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}
Pub Date : 2025-06-28DOI: 10.1016/j.pacs.2025.100747
Jonas J.M. Riksen , Antonius W. Schurink , Kalloor Joseph Francis , Cornelis Verhoef , Dirk J. Grünhagen , Gijs van Soest
Sentinel lymph node (SLN) biopsy is an essential procedure for accurate disease staging and treatment planning in patients with melanoma and breast cancer. Conventional preoperative imaging primarily utilizes lymphoscintigraphy with technetium-99m (Tc-99m), which presents several limitations, including radiation exposure, logistical challenges, and potential delays in surgical workflow. Photoacoustic imaging (PAI) has emerged as a promising alternative, leveraging optical contrast provided by indocyanine green (ICG). A feasibility study was conducted at Erasmus MC, University Medical Center Rotterdam, to assess the potential of dual-wavelength PAI for SLN mapping. PAI was employed to perform spectroscopic measurements in healthy volunteers, supporting the development of an optimal excitation protocol. Subsequently, in the patient phase, SLN mapping was performed using PAI with ICG, and the results were compared to the standard-of-care method utilizing Tc-99m. The excitation wavelengths of 800 nm and 860 nm were selected for ratiometric imaging to effectively visualize ICG while suppressing clutter from hemoglobin and melanin. Among the eleven evaluated sentinel nodes, seven were successfully identified using PAI. The maximum SLN detection depth achieved with PAI was 22 mm. This study illustrates the feasibility of ICG-enhanced dual-wavelength PAI for preoperative SLN mapping in patients with melanoma and breast cancer, as an alternative to lymphoscintigraphy. Analysis of false-negative detections suggests improvements to PAI and optimal patient selection. The proposed ratiometric PAI methodology, compared to multiwavelength spectroscopic imaging, enables faster imaging speeds and facilitates the transition to cheaper light sources.
{"title":"Dual-wavelength photoacoustic imaging of sentinel lymph nodes in patients with melanoma and breast cancer","authors":"Jonas J.M. Riksen , Antonius W. Schurink , Kalloor Joseph Francis , Cornelis Verhoef , Dirk J. Grünhagen , Gijs van Soest","doi":"10.1016/j.pacs.2025.100747","DOIUrl":"10.1016/j.pacs.2025.100747","url":null,"abstract":"<div><div>Sentinel lymph node (SLN) biopsy is an essential procedure for accurate disease staging and treatment planning in patients with melanoma and breast cancer. Conventional preoperative imaging primarily utilizes lymphoscintigraphy with technetium-99m (Tc-99m), which presents several limitations, including radiation exposure, logistical challenges, and potential delays in surgical workflow. Photoacoustic imaging (PAI) has emerged as a promising alternative, leveraging optical contrast provided by indocyanine green (ICG). A feasibility study was conducted at Erasmus MC, University Medical Center Rotterdam, to assess the potential of dual-wavelength PAI for SLN mapping. PAI was employed to perform spectroscopic measurements in healthy volunteers, supporting the development of an optimal excitation protocol. Subsequently, in the patient phase, SLN mapping was performed using PAI with ICG, and the results were compared to the standard-of-care method utilizing Tc-99m. The excitation wavelengths of 800 nm and 860 nm were selected for ratiometric imaging to effectively visualize ICG while suppressing clutter from hemoglobin and melanin. Among the eleven evaluated sentinel nodes, seven were successfully identified using PAI. The maximum SLN detection depth achieved with PAI was 22 mm. This study illustrates the feasibility of ICG-enhanced dual-wavelength PAI for preoperative SLN mapping in patients with melanoma and breast cancer, as an alternative to lymphoscintigraphy. Analysis of false-negative detections suggests improvements to PAI and optimal patient selection. The proposed ratiometric PAI methodology, compared to multiwavelength spectroscopic imaging, enables faster imaging speeds and facilitates the transition to cheaper light sources.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"45 ","pages":"Article 100747"},"PeriodicalIF":7.1,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144518988","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 : 2025-06-19DOI: 10.1016/j.pacs.2025.100743
Aboma Merdasa , Alice Fracchia , Magne Stridh , Jenny Hult , Emil Andersson , Patrik Edén , Victor Olariu , Malin Malmsjö
The incidence of melanoma is rising and will require more efficient diagnostic procedures to meet a growing demand. Excisional biopsy and histopathology is still the standard, which often requires multiple surgical incisions with increasing margins due inaccurate visual assessment of where the melanoma borders to healthy tissue. This challenge stems, in part, from the inability to reliably delineate the melanoma without visually inspecting chemically stained histopathological cross-sections. Spectroscopic imaging have shown promise to non-invasively characterize the molecular composition of tissue and thereby distinguish melanoma from healthy tissue based on spectral features. In this work we describe a computational framework applied to multispectral photoacoustic (PA) imaging data of melanoma in humans and demonstrate how the borders of the tumor can be automatically determined without human input. The framework combines K-means clustering, for an unbiased selection of training data, a one-dimensional convolutional neural network applied to PA spectra for classifying pixels as either healthy or diseased, and an active contour algorithm to finally delineate the melanoma in 3D. The work stands to impact clinical practice as it can provide both pre-surgical and perioperative guidance to ensure complete tumor removal with minimal surgical incisions.
{"title":"Advancing non-invasive melanoma diagnostics with deep learning and multispectral photoacoustic imaging","authors":"Aboma Merdasa , Alice Fracchia , Magne Stridh , Jenny Hult , Emil Andersson , Patrik Edén , Victor Olariu , Malin Malmsjö","doi":"10.1016/j.pacs.2025.100743","DOIUrl":"10.1016/j.pacs.2025.100743","url":null,"abstract":"<div><div>The incidence of melanoma is rising and will require more efficient diagnostic procedures to meet a growing demand. Excisional biopsy and histopathology is still the standard, which often requires multiple surgical incisions with increasing margins due inaccurate visual assessment of where the melanoma borders to healthy tissue. This challenge stems, in part, from the inability to reliably delineate the melanoma without visually inspecting chemically stained histopathological cross-sections. Spectroscopic imaging have shown promise to non-invasively characterize the molecular composition of tissue and thereby distinguish melanoma from healthy tissue based on spectral features. In this work we describe a computational framework applied to multispectral photoacoustic (PA) imaging data of melanoma in humans and demonstrate how the borders of the tumor can be automatically determined without human input. The framework combines K-means clustering, for an unbiased selection of training data, a one-dimensional convolutional neural network applied to PA spectra for classifying pixels as either healthy or diseased, and an active contour algorithm to finally delineate the melanoma in 3D. The work stands to impact clinical practice as it can provide both pre-surgical and perioperative guidance to ensure complete tumor removal with minimal surgical incisions.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"45 ","pages":"Article 100743"},"PeriodicalIF":7.1,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563726","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 : 2025-06-16DOI: 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.
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