Photoacoustics最新文献_第6页

Conductance-photoacoustic spectroscopy for fast and concurrent sensing of hydrogen and hydrocarbons 用于氢和碳氢化合物的快速和同步传感的电导光声光谱

IF 6.8 1区医学 Q1 ENGINEERING, BIOMEDICAL

Photoacoustics

Pub Date : 2025-07-23 DOI: 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.

准确、快速地检测氢和碳氢化合物对于现代能源、工业和环境系统的安全和效率至关重要。然而，这些物种的选择性和同时定量仍然是一个重大的技术挑战。在这里，我们介绍了电导光声光谱（ConPAS），这是一种在单个设备中结合电导共振调制和石英增强光声光谱的集成传感方法。通过将石英音叉（QTF）与催化铂微丝桥接，ConPAS可以从统一的电信号中同时提取氢和碳氢化合物浓度：氢通过频率分析量化，而碳氢化合物含量通过幅度分析同时确定。实验表明，氢气的最小检出限为0.69 %，丙烷为40.26 ppm，甲烷为133.7 ppm，响应时间为毫秒级，线性良好(R²>；0.99)。模块化结构允许通过材料替代灵活适应其他分析物，为同时，多组分气体传感提供可扩展和通用的解决方案。这项工作建立了ConPAS作为一个强大的、校准兼容的平台，用于富氢环境中的综合气体分析，对安全监测、过程控制和先进的能源应用具有广泛的意义。

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引用次数: 0

High-frequency (> 65 MHz) broadband transparent transducer with ultrathin gold electrode for dual-mode photoacoustic and laser-induced ultrasound microscopy 高频（bbb65 MHz）宽带透明换能器，超薄金电极，用于双模光声和激光诱导超声显微镜

IF 7.1 1区医学 Q1 ENGINEERING, BIOMEDICAL

Photoacoustics

Pub Date : 2025-07-20 DOI: 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.

对于高性能组合光声（PA）和超声（US）显微镜，精确的同轴对准的美国和激光束是必不可少的。这可以使用宽带透明超声换能器（tut）来实现。然而，由于光和超声的顺序传输以及双模探头的机械运动，目前的双模成像系统在同时采集PA和US数据时遇到了重大挑战，导致采集时间更长，并且可能出现配准不准确的情况。为了克服这些限制，我们提出了一种最近开发的高频宽带TUT，其超薄(<；10 nm)金电极，中心频率为65.6 MHz， -6 dB带宽为71.6 %。超薄金电极有利于激光诱导超声（LUS），能够同时获取PA和US图像。体内实验表明，LUS成像可以有效地取代传统的US成像，提供高效的PA/US双模成像和最小的配准误差。

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引用次数: 0

Reconstructing multiple initial pressure and speed of sound distributions simultaneously in photoacoustic tomography 光声层析成像中多重初始声压和声速分布的同时重建

IF 7.1 1区医学 Q1 ENGINEERING, BIOMEDICAL

Photoacoustics

Pub Date : 2025-07-11 DOI: 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.

光声层析成像中的图像重建依赖于对目标声速的准确了解。然而，由于声速分布的一般不知道，这可能会导致重建的初始压力分布出现伪影。因此，同时重建声速和初始压力对于光声断层成像的精确成像是有价值的。此外，声速分布可以提供关于成像目标的额外有价值的信息。本文研究了光声层析成像中声速和初始压力的同时重建。这个逆问题是高度不适定的。为了克服这一点，我们研究了一种方法，该方法通过利用由同一成像目标内不同初始压力分布产生的多个光声数据集来减轻不适。然后，将这些初始压力分布与声速分布同步重构。解决这个最小化问题的方法是使用一个基于梯度的迭代方法，配备了绑定约束和多网格方法。通过数值模拟对该方法进行了评价。研究了生成多个初始压力分布的不同方法及其对图像重建问题求解的影响。结果表明，光声数据可以同时重建声压和声速。此外，利用多个初始压力分布改进了重建，从而可以更好地区分声不均匀的初始压力和速度的位置，并减少图像伪影。

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引用次数: 0

PA OmniNet: A retraining-free, generalizable deep learning framework for robust photoacoustic image reconstruction PA OmniNet：一种用于鲁棒光声图像重建的无需再训练、可推广的深度学习框架

IF 7.1 1区医学 Q1 ENGINEERING, BIOMEDICAL

Photoacoustics

Pub Date : 2025-07-08 DOI: 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.

对于光声成像的临床翻译，开发具有成本效益的系统是必要的。一种方法是使用较少的传感器元件和采集通道，并结合稀疏采样。然而，这种方法引入了降低图像质量的重建伪影。虽然像U-net这样的深度学习模型在从有限的数据中重建图像方面显示出了希望，但它们通常需要对每个新的系统配置进行重新训练，这一过程需要更多的数据和更多的计算资源。在这项工作中，我们引入了PA OmniNet，这是一种改进的U-net模型，旨在跨不同的系统配置进行泛化，而无需再训练。与重新训练不同，PA OmniNet只使用一小组示例图像（在4到32之间）来适应新系统，称为上下文集。这一背景设置了条件，使模型能够有效地从各种稀疏采样光声成像应用的新输入图像中去除伪影。我们使用多种数据集对PA OmniNet与标准U-net进行了评估，包括来自小鼠和人类受试者的体内数据、合成数据和不同波长捕获的图像。PA OmniNet在泛化任务中始终优于传统的U-net，结构相似性指数平均提高8.3%，均方根误差降低11.6%，峰值信噪比提高1.55 dB。在我们66%的测试用例中，广义PA OmniNet甚至优于专门在新数据集上训练的U-net模型。代码可从https://github.com/olivierstam4/PA_OmniNet获得。

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引用次数: 0

Wavelet-enhanced residual optimal transport for Mamba-based image restoration in photoacoustic tomography 小波增强残差最优传输在光声断层成像中基于mamba的图像恢复

IF 7.1 1区医学 Q1 ENGINEERING, BIOMEDICAL

Photoacoustics

Pub Date : 2025-07-07 DOI: 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.

光声断层扫描（PAT）结合了高对比度的光学成像和通过超声检测的深层组织穿透。然而，硬件限制通常会导致图像采集过程中的稀疏采样，从而导致许多当前深度学习方法无法有效去除的破坏性条纹伪影。在本文中，我们引入了残差条件最优传输曼巴（RCMamba），这是一个新的框架，通过将基于小波的分析与专门为稀疏PAT图像恢复设计的混合多尺度状态空间模型主干相结合来增强残差最优传输。我们的方法有两个主要贡献。首先，我们提出了一种小波残差增强传输方案，该方案利用多分辨率分析和一种新的小波相干性惩罚来准确捕获稀疏采集特征的复杂的、尺度相关的稀疏模式。其次，我们开发了一种混合多尺度曼巴架构，该架构独特地结合了基于窗口的和全局状态空间扫描，以保留精细的解剖细节和远程结构信息。RCMamba在推进稀疏采样PAT系统用于诊断成像和介入程序的临床潜力方面具有很大的前景。

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引用次数: 0

Artifacts in photoacoustic imaging: Origins and mitigations 光声成像中的伪影：起源和缓解

IF 7.1 1区医学 Q1 ENGINEERING, BIOMEDICAL

Photoacoustics

Pub Date : 2025-07-05 DOI: 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.

光声成像（PAI）正迅速从实验室转移到临床，这增加了了解可能对患者护理产生不利影响的混杂因素的需求。在过去的五年中，具有里程碑意义的研究显示了PAI的临床应用，导致监管部门批准了几种设备。在本文中，我们描述了PAI中工件的各种原因，提供了概要概述和实际示例，模拟和实验。这项工作有两个目的：(1)教育临床用户识别伪影，了解其原因，并评估其影响，以及(2)为那些致力于改进它们的人提供当前系统局限性的参考。我们解释了PAI系统的两个方面是如何导致工件的：它们无法测量完整的数据集，以及在重建期间嵌入的假设。我们描述了PAI的物理基础，并提出了工件的分类。论文最后讨论了可能的先进缓解策略。

引用次数: 0

Multi-scenario photoacoustic endoscopy for in vivo functional imaging 用于体内功能成像的多场景光声内窥镜

IF 7.1 1区医学 Q1 ENGINEERING, BIOMEDICAL

Photoacoustics

Pub Date : 2025-07-05 DOI: 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.

光学内窥镜已广泛应用于内科病变器官的临床筛查和诊断。光声内窥镜是发展最快的光学内窥镜技术之一，具有丰富的光学对比度和较高的空间声分辨能力，具有相当的穿透深度。然而，在任意形状的生物束和腔中实现高速、大视场光声内窥镜仍然是一个挑战。在这里，我们开发了一种小型化的多视角光声内窥镜（Multi-PAE），它在一个胶囊大小的探针内集成了一个微光学扫描仪和一个折叠光路。探头具有不同方向的多个可互换成像接口，以成像不同的束和腔。我们提出了一种复合双螺旋共振扫描（CDSRS）机制，使光学扫描仪能够在大视场范围内进行稳定均匀的共振扫描。我们展示了Multi-PAE在大鼠直肠、家兔颈部和整个人类口腔中的多场景功能成像适用性。

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引用次数: 0

Towards bridging the synthetic-to-real gap in quantitative photoacoustic tomography via unsupervised domain adaptation 通过无监督域自适应弥合定量光声断层成像中合成与真实的差距

IF 7.1 1区医学 Q1 ENGINEERING, BIOMEDICAL

Photoacoustics

Pub Date : 2025-07-04 DOI: 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.

获取吸收系数注释的困难阻碍了深度学习在定量光声层析成像中的实际应用。虽然在合成数据上进行训练很容易实现，但合成域与真实域的差距对模型泛化提出了重大挑战。为了解决这个问题，我们提出了一个解码器增强的无监督领域自适应（DDA）框架，以实现从合成数据到未标记目标领域的知识转移。实验结果表明，DDA算法显著提高了对目标图像的估计性能，并在定量评价和视觉比较方面优于竞争方法。此外，我们还研究了跨领域标签分布相似度对领域自适应的影响，并提出了一种有效的数据合成方法。为了减轻吸收特性不匹配的影响，我们提出微调归一化层的仿射参数，这显著提高了使用标记的多波长光声图像从少至两个目标样品的估计精度。

引用次数: 0

Dual-wavelength photoacoustic imaging of sentinel lymph nodes in patients with melanoma and breast cancer 黑色素瘤和乳腺癌前哨淋巴结的双波长光声成像

IF 7.1 1区医学 Q1 ENGINEERING, BIOMEDICAL

Photoacoustics

Pub Date : 2025-06-28 DOI: 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.

前哨淋巴结（SLN）活检是黑色素瘤和乳腺癌患者准确疾病分期和治疗计划的重要步骤。传统的术前成像主要使用Tc-99m淋巴显像，但存在一些局限性，包括辐射暴露、后勤挑战和手术工作流程的潜在延迟。光声成像（PAI）已成为一种有前途的替代方案，利用吲哚菁绿（ICG）提供的光学对比度。鹿特丹大学医学中心Erasmus MC进行了一项可行性研究，以评估双波长PAI用于SLN定位的潜力。PAI被用于健康志愿者的光谱测量，以支持最佳激发方案的发展。随后，在患者期，使用PAI和ICG进行SLN制图，并将结果与使用Tc-99m的标准护理方法进行比较。选择800 nm和860 nm的激发波长进行比值成像，有效地显示ICG，同时抑制血红蛋白和黑色素的杂波。在11个评估的前哨淋巴结中，有7个使用PAI成功识别。PAI的最大SLN检测深度为22 mm。本研究说明了icg增强双波长PAI在黑色素瘤和乳腺癌患者的术前SLN定位中作为淋巴显像的替代方法的可行性。假阴性检测的分析提示PAI的改进和最佳患者选择。与多波长光谱成像相比，所提出的比率PAI方法可以实现更快的成像速度，并有利于向更便宜的光源过渡。

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引用次数: 0

Advancing non-invasive melanoma diagnostics with deep learning and multispectral photoacoustic imaging 利用深度学习和多光谱光声成像技术推进非侵入性黑色素瘤诊断

IF 7.1 1区医学 Q1 ENGINEERING, BIOMEDICAL

Photoacoustics

Pub Date : 2025-06-19 DOI: 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.

黑色素瘤的发病率正在上升，需要更有效的诊断程序来满足日益增长的需求。切除活检和组织病理学仍然是标准，这通常需要多个手术切口，由于不准确的视觉评估黑素瘤与健康组织的边界。这一挑战部分源于无法在没有视觉检查化学染色的组织病理学横截面的情况下可靠地描绘黑色素瘤。光谱成像是非侵入性地表征组织的分子组成，从而根据光谱特征区分黑色素瘤和健康组织。在这项工作中，我们描述了一个应用于人类黑色素瘤多光谱光声（PA）成像数据的计算框架，并演示了如何在没有人工输入的情况下自动确定肿瘤的边界。该框架结合了K-means聚类，用于无偏地选择训练数据，一维卷积神经网络应用于PA光谱，用于将像素分类为健康或患病，以及主动轮廓算法，最终在3D中描绘黑色素瘤。这项工作将影响临床实践，因为它可以为术前和围手术期提供指导，以确保以最小的手术切口完全切除肿瘤。

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引用次数: 0