Photoacoustics最新文献

{"title":"Signal-domain speed-of-sound correction for ring-array-based photoacoustic tomography","authors":"Daohuai Jiang ,&nbsp;Hengrong Lan ,&nbsp;Shangqing Tong ,&nbsp;Xianzeng Zhang ,&nbsp;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}

{"title":"Local oxygen concentration reversal from hyperoxia to hypoxia monitored by optical-resolution photoacoustic microscopy in inflammation-resolution process","authors":"Yizhou Tan ,&nbsp;Min Zhang ,&nbsp;Zhifeng Wu ,&nbsp;Jingqin Chen ,&nbsp;Yaguang Ren ,&nbsp;Chengbo Liu ,&nbsp;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}

{"title":"Model-informed deep-learning photoacoustic reconstruction for low-element linear array","authors":"Souradip Paul ,&nbsp;S. Alex Lee ,&nbsp;Shensheng Zhao ,&nbsp;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}

{"title":"Scale-equivariant deep model-based optoacoustic image reconstruction","authors":"Christoph Dehner ,&nbsp;Ledia Lilaj ,&nbsp;Vasilis Ntziachristos ,&nbsp;Guillaume Zahnd ,&nbsp;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}

{"title":"Spectroscopic photoacoustic denoising framework using hybrid analytical and data-free learning method","authors":"Fangzhou Lin ,&nbsp;Shang Gao ,&nbsp;Yichuan Tang ,&nbsp;Xihan Ma ,&nbsp;Ryo Murakami ,&nbsp;Ziming Zhang ,&nbsp;John D. Obayemi ,&nbsp;Winston O. Soboyejo ,&nbsp;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 &gt; 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}

{"title":"Iterative optimization algorithm with structural prior for artifacts removal of photoacoustic imaging","authors":"Yu Zhang ,&nbsp;Shuang Li ,&nbsp;Yibing Wang ,&nbsp;Yu Sun ,&nbsp;Tingting Huang ,&nbsp;Wenyi Xiang ,&nbsp;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}

{"title":"Wavelength-time-division multiplexed fiber-optic sensor array for wide-field photoacoustic microscopy","authors":"Wei Li ,&nbsp;Xiaoxuan Zhong ,&nbsp;Jie Huang ,&nbsp;Xue Bai ,&nbsp;Yizhi Liang ,&nbsp;Linghao Cheng ,&nbsp;Long Jin ,&nbsp;Hao-Cheng Tang ,&nbsp;Yinyan Lai ,&nbsp;Bai-Ou Guan","doi":"10.1016/j.pacs.2025.100725","DOIUrl":"10.1016/j.pacs.2025.100725","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"43 ","pages":"Article 100725"},"PeriodicalIF":7.1,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860253","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}

{"title":"Zero-Shot Artifact2Artifact: Self-incentive artifact removal for photoacoustic imaging","authors":"Shuang Li ,&nbsp;Qian Chen ,&nbsp;Chulhong Kim ,&nbsp;Seongwook Choi ,&nbsp;Yibing Wang ,&nbsp;Yu Zhang ,&nbsp;Changhui Li","doi":"10.1016/j.pacs.2025.100723","DOIUrl":"10.1016/j.pacs.2025.100723","url":null,"abstract":"<div><div>Three-dimensional (3D) photoacoustic imaging (PAI) with detector arrays has shown superior imaging capabilities in biomedical applications. However, the quality of 3D PAI is often degraded due to reconstruction artifacts caused by sparse detectors. Existing iterative or deep learning-based methods are either time-consuming or require large training datasets, limiting their practical application. Here, we propose Zero-Shot Artifact2Artifact (ZS-A2A), a zero-shot self-supervised artifact removal method based on a super-lightweight network, which leverages the fact that patterns of artifacts are more sensitive to sensor data loss. By randomly dropping acquired PA data, it spontaneously generates subset data to reconstruct images, which in turn stimulates the network to learn the artifact patterns in reconstruction results, thus enabling zero-shot artifact removal. This approach requires neither training data nor prior knowledge of the artifacts, making it suitable for artifact removal for arbitrary detector array configurations. We validated ZS-A2A in both simulation study and <span><math><mrow><mi>i</mi><mi>n</mi><mspace></mspace><mi>v</mi><mi>i</mi><mi>v</mi><mi>o</mi></mrow></math></span> animal experiments. Results demonstrate that ZS-A2A achieves high performance compared to existing zero-shot methods.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"43 ","pages":"Article 100723"},"PeriodicalIF":7.1,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865033","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}

{"title":"Efficient implementations of a Born Series for computing photoacoustic field from a collection of erythrocytes","authors":"Ujjal Mandal ,&nbsp;Navroop Singh ,&nbsp;Kartikay Singh ,&nbsp;Vinit Nana Hagone ,&nbsp;Jagpreet Singh ,&nbsp;Anshu S. Anand ,&nbsp;Ben T. Cox ,&nbsp;Ratan K. Saha","doi":"10.1016/j.pacs.2025.100724","DOIUrl":"10.1016/j.pacs.2025.100724","url":null,"abstract":"<div><div>Numerical implementation of the Born series procedure is a computationally expensive task. Various computational strategies have been adopted and tested in this work for fast execution of the convergent Born series (CBS) algorithm for solving inhomogeneous Helmholtz equation in the context of biomedical photoacoustics (PAs). The PA field estimated by the CBS method for a solid circular disk approximating a red blood cell exhibits excellent agreement with the analytical result. It is observed that PA pressure map for a collection of red blood cells (mimicking blood) retains the signature of multiple scattering of acoustic waves by the acoustically inhomogeneous PA sources. The developed numerical tool realizing the CBS algorithm compatible with systems having multiple graphics processing units can be utilized further for accurate and fast estimation of the PA field for large tissue media.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"43 ","pages":"Article 100724"},"PeriodicalIF":7.1,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143825590","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}

{"title":"Rapid synergistic thrombolysis of ischemic stroke guided by high-resolution and high-speed photoacoustic cerebrovascular imaging","authors":"Mengtao Han ,&nbsp;Zhiwei Xue ,&nbsp;Mengchen Yu ,&nbsp;Nanlin You ,&nbsp;Yaguang Ren ,&nbsp;Zhiqiang Xu ,&nbsp;Zhifeng Wu ,&nbsp;Yiming He ,&nbsp;Zonghai Sheng ,&nbsp;Chengbo Liu ,&nbsp;Donghai Wang ,&nbsp;Jingqin Chen","doi":"10.1016/j.pacs.2025.100722","DOIUrl":"10.1016/j.pacs.2025.100722","url":null,"abstract":"<div><div>Thrombosis is the major cause of ischemic stroke and poses a serious health burden globally. Current thrombolytic strategies, such as systematic administration of recombinant human tissue plasminogen activator (rt-PA), are challenged by limited thrombolysis efficiency due to low targeting ability and a short plasma half-life. Here, we report a rapid synergistic strategy that integrates sonothrombolysis and rt-PA mediated pharmacological thrombolysis to achieve accurate and efficient treatment of ischemic stroke. The strategy (PLPA@PFP) uses a platelet-biomimetic membrane as a carrier to deliver both perfluoropentane (PFP) and rt-PA, prolonging half-life and effectively accumulating at the thrombus within 0.5 hours. Upon exposure to focused ultrasound, PFP-based cavitation effects significantly enhance thrombus breakdown and rt-PA penetration, enabling synergistic sono/pharmacological thrombolysis both <em>in vitro</em> and <em>in vivo</em>. High-resolution photoacoustic (PA) imaging provides direct assessment of vascular reperfusion following therapeutic intervention in a murine model of ischemic stroke, offering important guidance for clinical treatment.</div></div>","PeriodicalId":56025,"journal":{"name":"Photoacoustics","volume":"43 ","pages":"Article 100722"},"PeriodicalIF":7.1,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143820612","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}