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Diffuse optical tomography of the brain: effects of inaccurate baseline optical parameters and refinements using learned post-processing 脑弥散光学断层成像:基线光学参数不准确的影响以及利用学习后处理技术进行的改进
IF 3.4 2区 医学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-06-25 DOI: 10.1364/boe.524245
Meghdoot Mozumder, Pauliina Hirvi, Ilkka Nissilä, Andreas Hauptmann, Jorge Ripoll, David E. Singh
Diffuse optical tomography (DOT) uses near-infrared light to image spatially varying optical parameters in biological tissues. In functional brain imaging, DOT uses a perturbation model to estimate the changes in optical parameters, corresponding to changes in measured data due to brain activity. The perturbation model typically uses approximate baseline optical parameters of the different brain compartments, since the actual baseline optical parameters are unknown. We simulated the effects of these approximate baseline optical parameters using parameter variations earlier reported in literature, and brain atlases from four adult subjects. We report the errors in estimated activation contrast, localization, and area when incorrect baseline values were used. Further, we developed a post-processing technique based on deep learning methods that can reduce the effects due to inaccurate baseline optical parameters. The method improved imaging of brain activation changes in the presence of such errors.
弥散光学断层成像(DOT)利用近红外线对生物组织中空间变化的光学参数进行成像。在脑功能成像中,DOT 使用扰动模型来估计光学参数的变化,这些变化与大脑活动导致的测量数据变化相对应。由于实际基线光学参数未知,扰动模型通常使用不同脑区的近似基线光学参数。我们利用文献中早先报道的参数变化和四个成年受试者的脑图谱模拟了这些近似基线光学参数的影响。我们报告了在使用不正确的基线值时,估计激活对比度、定位和面积的误差。此外,我们还开发了一种基于深度学习方法的后处理技术,可以减少因基线光学参数不准确而造成的影响。该方法改善了存在此类误差时大脑激活变化的成像。
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引用次数: 0
Adaptive optical third-harmonic generation microscopy for in vivo imaging of tissues 用于组织活体成像的自适应光学三次谐波发生显微镜
IF 3.4 2区 医学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-06-21 DOI: 10.1364/boe.527357
Cristina Rodríguez, Daisong Pan, Ryan G. Natan, Manuel A. Mohr, Max Miao, Xiaoke Chen, Trent R. Northen, John P. Vogel, Na Ji
Third-harmonic generation microscopy is a powerful label-free nonlinear imaging technique, providing essential information about structural characteristics of cells and tissues without requiring external labelling agents. In this work, we integrated a recently developed compact adaptive optics module into a third-harmonic generation microscope, to measure and correct for optical aberrations in complex tissues. Taking advantage of the high sensitivity of the third-harmonic generation process to material interfaces and thin membranes, along with the 1,300-nm excitation wavelength used here, our adaptive optical third-harmonic generation microscope enabled high-resolution in vivo imaging within highly scattering biological model systems. Examples include imaging of myelinated axons and vascular structures within the mouse spinal cord and deep cortical layers of the mouse brain, along with imaging of key anatomical features in the roots of the model plant Brachypodium distachyon. In all instances, aberration correction led to enhancements in image quality.
三次谐波发生显微镜是一种功能强大的无标记非线性成像技术,可提供有关细胞和组织结构特征的重要信息,而无需外部标记物。在这项工作中,我们将最近开发的紧凑型自适应光学模块集成到了第三谐波发生显微镜中,以测量和校正复杂组织中的光学像差。利用三次谐波发生过程对材料界面和薄膜的高灵敏度,以及这里使用的 1300 纳米激发波长,我们的自适应光学三次谐波发生显微镜实现了在高散射生物模型系统中的高分辨率活体成像。例如,对小鼠脊髓和大脑皮质深层内的髓鞘轴突和血管结构进行成像,以及对模式植物 Brachypodium distachyon 的根部关键解剖特征进行成像。在所有情况下,像差校正都能提高图像质量。
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引用次数: 0
Probabilistic volumetric speckle suppression in OCT using deep learning 利用深度学习对 OCT 中的体积斑点进行概率抑制
IF 3.4 2区 医学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-06-19 DOI: 10.1364/boe.523716
Bhaskara Rao Chintada, Sebastián Ruiz-Lopera, René Restrepo, Brett E. Bouma, Martin Villiger, Néstor Uribe-Patarroyo
We present a deep learning framework for volumetric speckle reduction in optical coherence tomography (OCT) based on a conditional generative adversarial network (cGAN) that leverages the volumetric nature of OCT data. In order to utilize the volumetric nature of OCT data, our network takes partial OCT volumes as input, resulting in artifact-free despeckled volumes that exhibit excellent speckle reduction and resolution preservation in all three dimensions. Furthermore, we address the ongoing challenge of generating ground truth data for supervised speckle suppression deep learning frameworks by using volumetric non-local means despeckling–TNode– to generate training data. We show that, while TNode processing is computationally demanding, it serves as a convenient, accessible gold-standard source for training data; our cGAN replicates efficient suppression of speckle while preserving tissue structures with dimensions approaching the system resolution of non-local means despeckling while being two orders of magnitude faster than TNode. We demonstrate fast, effective, and high-quality despeckling of the proposed network in different tissue types that are not part of the training. This was achieved with training data composed of just three OCT volumes and demonstrated in three different OCT systems. The open-source nature of our work facilitates re-training and deployment in any OCT system with an all-software implementation, working around the challenge of generating high-quality, speckle-free training data.
我们基于条件生成对抗网络(cGAN),利用光学相干断层扫描(OCT)数据的体积特性,提出了一种用于减少光学相干断层扫描(OCT)中体积斑点的深度学习框架。为了利用光学相干断层扫描数据的体积特性,我们的网络将部分光学相干断层扫描体积作为输入,从而产生无伪影去斑体积,在所有三个维度上都表现出出色的斑点减少和分辨率保持能力。此外,我们通过使用体积非局部手段去斑--TNode 来生成训练数据,从而解决了为有监督的斑点抑制深度学习框架生成基本真实数据这一持续存在的挑战。我们的研究表明,虽然 TNode 处理对计算要求很高,但它是一种方便、可访问的黄金标准训练数据源;我们的 cGAN 在保留组织结构的同时复制了有效的斑点抑制,其维度接近非局部手段去斑的系统分辨率,速度比 TNode 快两个数量级。我们展示了所提出的网络在不同组织类型中快速、有效、高质量地去斑,而这些组织类型并不是训练的一部分。这是在由三个 OCT 体积组成的训练数据中实现的,并在三个不同的 OCT 系统中进行了演示。我们的工作具有开源性质,可以通过全软件实现在任何 OCT 系统中进行再训练和部署,解决了生成高质量无斑点训练数据的难题。
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引用次数: 0
Coronary artery calcification and cardiovascular outcome as assessed by intravascular OCT and artificial intelligence 通过血管内 OCT 和人工智能评估冠状动脉钙化和心血管预后
IF 3.4 2区 医学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-06-17 DOI: 10.1364/boe.524946
Jinwei Tian, Chao Li, Zhifeng Qin, Yanwen Zhang, Qinglu Xu, Yuqi Zheng, Xiangyu Meng, Peng Zhao, Kaiwen Li, Suhong Zhao, Shan Zhong, Xinyu Hou, Xiang Peng, Yuxin Yang, Yu Liu, Songzhi Wu, Yidan Wang, Xiangwen Xi, Yanan Tian, Wenbo Qu, Na Sun, Fan Wang, Yan Wang, Jie Xiong, Xiaofang Ban, Taishi Yonetsu, Rocco Vergallo, Bo Zhang, Bo Yu, Zhao Wang
Coronary artery calcification (CAC) is a marker of atherosclerosis and is thought to be associated with worse clinical outcomes. However, evidence from large-scale high-resolution imaging data is lacking. We proposed a novel deep learning method that can automatically identify and quantify CAC in massive intravascular OCT data trained using efficiently generated sparse labels. 1,106,291 OCT images from 1,048 patients were collected and utilized to train and evaluate the method. The Dice similarity coefficient for CAC segmentation and the accuracy for CAC classification are 0.693 and 0.932, respectively, close to human-level performance. Applying the method to 1259 ST-segment elevated myocardial infarction patients imaged with OCT, we found that patients with a greater extent and more severe calcification in the culprit vessels were significantly more likely to have major adverse cardiovascular and cerebrovascular events (MACCE) (p < 0.05), while the CAC in non-culprit vessels did not differ significantly between MACCE and non-MACCE groups.
冠状动脉钙化(CAC)是动脉粥样硬化的标志物,被认为与较差的临床预后有关。然而,目前还缺乏来自大规模高分辨率成像数据的证据。我们提出了一种新颖的深度学习方法,它能在使用高效生成的稀疏标签训练的海量血管内 OCT 数据中自动识别和量化 CAC。我们收集了来自 1,048 名患者的 1,106,291 张 OCT 图像,并利用这些图像对该方法进行了训练和评估。CAC 分割的 Dice 相似系数和 CAC 分类的准确率分别为 0.693 和 0.932,接近人类水平。将该方法应用于 1259 例 ST 段抬高的心肌梗死患者的 OCT 图像,我们发现,罪魁祸首血管钙化范围更大、更严重的患者发生重大不良心脑血管事件(MACCE)的可能性明显更高(p <0.05),而非罪魁祸首血管的 CAC 在 MACCE 组和非 MACCE 组之间没有显著差异。
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引用次数: 0
Long-term imaging of three-dimensional hyphal development using the ePetri dish. 使用 ePetri 培养皿对三维菌丝发育进行长期成像。
IF 2.9 2区 医学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-06-17 eCollection Date: 2024-07-01 DOI: 10.1364/BOE.530483
Oumeng Zhang, Nic Dahlquist, Zachary Leete, Michael Xu, Dean Schneider, Changhuei Yang

Imaging three-dimensional microbial development and behavior over extended periods is crucial for advancing microbiological studies. Here, we introduce an upgraded ePetri dish system specifically designed for extended microbial culturing and 3D imaging, addressing the limitations of existing methods. Our approach includes a sealed growth chamber to enable long-term culturing, and a multi-step reconstruction algorithm that integrates 3D deconvolution, image filtering, ridge, and skeleton detection for detailed visualization of the hyphal network. The system effectively monitored the development of Aspergillus brasiliensis hyphae over a seven-day period, demonstrating the growth medium's stability within the chamber. The system's 3D imaging capability was validated in a volume of 5.5 mm × 4 mm × 0.5 mm, revealing a radial growth pattern of fungal hyphae. Additionally, we show that the system can identify potential filter failures that are undetectable with 2D imaging. With these capabilities, the upgraded ePetri dish represents a significant advancement in long-term 3D microbial imaging, promising new insights into microbial development and behavior across various microbiological research areas.

长时间三维微生物发育和行为成像对于推进微生物学研究至关重要。在此,我们介绍一种升级版 ePetri 培养皿系统,该系统专为长时间微生物培养和三维成像而设计,解决了现有方法的局限性。我们的方法包括一个可实现长期培养的密封生长室,以及一个多步骤重建算法,该算法集成了三维解卷积、图像过滤、脊和骨架检测,可实现对菌丝网络的详细可视化。该系统有效监测了巴西曲霉菌丝在七天时间内的生长情况,证明了生长培养基在培养室内的稳定性。该系统的三维成像能力在 5.5 毫米 × 4 毫米 × 0.5 毫米的体积内得到了验证,显示了真菌菌丝的径向生长模式。此外,我们还展示了该系统可以识别二维成像无法检测到的潜在过滤器故障。凭借这些功能,升级版 ePetri 培养皿代表了长期三维微生物成像技术的重大进步,有望为各种微生物研究领域的微生物发展和行为提供新的见解。
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引用次数: 0
Tunable dynamical tissue phantom for laser speckle imaging 用于激光斑点成像的可调动态组织模型
IF 3.4 2区 医学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-06-17 DOI: 10.1364/boe.528286
Soumyajit Sarkar, Murali K, Hari M. Varma
We introduce a novel method to design and implement a tunable dynamical tissue phantom for laser speckle-based in-vivo blood flow imaging. This approach relies on stochastic differential equations (SDE) to control a piezoelectric actuator which, upon illuminated with a laser source, generates speckles of pre-defined probability density function and auto-correlation. The validation experiments show that the phantom can generate dynamic speckles that closely replicate both surfaces as well as deep tissue blood flow for a reasonably wide range and accuracy.
我们介绍了一种新方法,用于设计和实现基于激光斑点的体内血流成像的可调动态组织模型。这种方法依靠随机微分方程(SDE)来控制压电致动器,当激光光源照射时,压电致动器会产生预定义概率密度函数和自相关的斑点。验证实验表明,该模型可产生动态斑点,在相当宽的范围内精确复制表面和深层组织的血流。
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引用次数: 0
Unsupervised denoising of photoacoustic images based on the Noise2Noise network 基于 Noise2Noise 网络的光声图像无监督去噪技术
IF 3.4 2区 医学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-06-17 DOI: 10.1364/boe.529253
Yanda Cheng, Wenhan Zheng, Robert Bing, Huijuan Zhang, Chuqin Huang, Peizhou Huang, Leslie Ying, Jun Xia
In this study, we implemented an unsupervised deep learning method, the Noise2Noise network, for the improvement of linear-array-based photoacoustic (PA) imaging. Unlike supervised learning, which requires a noise-free ground truth, the Noise2Noise network can learn noise patterns from a pair of noisy images. This is particularly important for in vivo PA imaging, where the ground truth is not available. In this study, we developed a method to generate noise pairs from a single set of PA images and verified our approach through simulation and experimental studies. Our results reveal that the method can effectively remove noise, improve signal-to-noise ratio, and enhance vascular structures at deeper depths. The denoised images show clear and detailed vascular structure at different depths, providing valuable insights for preclinical research and potential clinical applications.
在这项研究中,我们采用了一种无监督深度学习方法--Noise2Noise 网络,用于改进基于线性阵列的光声(PA)成像。与需要无噪声地面实况的监督学习不同,Noise2Noise 网络可以从一对噪声图像中学习噪声模式。这对于没有地面实况的活体 PA 成像尤为重要。在本研究中,我们开发了一种从单组 PA 图像生成噪声对的方法,并通过模拟和实验研究验证了我们的方法。结果表明,该方法能有效去除噪声,提高信噪比,并增强深部血管结构。去噪后的图像显示出不同深度的清晰而详细的血管结构,为临床前研究和潜在的临床应用提供了有价值的见解。
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引用次数: 0
Optofluidic photonic crystal fiber platform for sensitive and reliable fluorescence based biosensing. 用于灵敏可靠的荧光生物传感的光流体光子晶体光纤平台。
IF 2.9 2区 医学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-06-13 eCollection Date: 2024-07-01 DOI: 10.1364/BOE.527248
Baptiste Moeglen-Paget, Jayakumar Perumal, Georges Humbert, Malini Olivo, U S Dinish

Biosensing plays a pivotal role in various scientific domains, offering significant contributions to medical diagnostics, environmental monitoring, and biotechnology. Fluorescence biosensing relies on the fluorescence emission from labelled biomolecules to enable sensitive and selective identification and quantification of specific biological targets in various samples. Photonic crystal fibers (PCFs) have led to the development of optofluidic fibers enabling efficient light-liquid interaction within small liquid volume. Herein, we present the development of a user-friendly optofluidic-fiber platform with simple hardware requirements for sensitive and reliable fluorescence biosensing with high measurement repeatability. We demonstrate a sensitivity improvement of the fluorescence emission up to 17 times compared to standard cuvette measurement, with a limit of detection of Cy5 fluorophore as low as 100 pM. The improvement in measurement repeatability is exploited for detecting haptoglobin protein, a relevant biomarker to diagnose several diseases, by using commercially available Cy5 labelled antibodies. The study aims to showcase an optofluidic platform leveraging the benefits provided by optofluidic fibers, which encompass easy light injection, robustness, and high sensitivity.

生物传感在各个科学领域发挥着举足轻重的作用,为医疗诊断、环境监测和生物技术做出了重大贡献。荧光生物传感依靠标记生物分子的荧光发射,实现对各种样品中特定生物目标的灵敏、选择性识别和定量。光子晶体纤维(PCFs)的开发推动了光流体纤维的发展,使其能够在小体积液体中实现高效的光-液相互作用。在本文中,我们介绍了一种用户友好型光导流体光纤平台的开发情况,该平台硬件要求简单,可用于灵敏可靠的荧光生物传感,测量重复性高。我们展示了与标准比色皿测量相比,荧光发射的灵敏度提高了 17 倍,Cy5 荧光团的检测限低至 100 pM。测量可重复性的提高被用于使用市售的 Cy5 标记抗体检测血红蛋白,血红蛋白是诊断多种疾病的相关生物标志物。这项研究旨在展示一种光流体平台,它充分利用了光流体纤维的优点,包括光注入方便、坚固耐用和灵敏度高。
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引用次数: 0
Holistic vibrational spectromics assessment of human cartilage for osteoarthritis diagnosis. 用于骨关节炎诊断的人体软骨整体振动光谱评估。
IF 2.9 2区 医学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-06-13 eCollection Date: 2024-07-01 DOI: 10.1364/BOE.520171
Hiroki Cook, Anna Crisford, Konstantinos Bourdakos, Douglas Dunlop, Richard O C Oreffo, Sumeet Mahajan

Osteoarthritis (OA) is the most common degenerative joint disease, presented as wearing down of articular cartilage and resulting in pain and limited mobility for 1 in 10 adults in the UK [Osteoarthr. Cartil.28(6), 792 (2020)10.1016/j.joca.2020.03.004]. There is an unmet need for patient friendly paradigms for clinical assessment that do not use ionizing radiation (CT), exogenous contrast enhancing dyes (MRI), and biopsy. Hence, techniques that use non-destructive, near- and shortwave infrared light (NIR, SWIR) may be ideal for providing label-free, deep tissue interrogation. This study demonstrates multimodal "spectromics", low-level abstraction data fusion of non-destructive NIR Raman scattering spectroscopy and NIR-SWIR absorption spectroscopy, providing an enhanced, interpretable "fingerprint" for diagnosis of OA in human cartilage. This is proposed as method level innovation applicable to both arthro- or endoscopic (minimally invasive) or potential exoscopic (non-invasive) optical approaches. Samples were excised from femoral heads post hip arthroplasty from OA patients (n = 13) and age-matched control (osteoporosis) patients (n = 14). Under multivariate statistical analysis and supervised machine learning, tissue was classified to high precision: 100% segregation of tissue classes (using 10 principal components), and a classification accuracy of 95% (control) and 80% (OA), using the combined vibrational data. There was a marked performance improvement (5 to 6-fold for multivariate analysis) using the spectromics fingerprint compared to results obtained from solely Raman or NIR-SWIR data. Furthermore, clinically relevant tissue components were identified through discriminatory spectral features - spectromics biomarkers - allowing interpretable feedback from the enhanced fingerprint. In summary, spectromics provides comprehensive information for early OA detection and disease stratification, imperative for effective intervention in treating the degenerative onset disease for an aging demographic. This novel and elegant approach for data fusion is compatible with various NIR-SWIR optical devices that will allow deep non-destructive penetration.

骨关节炎(OA)是最常见的退行性关节疾病,表现为关节软骨磨损,英国每 10 个成年人中就有 1 人因此而感到疼痛和活动受限 [Osteoarthr.Cartil.28(6),792(2020)10.1016/j.joca.2020.03.004]。对于不使用电离辐射(CT)、外源性对比增强染料(MRI)和活组织检查的患者友好型临床评估范例的需求尚未得到满足。因此,使用非破坏性近红外和短波红外光(NIR、SWIR)的技术可能是提供无标记深层组织检查的理想选择。本研究展示了多模态 "光谱学"、非破坏性近红外拉曼散射光谱和近红外-短波红外吸收光谱的低级抽象数据融合,为诊断人体软骨的 OA 提供了增强的、可解释的 "指纹"。这是方法层面的创新,适用于关节镜或内窥镜(微创)或潜在的外窥镜(无创)光学方法。样本取自髋关节置换术后的股骨头,分别来自 OA 患者(13 人)和年龄匹配的对照组(骨质疏松症)患者(14 人)。通过多变量统计分析和监督机器学习,对组织进行了高精度分类:使用综合振动数据,组织类别分离率达 100%(使用 10 个主成分),分类准确率达 95%(对照组)和 80%(OA)。与仅使用拉曼或近红外-西红外数据得出的结果相比,使用光谱指纹的性能有了显著提高(多变量分析提高了 5 到 6 倍)。此外,通过鉴别性光谱特征(光谱生物标记)确定了与临床相关的组织成分,从而可以从增强的指纹中获得可解释的反馈。总之,光谱学为早期 OA 检测和疾病分层提供了全面的信息,对于有效干预老龄人口的退行性疾病治疗至关重要。这种新颖、优雅的数据融合方法与各种近红外-西红外光学设备兼容,可实现非破坏性的深度渗透。
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引用次数: 0
In vivo endoscopic optical coherence elastography based on a miniature probe. 基于微型探针的活体内窥镜光学相干弹性成像。
IF 2.9 2区 医学 Q2 BIOCHEMICAL RESEARCH METHODS Pub Date : 2024-06-12 eCollection Date: 2024-07-01 DOI: 10.1364/BOE.521154
Haoxing Xu, Qingrong Xia, Chengyou Shu, Jiale Lan, Xiatian Wang, Wen Gao, Shengmiao Lv, Riqiang Lin, Zhihua Xie, Xiaohui Xiong, Fei Li, Jinke Zhang, Xiaojing Gong

Optical coherence elastography (OCE) is a functional extension of optical coherence tomography (OCT). It offers high-resolution elasticity assessment with nanoscale tissue displacement sensitivity and high quantification accuracy, promising to enhance diagnostic precision. However, in vivo endoscopic OCE imaging has not been demonstrated yet, which needs to overcome key challenges related to probe miniaturization, high excitation efficiency and speed. This study presents a novel endoscopic OCE system, achieving the first endoscopic OCE imaging in vivo. The system features the smallest integrated OCE probe with an outer diameter of only 0.9 mm (with a 1.2-mm protective tube during imaging). Utilizing a single 38-MHz high-frequency ultrasound transducer, the system induced rapid deformation in tissues with enhanced excitation efficiency. In phantom studies, the OCE quantification results match well with compression testing results, showing the system's high accuracy. The in vivo imaging of the rat vagina demonstrated the system's capability to detect changes in tissue elasticity continually and distinguish between normal tissue, hematomas, and tissue with increased collagen fibers precisely. This research narrows the gap for the clinical implementation of the endoscopic OCE system, offering the potential for the early diagnosis of intraluminal diseases.

光学相干弹性成像(OCE)是光学相干断层扫描(OCT)的功能扩展。它提供高分辨率弹性评估,具有纳米级组织位移灵敏度和高量化准确性,有望提高诊断精度。然而,活体内窥镜 OCE 成像尚未得到证实,需要克服探头微型化、高激发效率和速度等关键挑战。本研究介绍了一种新型内窥镜 OCE 系统,首次实现了内窥镜 OCE 在体内成像。该系统具有最小的集成 OCE 探头,外径仅为 0.9 毫米(成像时带有 1.2 毫米的保护管)。该系统利用单个 38-MHz 高频超声换能器,在提高激发效率的同时诱导组织快速变形。在模型研究中,OCE 量化结果与压缩测试结果非常吻合,显示了该系统的高准确性。大鼠阴道的活体成像表明,该系统能够持续检测组织弹性的变化,并精确区分正常组织、血肿和胶原纤维增多的组织。这项研究缩小了内窥镜 OCE 系统在临床应用方面的差距,为早期诊断腔内疾病提供了可能。
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引用次数: 0
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Biomedical optics express
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