approaches for generating multiple initial pressure distributions and their effect on the solution of the image reconstruction problem were studied. The results show that initial pressure and speed of sound can be simultaneously reconstructed from photoacoustic data. Furthermore, utilising multiple initial pressure distributions improves the reconstructions such that the locations of initial pressure and speed of sound inhomogeneities can be better distinguished and image artifacts are reduced.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"45 ","pages":"Article 100748"},"PeriodicalIF":7.1,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144655096","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 : 2025-07-08DOI: 10.1016/j.pacs.2025.100740
Olivier J.M. Stam , Kalloor Joseph Francis , Navchetan Awasthi
For clinical translation of photoacoustic imaging cost-effective systems development is necessary. One approach is the use of fewer transducer elements and acquisition channels combined with sparse sampling. However, this approach introduces reconstruction artifacts that degrade image quality. While deep learning models such as U-net have shown promise in reconstructing images from limited data, they typically require retraining for each new system configuration, a process that demands more data and increased computational resources. In this work, we introduce PA OmniNet, a modified U-net model designed to generalize across different system configurations without the need for retraining. Instead of retraining, PA OmniNet adapts to a new system using only a small set of example images (between 4 and 32), known as a context set. This context set conditions the model to effectively remove artifacts from new input images in various sparse sampling photoacoustic imaging applications. We evaluated PA OmniNet against a standard U-net using multiple datasets, including in vivo data from mouse and human subjects, synthetic data, and images captured at different wavelengths. PA OmniNet consistently outperformed the traditional U-net in generalization tasks, achieving average improvements of 8.3% in the Structural Similarity Index, a 11.6% reduction in Root Mean Square Error, and a 1.55 dB increase in Peak Signal-to-Noise Ratio. In 66% of our test cases, the generalized PA OmniNet even outperformed U-net models trained specifically on the new dataset. Code is available at https://github.com/olivierstam4/PA_OmniNet.
{"title":"PA OmniNet: A retraining-free, generalizable deep learning framework for robust photoacoustic image reconstruction","authors":"Olivier J.M. Stam , Kalloor Joseph Francis , Navchetan Awasthi","doi":"10.1016/j.pacs.2025.100740","DOIUrl":"10.1016/j.pacs.2025.100740","url":null,"abstract":"<div><div>For clinical translation of photoacoustic imaging cost-effective systems development is necessary. One approach is the use of fewer transducer elements and acquisition channels combined with sparse sampling. However, this approach introduces reconstruction artifacts that degrade image quality. While deep learning models such as U-net have shown promise in reconstructing images from limited data, they typically require retraining for each new system configuration, a process that demands more data and increased computational resources. In this work, we introduce PA OmniNet, a modified U-net model designed to generalize across different system configurations without the need for retraining. Instead of retraining, PA OmniNet adapts to a new system using only a small set of example images (between 4 and 32), known as a context set. This context set conditions the model to effectively remove artifacts from new input images in various sparse sampling photoacoustic imaging applications. We evaluated PA OmniNet against a standard U-net using multiple datasets, including in vivo data from mouse and human subjects, synthetic data, and images captured at different wavelengths. PA OmniNet consistently outperformed the traditional U-net in generalization tasks, achieving average improvements of 8.3% in the Structural Similarity Index, a 11.6% reduction in Root Mean Square Error, and a 1.55 dB increase in Peak Signal-to-Noise Ratio. In 66% of our test cases, the generalized PA OmniNet even outperformed U-net models trained specifically on the new dataset. Code is available at <span><span>https://github.com/olivierstam4/PA_OmniNet</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"45 ","pages":"Article 100740"},"PeriodicalIF":7.1,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144633939","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 : 2025-07-07DOI: 10.1016/j.pacs.2025.100749
Simon C.K. Chan , Bingxin Huang , Hannah H. Kim , Victor T.C. Tsang , Terence T.W. Wong
Photoacoustic tomography (PAT) combines the high contrast of optical imaging with deep tissue penetration via ultrasound detection. However, hardware limitations often cause sparse sampling during image acquisition, resulting in disruptive streak artifacts that many current deep-learning methods fail to remove effectively. In this paper, we introduce Residual Condition Optimal Transport Mamba (RCMamba)—a novel framework that enhances residual optimal transport by integrating wavelet-based analysis with a hybrid multi-scale state space model backbone, specifically designed for sparse PAT image restoration. Our approach makes two primary contributions. First, we propose a wavelet residual-enhanced transport plan that leverages multi-resolution analysis and a novel wavelet coherence penalty to accurately capture the complex, scale-dependent sparsity patterns characteristic of sparse acquisitions. Second, we develop a hybrid multi-scale mamba architecture that uniquely combines window-based and global state space scanning to preserve both fine anatomical details and long-range structural information. Extensive experiments on vessel phantoms and in vivo mouse models across various sampling densities (16, 32, and 64 projections) demonstrate that RCMamba consistently outperforms state-of-the-art techniques in terms of artifact suppression and structural fidelity. RCMamba holds great promise in advancing the clinical potential of sparse-sampling PAT systems for diagnostic imaging and interventional procedures.
{"title":"Wavelet-enhanced residual optimal transport for Mamba-based image restoration in photoacoustic tomography","authors":"Simon C.K. Chan , Bingxin Huang , Hannah H. Kim , Victor T.C. Tsang , Terence T.W. Wong","doi":"10.1016/j.pacs.2025.100749","DOIUrl":"10.1016/j.pacs.2025.100749","url":null,"abstract":"<div><div>Photoacoustic tomography (PAT) combines the high contrast of optical imaging with deep tissue penetration via ultrasound detection. However, hardware limitations often cause sparse sampling during image acquisition, resulting in disruptive streak artifacts that many current deep-learning methods fail to remove effectively. In this paper, we introduce Residual Condition Optimal Transport Mamba (RCMamba)—a novel framework that enhances residual optimal transport by integrating wavelet-based analysis with a hybrid multi-scale state space model backbone, specifically designed for sparse PAT image restoration. Our approach makes two primary contributions. First, we propose a wavelet residual-enhanced transport plan that leverages multi-resolution analysis and a novel wavelet coherence penalty to accurately capture the complex, scale-dependent sparsity patterns characteristic of sparse acquisitions. Second, we develop a hybrid multi-scale mamba architecture that uniquely combines window-based and global state space scanning to preserve both fine anatomical details and long-range structural information. Extensive experiments on vessel phantoms and <em>in vivo</em> mouse models across various sampling densities (16, 32, and 64 projections) demonstrate that RCMamba consistently outperforms state-of-the-art techniques in terms of artifact suppression and structural fidelity. RCMamba holds great promise in advancing the clinical potential of sparse-sampling PAT systems for diagnostic imaging and interventional procedures.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"45 ","pages":"Article 100749"},"PeriodicalIF":7.1,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596872","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 : 2025-07-05DOI: 10.1016/j.pacs.2025.100745
Max T. Rietberg , Janek Gröhl , Thomas R. Else , Sarah E. Bohndiek , Srirang Manohar , Benjamin T. Cox
Photoacoustic imaging (PAI) is rapidly moving from the laboratory to the clinic, increasing the need to understand confounders which might adversely affect patient care. Over the past five years, landmark studies have shown the clinical utility of PAI, leading to regulatory approval of several devices. In this article, we describe the various causes of artifacts in PAI, providing schematic overviews and practical examples, simulated as well as experimental. This work serves two purposes: (1) educating clinical users to identify artifacts, understand their causes, and assess their impact, and (2) providing a reference of the limitations of current systems for those working to improve them. We explain how two aspects of PAI systems lead to artifacts: their inability to measure complete data sets, and embedded assumptions during reconstruction. We describe the physics underlying PAI, and propose a classification of the artifacts. The paper concludes by discussing possible advanced mitigation strategies.
{"title":"Artifacts in photoacoustic imaging: Origins and mitigations","authors":"Max T. Rietberg , Janek Gröhl , Thomas R. Else , Sarah E. Bohndiek , Srirang Manohar , Benjamin T. Cox","doi":"10.1016/j.pacs.2025.100745","DOIUrl":"10.1016/j.pacs.2025.100745","url":null,"abstract":"<div><div>Photoacoustic imaging (PAI) is rapidly moving from the laboratory to the clinic, increasing the need to understand confounders which might adversely affect patient care. Over the past five years, landmark studies have shown the clinical utility of PAI, leading to regulatory approval of several devices. In this article, we describe the various causes of artifacts in PAI, providing schematic overviews and practical examples, simulated as well as experimental. This work serves two purposes: (1) educating clinical users to identify artifacts, understand their causes, and assess their impact, and (2) providing a reference of the limitations of current systems for those working to improve them. We explain how two aspects of PAI systems lead to artifacts: their inability to measure complete data sets, and embedded assumptions during reconstruction. We describe the physics underlying PAI, and propose a classification of the artifacts. The paper concludes by discussing possible advanced mitigation strategies.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"45 ","pages":"Article 100745"},"PeriodicalIF":7.1,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672574","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 : 2025-07-05DOI: 10.1016/j.pacs.2025.100750
Xiao Liang , Yuanlong Zhao , Linyang Li , Hongdian Sun , Wei Qin , Tingting Li , Heng Guo , Weizhi Qi , Lei Xi
Optical endoscopy has been extensively used in clinical screening and diagnosis of internal diseased organs. Photoacoustic endoscopy, one of the rapidest evolving optical endoscopies, combines rich optical contrasts with high spatial acoustic resolving capability at a considerable penetration depth. However, implementing high-speed, large field-of-view photoacoustic endoscopy in arbitrarily shaped biological tracts and cavities remains a challenge. Here, we develop a miniaturized, multi-view photoacoustic endoscope (Multi-PAE) that integrates a micro-optical scanner and a folded optical path within a capsule-sized probe. The probe features multiple interchangeable imaging interfaces in different orientations to image diverse tracts and cavities. We propose a compound double spiral resonant scanning (CDSRS) mechanism to enable the optical scanner to perform stable and uniform resonant scanning over a large field-of-view. We demonstrate the multi-scenario functional imaging applicability of Multi-PAE in rat rectums, rabbit cervices, and entire human oral cavities.
{"title":"Multi-scenario photoacoustic endoscopy for in vivo functional imaging","authors":"Xiao Liang , Yuanlong Zhao , Linyang Li , Hongdian Sun , Wei Qin , Tingting Li , Heng Guo , Weizhi Qi , Lei Xi","doi":"10.1016/j.pacs.2025.100750","DOIUrl":"10.1016/j.pacs.2025.100750","url":null,"abstract":"<div><div>Optical endoscopy has been extensively used in clinical screening and diagnosis of internal diseased organs. Photoacoustic endoscopy, one of the rapidest evolving optical endoscopies, combines rich optical contrasts with high spatial acoustic resolving capability at a considerable penetration depth. However, implementing high-speed, large field-of-view photoacoustic endoscopy in arbitrarily shaped biological tracts and cavities remains a challenge. Here, we develop a miniaturized, multi-view photoacoustic endoscope (Multi-PAE) that integrates a micro-optical scanner and a folded optical path within a capsule-sized probe. The probe features multiple interchangeable imaging interfaces in different orientations to image diverse tracts and cavities. We propose a compound double spiral resonant scanning (CDSRS) mechanism to enable the optical scanner to perform stable and uniform resonant scanning over a large field-of-view. We demonstrate the multi-scenario functional imaging applicability of Multi-PAE in rat rectums, rabbit cervices, and entire human oral cavities.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"45 ","pages":"Article 100750"},"PeriodicalIF":7.1,"publicationDate":"2025-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144579784","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 : 2025-07-04DOI: 10.1016/j.pacs.2025.100736
Zeqi Wang , Wei Tao , Zhuang Zhang , Hui Zhao
The difficulty of obtaining absorption coefficient annotations hinders the practical application of deep learning in quantitative photoacoustic tomography. While training on synthetic data is easy to implement, the synthetic-to-real domain gap poses a significant challenge to model generalization. To address this, we propose a Decoder-enhanced unsupervised Domain Adaptation (DDA) framework to enable knowledge transfer from synthetic data to an unlabeled target domain. Experimental results show that DDA significantly improves estimation performance on target images and surpasses competing methods in quantitative evaluation and visual comparison. Additionally, we investigate the effect of cross-domain label distribution similarity on domain adaptation and recommend an effective approach for data synthesis. To mitigate the effect of absorption property mismatch, we propose fine-tuning the affine parameters of normalization layers, which significantly improves estimation accuracy using labeled multi-wavelength photoacoustic images from as few as two target samples.
{"title":"Towards bridging the synthetic-to-real gap in quantitative photoacoustic tomography via unsupervised domain adaptation","authors":"Zeqi Wang , Wei Tao , Zhuang Zhang , Hui Zhao","doi":"10.1016/j.pacs.2025.100736","DOIUrl":"10.1016/j.pacs.2025.100736","url":null,"abstract":"<div><div>The difficulty of obtaining absorption coefficient annotations hinders the practical application of deep learning in quantitative photoacoustic tomography. While training on synthetic data is easy to implement, the synthetic-to-real domain gap poses a significant challenge to model generalization. To address this, we propose a Decoder-enhanced unsupervised Domain Adaptation (DDA) framework to enable knowledge transfer from synthetic data to an unlabeled target domain. Experimental results show that DDA significantly improves estimation performance on target images and surpasses competing methods in quantitative evaluation and visual comparison. Additionally, we investigate the effect of cross-domain label distribution similarity on domain adaptation and recommend an effective approach for data synthesis. To mitigate the effect of absorption property mismatch, we propose fine-tuning the affine parameters of normalization layers, which significantly improves estimation accuracy using labeled multi-wavelength photoacoustic images from as few as two target samples.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"45 ","pages":"Article 100736"},"PeriodicalIF":7.1,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144703708","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}
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